phd in data science part time

Where To Earn A Ph.D. In Data Science Online In 2024

Updated: Apr 3, 2024, 2:15pm

Data science is among the most in-demand skill sets in the modern economy. Data science professionals help businesses make decisions by creating analytical models, combining elements of math, artificial intelligence, machine learning and statistics.

If you want to pursue a high-paying data science career or teach data science at the college level, you may want to earn a terminal degree in the field. Online Ph.D. in data science programs allow you to advance your career while balancing other responsibilities at work or home.

We found two online data science programs that met our ranking criteria. Read on to learn more about these schools and find answers to frequently asked questions about data science.

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Online Ph.D. in Data Science Option

Capitol technology university, national university.

Located just outside Washington, D.C., in South Laurel, Maryland, Capitol Technology University offers an online doctoral degree in business analytics and data science. The program includes a limited residency requirement: Students must complete a course in contemporary research in management on campus, during which they take a qualifying exam. The degree requires 54 to 66 credits, and students can graduate within three years.

All students must also complete a dissertation and an oral defense of their work. The program costs $950 per credit for both in-state and out-of-state learners. Retired and active duty military receive a tuition discount.

At a Glance

• School Type: Private
• Application Fee: $100
• Degree Credit Requirements: 54 to 66 credits
• Program Enrollment Options: Part-time
• Notable Major-Specific Courses: Management theory in a global economy; analytics and decision analysis
• Concentrations Available: N/A
• In-Person Requirements: Yes, for residency

Based in San Diego, California, National University (NU) offers a variety of online programs, including a Ph.D. in data science. NU’s program requires 60 credits and takes an estimated 40 months. NU aims for flexibility, delivering coursework asynchronously and offering a new start date each Monday. The curriculum comprises 20 courses covering data science principles and data preparation methods.

NU runs on the quarter system and charges $442 per quarter unit for graduate courses. The program does not include any in-person requirements.

• Application Fee: Free
• Degree Credit Requirements: 60 credits
• Notable Major-Specific Courses: Principles of data science, data preparation methods
• In-Person Requirements: No

How To Find the Right Online Ph.D. in Data Science for You

Consider your future goals.

A Ph.D. in data science makes sense if you want to become a college professor , conduct original research or compete for the highest-paying and most cognitively demanding business analytics and machine learning positions. If you plan to pursue other careers, you may not need a terminal degree in this field.

If you want to work in academia, make sure your chosen doctorate in data science includes a dissertation requirement. A dissertation allows you to perform original research and contribute to scholarship in your field before you graduate. In turn, you’ll get a sense of your chosen career and a head start on professional publication.

Understand Your Expenses and Financing Options

Per-credit tuition rates for the programs in our guide ranged from $442 to $950. A 60-credit degree from NU totals about $26,500, while the 66-credit option at Capitol Tech costs more than $62,000.

Private universities, including NU and Capitol Tech, tend to cost more than public schools. Graduate students at nonprofit private universities paid an average of $20,408 per year in 2022-23, according to the National Center for Education Statistics . Over the course of a typical three-year Ph.D. program, this translates to about $61,000. This roughly matches Capitol Tech’s tuition, while NU offers a more affordable program.

While a Ph.D. might help you land a lucrative role in the long run, the upfront investment is still significant. Make sure to fill out the FAFSA ® to access federal student aid. This application is the gateway to opportunities like scholarships, grants and loans. You can pursue similar opportunities through schools and nonprofit organizations.

As a doctoral student, you may be able to access graduate assistantships or stipends, but these are often reserved for on-campus students who teach undergraduates or assist professors with research.

Should You Enroll in a Ph.D. in Data Science Online?

Pursuing a Ph.D. in data science online suits a specific kind of learner. To decide if that’s you, ask yourself a few key questions:

• What’s my budget? In some cases, public universities allow students who exclusively enroll in online courses to pay in-state or otherwise discounted tuition rates. Even if you have to pay full price, distance learners generally save on costs associated with housing and transportation.
• What are my other commitments? Distance learning is often a good fit for parents and students who need to work full time while pursuing their degree. Learners with outside responsibilities might pursue a program with asynchronous course delivery, which eliminates scheduled class sessions.
• What’s my learning style? Distance learning requires a great deal of discipline, organization and time management. If you need external accountability or prefer the structure of a peer group or physical classroom, on-campus learning might offer a better fit.

Accreditation for Online Ph.D.s in Data Science

There are two important types of college accreditation to consider: institutional and programmatic.

Institutional accreditation is essential; it involves vetting schools to ensure the quality of their finances, academics, and faculty, among other areas. The Council for Higher Education Accreditation (CHEA) and U.S. Department of Education oversee the regional agencies that administer this process.

You should only enroll at institutionally accredited schools. Otherwise, you will be ineligible for federal financial aid. You can check a school’s accreditation status on its website or by visiting the directory on CHEA’s website .

Individual departments and degrees earn programmatic accreditation based on their curriculum, faculty and learner outcomes. However, this process has not been widely established for data science programs, so it shouldn’t make or break your enrollment decision. However, you can still keep an eye out for accreditation from the Data Science Council of America (DASCA).

Our Methodology

We ranked two accredited, nonprofit colleges offering online Ph.D.s in data science in the U.S. using 15 data points in the categories of student experience, credibility, student outcomes and affordability. We pulled data for these categories from reliable resources such as the Integrated Postsecondary Education Data System ; private, third-party data sources; and individual school and program websites.

Data is accurate as of February 2024. Note that because online doctorates are relatively uncommon, fewer schools meet our ranking standards at the doctoral level.

We scored schools based on the following metrics:

Student Experience:

• Student-to-faculty ratio
• Socioeconomic diversity
• Availability of online coursework
• Total number of graduate assistants
• Proportion of graduate students enrolled in at least some distance education

Credibility:

• Fully accredited
• Programmatic accreditation status
• Nonprofit status

Student Outcomes:

• Overall graduation rate
• Median earnings 10 years after graduation

Affordability:

• In-state graduate student tuition
• In-state graduate student fees
• Alternative tuition plans offered
• Median federal student loan debt
• Student loan default rate

We listed the two schools in the U.S. that met our ranking criteria.

Find our full list of methodologies here .

Frequently Asked Questions (FAQs) About Earning a Ph.D. in Data Science Online

Can i do a ph.d. in data science online.

Yes, you can. National University and Capitol Technology University both offer Ph.D. programs in data science that you can complete mostly or entirely online.

Is a Ph.D. worth it for data science?

It depends on your goals and circumstances. A Ph.D. in data science may be a good fit if you want to pursue a career in research or academia or compete for advanced, lucrative positions in business analytics, artificial intelligence or machine learning.

Is it okay to get a Ph.D. online?

Yes, as long as the program is accredited. Distance learning requires strong motivation and self-discipline, so it suits some students better than others.

Can you become a professor with an online Ph.D.?

Yes, you can. Online diplomas feature the same coursework and degree requirements as in-person degrees, and your degree won’t say “online”.

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PhD in Data Science – Your Guide to Choosing a Doctorate Degree Program

Created by aasif.faizal

Professional opportunities in data science are growing incredibly fast. That’s great news for students looking to pursue a career as a data scientist. But it also means that there are a lot more options out there to investigate and understand before developing the best educational path for you.

A PhD is the most advanced data science degree you can get, reflecting a depth of knowledge and technical expertise that will put you at the top of your field.

This means that PhD programs are the most time-intensive degree option out there, typically requiring that students complete dissertations involving rigorous research. This means that PhDs are not for everyone. Indeed, many who work in the world of big data hold master’s degrees rather than PhDs, which tend to involve the same coursework as PhD programs without a dissertation component. However, for the right candidate, a PhD program is the perfect choice to become a true expert on your area of focus.

If you’ve concluded that a data science PhD is the right path for you, this guide is intended to help you choose the best program to suit your needs. It will walk through some of the key considerations while picking graduate data science programs and some of the nuts and bolts (like course load and tuition costs) that are part of the data science PhD decision-making process.

Data Science PhD vs. Masters: Choosing the right option for you

If you’re considering pursuing a data science PhD, it’s worth knowing that such an advanced degree isn’t strictly necessary in order to get good work opportunities. Many who work in the field of big data only hold master’s degrees, which is the level of education expected to be a competitive candidate for data science positions.

So why pursue a data science PhD?

Simply put, a PhD in data science will leave you qualified to enter the big data industry at a high level from the outset.

You’ll be eligible for advanced positions within companies, holding greater responsibilities, keeping more direct communication with leadership, and having more influence on important data-driven decisions. You’re also likely to receive greater compensation to match your rank.

However, PhDs are not for everyone. Dissertations require a great deal of time and an interest in intensive research. If you are eager to jumpstart a career quickly, a master’s program will give you the preparation you need to hit the ground running. PhDs are appropriate for those who want to commit their time and effort to schooling as a long-term investment in their professional trajectory.

For more information on the difference between data science PhD’s and master’s programs, take a look at our guide here.

Topics include:

• Can I get an Online Ph.D in Data Science?
• Overview of Ph.d Coursework

Preparing for a Doctorate Program

Building a solid track record of professional experience, things to consider when choosing a school.

• What Does it Cost to Get a Ph.D in Data Science?
• School Listings

Data Science PhD Programs, Historically

Historically, data science PhD programs were one of the main avenues to get a good data-related position in academia or industry. But, PhD programs are heavily research oriented and require a somewhat long term investment of time, money, and energy to obtain. The issue that some data science PhD holders are reporting, especially in industry settings, is that that the state of the art is moving so quickly, and that the data science industry is evolving so rapidly, that an abundance of research oriented expertise is not always what’s heavily sought after.

Instead, many companies are looking for candidates who are up to date with the latest data science techniques and technologies, and are willing to pivot to match emerging trends and practices.

One recent development that is making the data science graduate school decisions more complex is the introduction of specialty master’s degrees, that focus on rigorous but compact, professional training. Both students and companies are realizing the value of an intensive, more industry-focused degree that can provide sufficient enough training to manage complex projects and that are more client oriented, opposed to research oriented.

However, not all prospective data science PhD students are looking for jobs in industry. There are some pretty amazing research opportunities opening up across a variety of academic fields that are making use of new data collection and analysis tools. Experts that understand how to leverage data systems including statistics and computer science to analyze trends and build models will be in high demand.

Can You Get a PhD in Data Science Online?

While it is not common to get a data science Ph.D. online, there are currently two options for those looking to take advantage of the flexibility of an online program.

Indiana University Bloomington and Northcentral University both offer online Ph.D. programs with either a minor or specialization in data science.

Given the trend for schools to continue increasing online offerings, expect to see additional schools adding this option in the near future.

Overview of PhD Coursework

A PhD requires a lot of academic work, which generally requires between four and five years (sometimes longer) to complete.

Here are some of the high level factors to consider and evaluate when comparing data science graduate programs.

How many credits are required for a PhD in data science?

On average, it takes 71 credits to graduate with a PhD in data science — far longer (almost double) than traditional master’s degree programs. In addition to coursework, most PhD students also have research and teaching responsibilities that can be simultaneously demanding and really great career preparation.

What’s the core curriculum like?

In a data science doctoral program, you’ll be expected to learn many skills and also how to apply them across domains and disciplines. Core curriculums will vary from program to program, but almost all will have a core foundation of statistics.

All PhD candidates will have to take a qualifying exam. This can vary from university to university, but to give you some insight, it is broken up into three phases at Yale. They have a practical exam, a theory exam and an oral exam. The goal is to make sure doctoral students are developing the appropriate level of expertise.

Dissertation

One of the final steps of a PhD program involves presenting original research findings in a formal document called a dissertation. These will provide background and context, as well as findings and analysis, and can contribute to the understanding and evolution of data science. A dissertation idea most often provides the framework for how a PhD candidate’s graduate school experience will unfold, so it’s important to be thoughtful and deliberate while considering research opportunities.

Since data science is such a rapidly evolving field and because choosing the right PhD program is such an important factor in developing a successful career path, there are some steps that prospective doctoral students can take in advance to find the best-fitting opportunity.

Join professional associations

Even before being fully credentials, joining professional associations and organizations such as the Data Science Association and the American Association of Big Data Professionals is a good way to get exposure to the field. Many professional societies are welcoming to new members and even encourage student participation with things like discounted membership fees and awards and contest categories for student researchers. One of the biggest advantages to joining is that these professional associations bring together other data scientists for conference events, research-sharing opportunities, networking and continuing education opportunities.

Leverage your social network

Be on the lookout to make professional connections with professors, peers, and members of industry. There are a number of LinkedIn groups dedicated to data science. A well-maintained professional network is always useful to have when looking for advice or letters of recommendation while applying to graduate school and then later while applying for jobs and other career-related opportunities.

Kaggle competitions

Kaggle competitions provide the opportunity to solve real-world data science problems and win prizes. A list of data science problems can be found at Kaggle.com . Winning one of these competitions is a good way to demonstrate professional interest and experience.

Internships

Internships are a great way to get real-world experience in data science while also getting to work for top names in the world of business. For example, IBM offers a data science internship which would also help to stand out when applying for PhD programs, as well as in seeking employment in the future.

Demonstrating professional experience is not only important when looking for jobs, but it can also help while applying for graduate school. There are a number of ways for prospective students to gain exposure to the field and explore different facets of data science careers.

Get certified

There are a number of data-related certificate programs that are open to people with a variety of academic and professional experience. DeZyre has an excellent guide to different certifications, some of which might help provide good background for graduate school applications.

Conferences

Conferences are a great place to meet people presenting new and exciting research in the data science field and bounce ideas off of newfound connections. Like professional societies and organizations, discounted student rates are available to encourage student participation. In addition, some conferences will waive fees if you are presenting a poster or research at the conference, which is an extra incentive to present.

It can be hard to quantify what makes a good-fit when it comes to data science graduate school programs. There are easy to evaluate factors, such as cost and location, and then there are harder to evaluate criteria such as networking opportunities, accessibility to professors, and the up-to-dateness of the program’s curriculum.

Nevertheless, there are some key relevant considerations when applying to almost any data science graduate program.

What most schools will require when applying:

• All undergraduate and graduate transcripts
• A statement of intent for the program (reason for applying and future plans)
• Letters of reference
• Application fee
• Online application
• A curriculum vitae (outlining all of your academic and professional accomplishments)

What Does it Cost to Get a PhD in Data Science?

The great news is that many PhD data science programs are supported by fellowships and stipends. Some are completely funded, meaning the school will pay tuition and basic living expenses. Here are several examples of fully funded programs:

• University of Southern California
• University of Nevada, Reno
• Kennesaw State University
• Worcester Polytechnic Institute
• University of Maryland

For all other programs, the average range of tuition, depending on the school can range anywhere from $1,300 per credit hour to $2,000 amount per credit hour. Remember, typical PhD programs in data science are between 60 and 75 credit hours, meaning you could spend up to $150,000 over several years.

That’s why the financial aspects are so important to evaluate when assessing PhD programs, because some schools offer full stipends so that you are able to attend without having to find supplemental scholarships or tuition assistance.

Can I become a professor of data science with a PhD.? Yes! If you are interested in teaching at the college or graduate level, a PhD is the degree needed to establish the full expertise expected to be a professor. Some data scientists who hold PhDs start by entering the field of big data and pivot over to teaching after gaining a significant amount of work experience. If you’re driven to teach others or to pursue advanced research in data science, a PhD is the right degree for you.

Do I need a master’s in order to pursue a PhD.? No. Many who pursue PhDs in Data Science do not already hold advanced degrees, and many PhD programs include all the coursework of a master’s program in the first two years of school. For many students, this is the most time-effective option, allowing you to complete your education in a single pass rather than interrupting your studies after your master’s program.

Can I choose to pursue a PhD after already receiving my master’s? Yes. A master’s program can be an opportunity to get the lay of the land and determine the specific career path you’d like to forge in the world of big data. Some schools may allow you to simply extend your academic timeline after receiving your master’s degree, and it is also possible to return to school to receive a PhD if you have been working in the field for some time.

If a PhD. isn’t necessary, is it a waste of time? While not all students are candidates for PhDs, for the right students – who are keen on doing in-depth research, have the time to devote to many years of school, and potentially have an interest in continuing to work in academia – a PhD is a great choice. For more information on this question, take a look at our article Is a Data Science PhD. Worth It?

Complete List of Data Science PhD Programs

Below you will find the most comprehensive list of schools offering a doctorate in data science. Each school listing contains a link to the program specific page, GRE or a master’s degree requirements, and a link to a page with detailed course information.

Note that the listing only contains true data science programs. Other similar programs are often lumped together on other sites, but we have chosen to list programs such as data analytics and business intelligence on a separate section of the website.

Boise State University  – Boise, Idaho PhD in Computing – Data Science Concentration

The Data Science emphasis focuses on the development of mathematical and statistical algorithms, software, and computing systems to extract knowledge or insights from data.  

In 60 credits, students complete an Introduction to Graduate Studies, 12 credits of core courses, 6 credits of data science elective courses, 10 credits of other elective courses, a Doctoral Comprehensive Examination worth 1 credit, and a 30-credit dissertation.

Electives can be taken in focus areas such as Anthropology, Biometry, Ecology/Evolution and Behavior, Econometrics, Electrical Engineering, Earth Dynamics and Informatics, Geoscience, Geostatistics, Hydrology and Hydrogeology, Materials Science, and Transportation Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $7,236 total (Resident), $24,573 total (Non-resident)

View Course Offerings

Bowling Green State University  – Bowling Green, Ohio Ph.D. in Data Science

Data Science students at Bowling Green intertwine knowledge of computer science with statistics.

Students learn techniques in analyzing structured, unstructured, and dynamic datasets.

Courses train students to understand the principles of analytic methods and articulating the strengths and limitations of analytical methods.

The program requires 60 credit hours in the studies of Computer Science (6 credit hours), Statistics (6 credit hours), Data Science Exploration and Communication, Ethical Issues, Advanced Data Mining, and Applied Data Science Experience.

Students must also complete 21 credit hours of elective courses, a qualifying exam, a preliminary exam, and a dissertation.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,418 (Resident), $14,410 (Non-resident)

Brown University  – Providence, Rhode Island PhD in Computer Science – Concentration in Data Science

Brown University’s database group is a world leader in systems-oriented database research; they seek PhD candidates with strong system-building skills who are interested in researching TupleWare, MLbase, MDCC, Crowd DB, or PIQL.

In order to gain entrance, applicants should consider first doing a research internship at Brown with this group. Other ways to boost an application are to take and do well at massive open online courses, do an internship at a large company, and get involved in a large open-source software project.

Coding well in C++ is preferred.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $62,680 total

Chapman University  – Irvine, California Doctorate in Computational and Data Sciences

Candidates for the doctorate in computational and data science at Chapman University begin by completing 13 core credits in basic methodologies and techniques of computational science.

Students complete 45 credits of electives, which are personalized to match the specific interests and research topics of the student.

Finally, students complete up to 12 credits in dissertation research.

Applicants must have completed courses in differential equations, data structures, and probability and statistics, or take specific foundation courses, before beginning coursework toward the PhD.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,538 per year

Clemson University / Medical University of South Carolina (MUSC) – Joint Program – Clemson, South Carolina & Charleston, South Carolina Doctor of Philosophy in Biomedical Data Science and Informatics – Clemson

The PhD in biomedical data science and informatics is a joint program co-authored by Clemson University and the Medical University of South Carolina (MUSC).

Students choose one of three tracks to pursue: precision medicine, population health, and clinical and translational informatics. Students complete 65-68 credit hours, and take courses in each of 5 areas: biomedical informatics foundations and applications; computing/math/statistics/engineering; population health, health systems, and policy; biomedical/medical domain; and lab rotations, seminars, and doctoral research.

Applicants must have a bachelor’s in health science, computing, mathematics, statistics, engineering, or a related field, and it is recommended to also have competency in a second of these areas.

Program requirements include a year of calculus and college biology, as well as experience in computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,858 total (South Carolina Resident), $22,566 total (Non-resident)

View Course Offerings – Clemson

George Mason University  – Fairfax, Virginia Doctor of Philosophy in Computational Sciences and Informatics – Emphasis in Data Science

George Mason’s PhD in computational sciences and informatics requires a minimum of 72 credit hours, though this can be reduced if a student has already completed a master’s. 48 credits are toward graduate coursework, and an additional 24 are for dissertation research.

Students choose an area of emphasis—either computer modeling and simulation or data science—and completed 18 credits of the coursework in this area. Students are expected to completed the coursework in 4-5 years.

Applicants to this program must have a bachelor’s degree in a natural science, mathematics, engineering, or computer science, and must have knowledge and experience with differential equations and computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $13,426 total (Virginia Resident), $35,377 total (Non-resident)

Harrisburg University of Science and Technology  – Harrisburg, Pennsylvania Doctor of Philosophy in Data Sciences

Harrisburg University’s PhD in data science is a 4-5 year program, the first 2 of which make up the Harrisburg master’s in analytics.

Beyond this, PhD candidates complete six milestones to obtain the degree, including 18 semester hours in doctoral-level courses, such as multivariate data analysis, graph theory, machine learning.

Following the completion of ANLY 760 Doctoral Research Seminar, students in the program complete their 12 hours of dissertation research bringing the total program hours to 36.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $14,940 total

Icahn School of Medicine at Mount Sinai  – New York, New York Genetics and Data Science, PhD

As part of the Biomedical Science PhD program, the Genetics and Data Science multidisciplinary training offers research opportunities that expand on genetic research and modern genomics. The training also integrates several disciplines of biomedical sciences with machine learning, network modeling, and big data analysis.

Students in the Genetics and Data Science program complete a predetermined course schedule with a total of 64 credits and 3 years of study.

Additional course requirements and electives include laboratory rotations, a thesis proposal exam and thesis defense, Computer Systems, Intro to Algorithms, Machine Learning for Biomedical Data Science, Translational Genomics, and Practical Analysis of a Personal Genome.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $31,303 total

Indiana University-Purdue University Indianapolis  – Indianapolis, Indiana PhD in Data Science PhD Minor in Applied Data Science

Doctoral candidates pursuing the PhD in data science at Indiana University-Purdue must display competency in research, data analytics, and at management and infrastructure to earn the degree.

The PhD is comprised of 24 credits of a data science core, 18 credits of methods courses, 18 credits of a specialization, written and oral qualifying exams, and 30 credits of dissertation research. All requirements must be completed within 7 years.

Applicants are generally expected to have a master’s in social science, health, data science, or computer science. 

Currently a majority of the PhD students at IUPUI are funded by faculty grants and two are funded by the federal government. None of the students are self funded.

IUPUI also offers a PhD Minor in Applied Data Science that is 12-18 credits. The minor is open to students enrolled at IUPUI or IU Bloomington in a doctoral program other than Data Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $9,228 per year (Indiana Resident), $25,368 per year (Non-resident)

Jackson State University – Jackson, Mississippi PhD Computational and Data-Enabled Science and Engineering

Jackson State University offers a PhD in computational and data-enabled science and engineering with 5 concentration areas: computational biology and bioinformatics, computational science and engineering, computational physical science, computation public health, and computational mathematics and social science.

Students complete 12 credits of common core courses, 12 credits in the specialization, 24 credits of electives, and 24 credits in dissertation research.

Students may complete the doctoral program in as little as 5 years and no more than 8 years.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,270 total

Kennesaw State University  – Kennesaw, Georgia PhD in Analytics and Data Science

Students pursuing a PhD in analytics and data science at Kennesaw State University must complete 78 credit hours: 48 course hours and 6 electives (spread over 4 years of study), a minimum 12 credit hours for dissertation research, and a minimum 12 credit-hour internship.

Prior to dissertation research, the comprehensive examination will cover material from the three areas of study: computer science, mathematics, and statistics.

Successful applicants will have a master’s degree in a computational field, calculus I and II, programming experience, modeling experience, and are encouraged to have a base SAS certification.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,328 total (Georgia Resident), $19,188 total (Non-resident)

New Jersey Institute of Technology  – Newark, New Jersey PhD in Business Data Science

Students may enter the PhD program in business data science at the New Jersey Institute of Technology with either a relevant bachelor’s or master’s degree. Students with bachelor’s degrees begin with 36 credits of advanced courses, and those with master’s take 18 credits before moving on to credits in dissertation research.

Core courses include business research methods, data mining and analysis, data management system design, statistical computing with SAS and R, and regression analysis.

Students take qualifying examinations at the end of years 1 and 2, and must defend their dissertations successfully by the end of year 6.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $21,932 total (New Jersey Resident), $32,426 total (Non-resident)

New York University  – New York, New York PhD in Data Science

Doctoral candidates in data science at New York University must complete 72 credit hours, pass a comprehensive and qualifying exam, and defend a dissertation with 10 years of entering the program.

Required courses include an introduction to data science, probability and statistics for data science, machine learning and computational statistics, big data, and inference and representation.

Applicants must have an undergraduate or master’s degree in fields such as mathematics, statistics, computer science, engineering, or other scientific disciplines. Experience with calculus, probability, statistics, and computer programming is also required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,332 per year

View Course Offering

Northcentral University  – San Diego, California PhD in Data Science-TIM

Northcentral University offers a PhD in technology and innovation management with a specialization in data science.

The program requires 60 credit hours, including 6-7 core courses, 3 in research, a PhD portfolio, and 4 dissertation courses.

The data science specialization requires 6 courses: data mining, knowledge management, quantitative methods for data analytics and business intelligence, data visualization, predicting the future, and big data integration.

Applicants must have a master’s already.

Delivery Method: Online GRE: Required 2022-2023 Tuition: $16,794 total

Stevens Institute of Technology – Hoboken, New Jersey Ph.D. in Data Science

Stevens Institute of Technology has developed a data science Ph.D. program geared to help graduates become innovators in the space.

The rigorous curriculum emphasizes mathematical and statistical modeling, machine learning, computational systems and data management.

The program is directed by Dr. Ted Stohr, a recognized thought leader in the information systems, operations and business process management arenas.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $39,408 per year

University at Buffalo – Buffalo, New York PhD Computational and Data-Enabled Science and Engineering

The curriculum for the University of Buffalo’s PhD in computational and data-enabled science and engineering centers around three areas: data science, applied mathematics and numerical methods, and high performance and data intensive computing. 9 credit course of courses must be completed in each of these three areas. Altogether, the program consists of 72 credit hours, and should be completed in 4-5 years. A master’s degree is required for admission; courses taken during the master’s may be able to count toward some of the core coursework requirements.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,310 per year (New York Resident), $23,100 per year (Non-resident)

University of Colorado Denver – Denver, Colorado PhD in Big Data Science and Engineering

The University of Colorado – Denver offers a unique program for those students who have already received admission to the computer science and information systems PhD program.

The Big Data Science and Engineering (BDSE) program is a PhD fellowship program that allows selected students to pursue research in the area of big data science and engineering. This new fellowship program was created to train more computer scientists in data science application fields such as health informatics, geosciences, precision and personalized medicine, business analytics, and smart cities and cybersecurity.

Students in the doctoral program must complete 30 credit hours of computer science classes beyond a master’s level, and 30 credit hours of dissertation research.

The BDSE fellowship requires students to have an advisor both in the core disciplines (either computer science or mathematics and statistics) as well as an advisor in the application discipline (medicine and public health, business, or geosciences).

In addition, the fellowship covers full stipend, tuition, and fees up to ~50k for BDSE fellows annually. Important eligibility requirements can be found here.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $55,260 total

University of Marylan d  – College Park, Maryland PhD in Information Studies

Data science is a potential research area for doctoral candidates in information studies at the University of Maryland – College Park. This includes big data, data analytics, and data mining.

Applicants for the PhD must have taken the following courses in undergraduate studies: programming languages, data structures, design and analysis of computer algorithms, calculus I and II, and linear algebra.

Students must complete 6 qualifying courses, 2 elective graduate courses, and at least 12 credit hours of dissertation research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $16,238 total (Maryland Resident), $35,388 total (Non-resident)

University of Massachusetts Boston  – Boston, Massachusetts PhD in Business Administration – Information Systems for Data Science Track

The University of Massachusetts – Boston offers a PhD in information systems for data science. As this is a business degree, students must complete coursework in their first two years with a focus on data for business; for example, taking courses such as business in context: markets, technologies, and societies.

Students must take and pass qualifying exams at the end of year 1, comprehensive exams at the end of year 2, and defend their theses at the end of year 4.

Those with a degree in statistics, economics, math, computer science, management sciences, information systems, and other related fields are especially encouraged, though a quantitative degree is not necessary.

Students accepted by the program are ordinarily offered full tuition credits and a stipend ($25,000 per year) to cover educational expenses and help defray living costs for up to three years of study.

During the first two years of coursework, they are assigned to a faculty member as a research assistant; for the third year students will be engaged in instructional activities. Funding for the fourth year is merit-based from a limited pool of program funds

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $18,894 total (in-state), $36,879 (out-of-state)

University of Nevada Reno – Reno, Nevada PhD in Statistics and Data Science

The University of Nevada – Reno’s doctoral program in statistics and data science is comprised of 72 credit hours to be completed over the course of 4-5 years. Coursework is all within the scope of statistics, with titles such as statistical theory, probability theory, linear models, multivariate analysis, statistical learning, statistical computing, time series analysis.

The completion of a Master’s degree in mathematics or statistics prior to enrollment in the doctoral program is strongly recommended, but not required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,814 total (in-state), $22,356 (out-of-state)

University of Southern California – Los Angles, California PhD in Data Sciences & Operations

USC Marshall School of Business offers a PhD in data sciences and operations to be completed in 5 years.

Students can choose either a track in operations management or in statistics. Both tracks require 4 courses in fall and spring of the first 2 years, as well as a research paper and courses during the summers. Year 3 is devoted to dissertation preparation and year 4 and/or 5 to dissertation defense.

A bachelor’s degree is necessary for application, but no field or further experience is required.

Students should complete 60 units of coursework. If the students are admitted with Advanced Standing (e.g., Master’s Degree in appropriate field), this requirement may be reduced to 40 credits.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $63,468 total

University of Tennessee-Knoxville  – Knoxville, Tennessee The Data Science and Engineering PhD

The data science and engineering PhD at the University of Tennessee – Knoxville requires 36 hours of coursework and 36 hours of dissertation research. For those entering with an MS degree, only 24 hours of course work is required.

The core curriculum includes work in statistics, machine learning, and scripting languages and is enhanced by 6 hours in courses that focus either on policy issues related to data, or technology entrepreneurship.

Students must also choose a knowledge specialization in one of these fields: health and biological sciences, advanced manufacturing, materials science, environmental and climate science, transportation science, national security, urban systems science, and advanced data science.

Applicants must have a bachelor’s or master’s degree in engineering or a scientific field. 

All students that are admitted will be supported by a research fellowship and tuition will be included.

Many students will perform research with scientists from Oak Ridge national lab, which is located about 30 minutes drive from campus.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,468 total (Tennessee Resident), $29,656 total (Non-resident)

University of Vermont – Burlington, Vermont Complex Systems and Data Science (CSDS), PhD

Through the College of Engineering and Mathematical Sciences, the Complex Systems and Data Science (CSDS) PhD program is pan-disciplinary and provides computational and theoretical training. Students may customize the program depending on their chosen area of focus.

Students in this program work in research groups across campus.

Core courses include Data Science, Principles of Complex Systems and Modeling Complex Systems. Elective courses include Machine Learning, Complex Networks, Evolutionary Computation, Human/Computer Interaction, and Data Mining.

The program requires at least 75 credits to graduate with approval by the student graduate studies committee.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $12,204 total (Vermont Resident), $30,960 total (Non-resident)

University of Washington Seattle Campus – Seattle, Washington PhD in Big Data and Data Science

The University of Washington’s PhD program in data science has 2 key goals: training of new data scientists and cyberinfrastructure development, i.e., development of open-source tools and services that scientists around the world can use for big data analysis.

Students must take core courses in data management, machine learning, data visualization, and statistics.

Students are also required to complete at least one internship that covers practical work in big data.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $17,004 per year (Washington resident), $30,477 (non-resident)

University of Wisconsin-Madison – Madison, Wisconsin PhD in Biomedical Data Science

The PhD program in Biomedical Data Science offered by the Department of Biostatistics and Medical Informatics at UW-Madison is unique, in blending the best of statistics and computer science, biostatistics and biomedical informatics. 

Students complete three year-long course sequences in biostatistics theory and methods, computer science/informatics, and a specialized sequence to fit their interests.

Students also complete three research rotations within their first two years in the program, to both expand their breadth of knowledge and assist in identifying a research advisor.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,728 total (in-state), $24,054 total (out-of-state)

Vanderbilt University – Nashville, Tennessee Data Science Track of the BMI PhD Program

The PhD in biomedical informatics at Vanderbilt has the option of a data science track.

Students complete courses in the areas of biomedical informatics (3 courses), computer science (4 courses), statistical methods (4 courses), and biomedical science (2 courses). Students are expected to complete core courses and defend their dissertations within 5 years of beginning the program.

Applicants must have a bachelor’s degree in computer science, engineering, biology, biochemistry, nursing, mathematics, statistics, physics, information management, or some other health-related field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $53,160 per year

Washington University in St. Louis – St. Louis, Missouri Doctorate in Computational & Data Sciences

Washington University now offers an interdisciplinary Ph.D. in Computational & Data Sciences where students can choose from one of four tracks (Computational Methodologies, Political Science, Psychological & Brain Sciences, or Social Work & Public Health).

Students are fully funded and will receive a stipend for at least five years contingent on making sufficient progress in the program.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $59,420 total

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

The PhD in data science at Worcester Polytechnic Institute focuses on 5 areas: integrative data science, business intelligence and case studies, data access and management, data analytics and mining, and mathematical analysis.

Students first complete a master’s in data science, and then complete 60 credit hours beyond the master’s, including 30 credit hours of research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $28,980 per year

Yale University – New Haven, Connecticut PhD Program – Department of Stats and Data Science

The PhD in statistics and data science at Yale University offers broad training in the areas of statistical theory, probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods. Students complete 12 courses in the first year in these topics.

Students are required to teach one course each semester of their third and fourth years.

Most students complete and defend their dissertations in their fifth year.

Applicants should have an educational background in statistics, with an undergraduate major in statistics, mathematics, computer science, or similar field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $46,900 total

• Related Programs

10 Best Online PhD in Data Science Programs [2024 Guide]

If you have a passion for mining information from large amounts of data, you should consider exploring PhD Data Science online programs.

Editorial Listing ShortCode:

Furthering your education in this field can help take your career to the next level. By earning your PhD, you may increase not only your knowledge but also your salary.

Universities Offering Online Data Science Doctorate Degree Programs

Methodology: The following school list is in alphabetical order. To be included, a college or university must be regionally accredited and offer degree programs online or in a hybrid format. In addition, the schools offer online data science programs .

1. Capella University

Founded in 1993, private Capella University offers online doctorate, master’s, and bachelor’s degrees. The Minneapolis-based school’s 38,000 enrolled students represent 50 states and 61 countries. Doctoral students account for more than 27 percent of Capella University’s student body.

• DBA in Business Intelligence – Data Analytics

Capella University is accredited by the Higher Learning Commission.

2. Capitol Technology University

Capitol Technology University is a private university located near the nation’s capital in South Laurel, Maryland. Established in 1927, the university now offers undergraduate and master’s programs in business, computer science, cybrsecurity, and engineering.

Capitol Technology University is a military-friendly school founded by a Navy veteran. It holds the prestigious SC Media Award for Best Cybersecurity Higher Education Program. The school’s annual enrollment is approximately 850 students.

• PhD in Business Analytics and Data Science

Capitol Technology University  is accredited by the Middle States Commission on Higher Education.

3. Colorado Technical University

Colorado Technical University was founded in 1965. This private university offers undergraduate, graduate, and doctoral degrees in business management and technology.

The school has earned the U.S. News & World Report “Best for Veterans” designation, the Council of College and Military Educators (CCME) Institution Award, and recognition as a center of Academic Excellence in Information Assurance and Cyber Defense from the NSA and Department of Homeland Security.

Annual enrollment stands at around 26,000 students.

• Doctor of Computer Science – Big Data Analytics

Colorado Technical University  is accredited by the Higher Learning Commission.

4. Columbia University

New York City’s Columbia University is a private Ivy League research university founded in 1754. It stands today as the oldest university in New York City. Columbia operates four undergraduate schools and 15 graduate/professional schools.

Bachelor’s, master’s, and PhD programs covering business, medicine, liberal arts, technology, and political science are available. Student enrollment at Columbia stands at roughly 33,413.

• PhD in Data Science

Columbia  is accredited by the Middle States Commission on Higher Education.

5. Grand Canyon University

Grand Canyon University is a private Christian college based in Phoenix, Arizona. With a student enrollment of 70,000 students, it is considered to be the world’s largest Christian university.

Grand Canyon University offers bachelor’s, master’s, and doctoral degrees in business, education, health sciences, liberal arts, and nursing. The school offers a total of 200 academic programs throughout its nine colleges.

• DBA in Data Analytics

Grand Canyon University is accredited by the Higher Learning Commission.

6. Harrisburg University of Science and Technology

Founded in 2001, Harrisburg University of Science and Technology is a STEM-focused institution with campuses in Harrisburg and Philadelphia.

This private university offers bachelor’s degrees, master’s degrees, doctoral degrees, and certificate programs. The nearly 6,000 students enrolled study degree paths related to applied science and technology.

• PhD in Data Sciences

Harrisburg University of Science and Technology is accredited by the Middle States Commission on Higher Education.

7. Indiana University-Purdue University Indianapolis

Indiana University-Purdue University Indianapolis is a public research university offering more than 225 options for bachelor’s, master’s, and doctoral degrees across 18 different schools. The university’s campus is based in Indianapolis, Indiana.

The more than 30,000 students enrolled pursue degrees in majors like art and design, business, education, engineering, law, liberal arts, medicine, nursing, and social work.

• PhD in Data Science (on-campus)

Indiana University – Purdue University Indianapolis  is accredited by the Higher Learning Commission.

8. National University

National University is a network of nonprofit educational institutions that is headquartered in San Diego, California. It offers a range of bachelor’s degrees, master’s degrees, doctoral degrees, and certificates in business, education, marriage and family therapy, psychology, and technology.

NU has over 30,000 students enrolled and more than 220,000 alumni from around the world.

National University is accredited by the Western Association of Schools and Colleges.

9. Stevens Institute of Technology

Located in Hoboken, New Jersey, Stevens Institute of Technology is a private research institution with an enrollment of approximately 6,125 students. Founded in 1870, the school has been named among the “Best Value Colleges” by the Princeton Review.

Additionally, the Princeton Review ranks Stevens Institute of Technology among its “Top 15 for Internships.” The school’s undergraduate and graduate students represent 47 states and 60 countries. Students can pursue bachelor’s, master’s, doctoral, and certificate programs.

Stevens Institute of Technology is accredited by the Middle States Commission on Higher Education.

10. University of Central Florida

Located along Orlando’s Space Coast, the University of Central Florida is a public research university with a student enrollment of approximately 69,525. It offers bachelor’s, master’s, and doctoral programs.

Students can pursue degrees in arts and humanities, business, engineering, computer science, health science, medicine, and nursing. The University of Central Florida has been ranked as a “Best Southeastern College” by the Princeton Review.

• PhD in Big Data Analytics

The  University of Central Florida  is accredited by the Southern Association of Colleges and Schools Commission on Colleges.

Online PhD in Data Science Programs

Data science is exactly what it sounds like – the study of data. Data scientists look at sets of data and notice patterns that emerge. They identify key information that data presents which may not seem readily apparent at first.

If you are someone that notices the small details while also keeping an eye on the bigger picture, a career in data science may be right for you. If you find trends and patterns in large amounts of data, you may be well-suited for this field.

What kind of job can you expect to have as a data scientist? In the last few years, Glassdoor has continuously ranked data scientist as one of the best jobs to have in the United States. The options for specific jobs are numerous and varied.

For example, one data scientist may work as a statistician and interpret statistical information for the U.S. Department of Agriculture. Another data scientist may be a business intelligence developer for Discover, creating strategies for businesses to make more informed decisions about their company.

Data Science Pros and Cons

As with any financial and length time investment, you should consider both the pros and the cons of earning your PhD in an online data science program.

All salary data in this table was provided by the Bureau of Labor Statistics.

Choosing to pursue an online PhD in a data science program is decision that must be taken into careful consideration, but there are many benefits to completing a program.

Data Science Curriculum & Courses

Curriculum for data science programs is heavily focused on analysis and research. Examples of courses offered by universities like Dakota State University and the University of North Texas are listed below.

• Information Systems – This course is designed to help students learn about the role information systems have for businesses and other organizations.
• Applied Statistics – This class teaches how to use statistical software to study data samples and make inferences based on the data presented.
• Project and Change Management – This class is designed to help students learn the underlying principles for managing information systems and how to utilize software for project management.
• Technology for Mobile Devices – Students in this course study the process of developing apps for mobile devices like smartphones and tablets.
• Advanced Network Technology and Management – This class helps students learn how to work with a model network environment, including how to find solutions for problems with the network.
• Seminar in Research and Research Methodology – Students in this seminar are asked to develop a research proposal and participate in a research study.
• Knowledge Management Tools and Technologies – This course introduces students to a variety of technologies including those associated with knowledge management and IT infrastructure.
• Seminar in Communication and Use of Information – This class explores the roles communication plays at various levels in society.
• Readings in Information Science – Students in this class study texts which emphasize methodological and theoretical issues.
• Medical Geography – In this course, students study the correlation between location and health care and work on their own projects.

Exploring the curriculum offered by different universities can help you determine which online PhD program is best suited for your interests and your needs.

Data Science PhD Admissions

Before applying for a PhD program, you will want to ensure that you have all the application materials on hand, including the commonly required materials listed below.

• Reference letters – You should request these documents well before your application deadline as mentors may not be able to honor a last-minute request due to time constraints.
• All transcripts – These grades will include both undergraduate and graduate level courses.
• Letter of intent – Be prepared to explain in writing why you want to enroll in the program and what you plan to do after its completion.
• Application fee – Fees to cover administrative costs of reviewing your application can add up, so make sure to budget for the costs of each one.
• Resume – Schools want to know your background in not just education but in the job market as well.
• Specific program application – Your prospective school will most likely have its own unique application on its official website.

Save yourself the stress of anxiously waiting to receive documents from an institution or mentor in time and compile them well ahead of the due date.

Data Science PhD Careers & Salaries

According to the U.S. Bureau of Labor Statistics , computer and information research scientists earned a median of $131,490 a year. Data scientists as a group earn increasingly high salaries in various industries including research laboratories, government departments, and a variety of companies focused on technology.

Some of the top companies that utilize data scientists are IBM, Amazon, Microsoft, Facebook, Oracle, Google, and Apple. These multi-billion dollar companies are consistently hiring data scientists to interpret the large amounts of data, or “big data,” that is collected via their services.

Data scientists can expect to work in roles where job duties include designing data models, organizing data from multiple sources, and identifying trends in data.

Data scientists use a comprehensive process for gathering and analyzing information including asking questions, acquiring data, storing data, using models to interpret it, and presenting their findings to stakeholders in the community.

According to the Bureau of Labor Statistics, some careers in the data science field include:

Whatever the job title, data scientists continually earn a significant amount more than employees in other fields.

Data Science Accreditation

Before clicking the “submit” button on your application to a PhD program, you will want to ensure that the university you are applying to is accredited, meaning it is recognized as a legitimate program that offers quality coursework and research opportunities.

If you decide to apply to a program related to computer technology or engineering, the Accreditation Board for Engineering and Technology (ABET) determine which schools offer suitable coursework and requirements for these fields. Also be sure that your prospective university is regionally accredited, the gold-standard for accreditation in the United States.

Search on your prospective schools’ website for information regarding their accreditation status. You will want to ensure that the schools you apply to are regionally accredited so you can get the most out of your PhD experience and your credits will be more likely to transfer should you switch schools while studying.

Data Science Professional Organizations

Joining a professional organization can help to advance your career by connecting you with other individuals who work in the same field.

Professional organizations offer a multitude of benefits, including networking opportunities (which may help to connect you with future employers), and they can also provide inspiration for completing your PhD program, decreasing feelings of isolation that can accompany students.

• Association for Information Science and Technology – This organization states its role “advances the information sciences and similar applications of information technology by helping members build their skills and [develop] their careers” via several different ways, including training and education.
• Association of Information Technology Professionals – This agency gives members advice on how to pursue certain career paths while also providing discounts on certifications and resources for professional development.
• International Association for Social Science Information Services and Technology – IASSIST has 300 members from countries around the world. They offer resources for professionals from sectors such as non-profits, academia, and government.

While some organizations may have a yearly membership fee, the potential gains for job opportunities and professional development through these groups can easily offset those costs.

Financial Aid

Across the nation, the average cost of obtaining a PhD online is between $4,000 and $20,000.  As a student in a PhD program, you can expect to have costs from tuition, books, personal supplies, transportation, etc. Without the time or energy for a full-time or often, even part-time job, you should explore all financial aid options available.

Financial aid for PhD students can come in the form of loans, scholarships, and grants. Grants and scholarships typically do not have to be paid back, but loans are borrowed money which may accrue interest and should be a last resort for students.

Some specific scholarships and grants are designed with scientists, including data scientists, in mind. For example, the National Science Foundation Graduate Research Fellowship is designed to support students who are pursuing research-based doctoral degrees.

Previous recipients include Nobel Prize winners, a U.S. Secretary of Energy, and the founder of Google.

Another common source of money comes from taking on teaching assistant positions within your university or becoming an assistant lecturer. Both positions are great for gaining experience teaching in your academic department while generating income to offset the costs incurred from your years of study.

How long does it take to get a PhD in data science?

It takes an average of 71 credits to complete a PhD in data science. On top of this, students may also have responsibilities to research and/or teach, which can make the process take even longer.

It is not unusual for some PhD programs to take anywhere from four to five years to complete.

Is a PhD in data science worth it?

Whether or not a PhD in data science is “worth it” depends on a number of factors. Do you have the time available for next few years (possibly longer) to invest in this opportunity? Are you motivated enough to complete coursework while also on a shoestring budget?

Search for employment positions you are interested in and take a look at the education requirements employers are requesting. These factors may effect your decision in potentially pursuing an online masters in data science instead.

Can I do a PhD in data science?

Whether or not you complete a PhD in data science depends on your ability to stay focused and motivated. PhD programs are notoriously intensive, and they are not for everyone.

You should have a better reason for applying to a program than simply not knowing what to do in today’s job market.

Getting Your PhD in Data Science Online

Obtaining your doctoral degree in data science is not an easy task, but it is also not an impossible one. If you are serious about pursuing your PhD, talk to experts in the field. The admissions departments at prospective universities can help put you in touch with recruiters who can give you more information about the program.

Joining a professional organization can help you connect with individuals who are working in the field, many of whom will have obtained their higher education degree. With careful planning and the right information to make informed career choices, you can further your education and your sense of accomplishment.

PhD in Data Science

The PhD in Data Science is designed to be completed fully in-person at UChicago’s Hyde Park campus. There are no online options at this time. Newly admitted students are guaranteed full-funding for up to 5 years and provided with an annual stipend, contingent on satisfactory progress towards the degree.

First-Year Requirements

The standard first-year program requires students to complete nine courses: four required courses (1-4 below); one elective either in mathematical foundations or scalability and computing (pick from either 5 or 6); and four graduate electives that can come from proposed courses in data science as well as existing courses in Computer Science or Statistics. Some students, after consulting with the graduate committee advisor, might decide to take the nine courses over the first two years:

Required Courses:

• Foundations of Machine Learning and AI Part 1
• Responsible Use of Data and Algorithms
• Data Interaction
• Systems for Data and Computers/Data Design
• Foundations of Machine Learning and AI Part 2
• Data Engineering and Scalable Computing

Synthesis project

Students will take courses during the first two years after which they focus primarily on their research. A milestone in this transition is completion of a synthesis project before the end of the second year in the program. Thesis projects can be done in partnership with any of DSI affiliates and aims to meaningfully connect PhD students to their chosen focus areas.

Thesis Advisor and Dissertation Committee

Students typically select a thesis advisor by the beginning of their second year. By the end of the third year, each PhD student, after consultation with their advisor, shall establish a thesis committee of at least three faculty members, including the advisor, with at least half of the members coming from the Committee on Data Science (CODAS) .

Proposal Presentation and Admission to Candidacy

By the end of the third year, students should have scheduled and completed a proposal presentation to their committee in order to be advanced to candidacy. The proposal presentation is typically an hour-long meeting that begins with a 30-minute presentation by the student followed by a question and discussion period with the committee.

Dissertation Defense

The PhD degree will be awarded to candidates following a successful defense and the electronic submission of the final version of the dissertation to the University’s Dissertation Office.

Doctor of Philosophy in Data Science

Developing future pioneers in data science

The School of Data Science at the University of Virginia is committed to educating the next generation of data science leaders. The Ph.D. in Data Science is designed to impart the skills and knowledge necessary to enable research and discovery in data science methods. Because the end goal is to extract knowledge and enable discovery from complex data, the program also boasts robust applied training that is geared toward interdisciplinary collaboration. Doctoral candidates will master the computational and mathematical foundations of data science, and develop competencies in data engineering, software development, data policy and ethics. 

Doctoral students in our program apprentice with faculty and pursue advanced research in an interdisciplinary, collaborative environment that is often focused on scientific discovery via data science methods. By serving as teaching assistants for the School’s undergraduate and graduate programs, they learn to be adroit educators and hone their critical thinking and communication skills.

LEARNING OUTCOMES

Pursuing a Ph.D. in Data Science will prepare you to become an expert in the field and work at the cutting edge of a new discipline. According to LinkedIn’s most recent Emerging Jobs Report, data science is booming and data scientist is one of the top three fastest growing jobs. A Ph.D. in Data Science from the University of Virginia opens career paths in academia, industry or government. Graduates of our program will:

• Understand data as a generic concept, and how data encodes and captures information
• Be fluent in modern data engineering techniques, and work with complex and large data sets
• Recognize ethical and legal issues relevant to data analytics and their impact on society 
• Develop innovative computational algorithms and novel statistical methods that transform data into knowledge
• Collaborate with research teams from a wide array of scientific fields 
• Effectively communicate methods and results to a variety of audiences and stakeholders
• Recognize the broad applicability of data science methods and models 

Graduates of the Ph.D. in Data Science will have contributed novel methodological research to the field of data science, demonstrated their work has impactful interdisciplinary applications and defended their methods in an open forum.

A Week in the Life: First-Year Ph.D. Student

Ph.D. Student Profile: Jade Preston

Ph.D. Student Profile: Beau LeBlond

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Data Science Doctoral Program

Program details.

Gain in-demand skills in emerging areas like artificial intelligence, machine learning and language processing in a Ph.D. program designed with input from industry leaders.

An interdisciplinary degree program of the Schaefer School of Engineering and Science and the Stevens School of Business, the data science Ph.D. curriculum drives students to master the bedrock principles, methods and systems for extracting insights from rich data sets. Then, you’ll apply those theories, techniques and applications in practical research alongside Stevens faculty who are working at the forefront of the data science field. Our graduates go on to pursue research careers in academia and secure important positions in industries like business, financial services and life sciences.

The Department of Computer Science offers dynamic opportunities to explore leading-edge research within a close community of faculty mentors. You'll be able to study under a faculty mentor in the area that you find most exciting:

Theoretical underpinnings of data science, including machine learning and artificial intelligence

Applications of data science to financial services

Applications of data science to the life sciences

Computer Science Research

The computer science department at Stevens offers you a maximum amount of flexibility to pursue research opportunities in cutting-edge, competitive areas of exploration like secure systems, machine learning, cryptography and visual computing. You’ll work with recognized leaders in the field, gain exposure to top industry labs and learn sought-after principles that will help propel your career. Learn more about research in the Department of Computer Science.

The Stevens Advantage

Just 15 minutes away from the center of Manhattan, Stevens sits near the heart of one of the world’s top technology hubs. This proximity gives Stevens students exemplary recruitment opportunities with some of the biggest names in business and technology.

More Advantages to Our Program

Cross-disciplinary curriculum and research opportunities

Study under well-known researcher faculty

Application-oriented research

Highly collaborative environment

Opportunity for industry collaborations

Access to leading research universities and national laboratories in the New York City area

Areas of Focus

Mathematical and statistical modelling including multivariate analytics, financial time series and dynamic programming techniques

Machine learning and artificial intelligence applications for statistical learning and financial analytics

Computational systems, exploring advanced algorithm design, distributed systems and cloud technologies

Data management at scale, involving a deeper dive into data technologies, mobile systems and data management

Additional Information

Who should apply.

We welcome applicants with a master’s degree in a technical discipline (such as computer science, business intelligence and analytics, financial analytics, financial engineering or biomedical engineering and chemical biology). However, exceptional applicants with a bachelor’s degree and relevant work experience will also be considered.

Students may begin this Ph.D. program in the fall semester only. Therefore, applications must be submitted by February 1 for admission the following fall. Applicants are generally notified of their admission status around February 15.

Program Admission Requirements

An excellent GMAT or GRE score (required for both part-time and full-time applicants)

Prerequisite courses in calculus, statistics, probability, algebra and database management

Fluency in at least one programming language, like C++ or Java

For international students: An excellent TOEFL or IELTS score

Writing sample (such as journal or conference publication, thesis, or research reports)

View General Admissions Requirements >

Data Science Doctoral Program Curriculum Overview

Coursework in the Data Science program is supplemented by rigorous research requirements that challenge students to discover creative solutions to problems in data analysis and computer science. As you work to create original, substantial research for your dissertation, you’ll be supported by faculty advisors from both the business and engineering schools at Stevens, a tremendous advantage in helping you prepare for a research career. A distinguishing feature of the curriculum is its flexibility. Core courses support the four pillars of the program — mathematical and statistical modeling, machine learning and A.I., computational systems, and data management — while students select a concentration that aligns with their career interests.

Concentrations

The program offers two customizable concentrations that draw upon Stevens’ leadership in financial services and life sciences. You'll select at least three courses from either concentration, or work with your advisor to create a concentration in another discipline.

Financial services

This concentration prepares students to lead forays into areas such as financial innovation, high-frequency trading, large-scale portfolio optimization, automated investment systems, financial data mining and visualization, and trade surveillance and financial fraud detection. These topics will be covered with emphasis on practical solutions to the challenges facing investment banks, hedge funds, mutual funds, exchanges and regulators.

Life sciences

This concentration prepares you to pursue advanced research topics, such as computational modeling in biology and biomedical science, bioinformatics, computational and medicinal chemistry, and biomedical data reduction. Statistical modeling, data management and machine learning techniques will help you identify trends in healthcare data and direct research in the pharmaceutical industry, in government or at hospitals.

General electives

With your advisor's approval, you may select from a list of approved general electives to round out your course requirements. Courses in areas like applied machine learning, distributed systems and cloud computing, cognitive computing and web mining are available.

Doctoral Dissertation and Advisory Committee

Following completion of the written exams and all coursework, you are required to write and defend a dissertation in a selected area of concentration. It is expected your dissertation will contribute to the creation of knowledge and the development of theory and practice in a selected area. The dissertation, and related research, is the most significant component of your doctoral degree, and will prepare you for the challenges of doing original work and getting published in competitive peer-reviewed journals.

LEARN MORE ABOUT GENERAL REQUIREMENTS >

If you have existing graduate credits or experience in this area of study, contact  [email protected]  to discuss opportunities to include it in the curriculum.

A Tech Forward Education

An expert in data mining and deep learning, Dr. Lappas is an authority in the business impact of fake reviews in social media.

Data Science Faculty

A former chief scientist at AT&T Labs, Dr. Belanger has earned more than 30 patents related to data science and business analytics.

David Belanger

A highly cited business researcher investigating applications of machine learning and social network algorithms to solve finance problems.

Germán Creamer

Dr. Florescu is an expert in creating stochastic models for practical application. He leads an international conference on high frequency in finance.

Ionut Florescu

Related Programs

Computer science doctoral program.

Prepare to make an enduring impact in fields like machine learning, artificial intelligence and cybersecurity with a Ph.D. in computer science from Stevens.

Boston University Academics

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PhD in Computing & Data Sciences

For more information and to get in touch, please visit the Faculty of Computing & Data Sciences website .

The PhD program in Computing & Data Sciences (CDS) at Boston University prepares its graduates to make significant contributions to the art, science, and engineering of computational and data-driven processes that are woven into all aspects of society, economy, and public discourse, leading to solutions of problems and synthesis of knowledge related to the methodical, generalizable, and scalable extraction of insights from data as well as the design of new information systems and products that enable actionable use of those insights to advance scholarly as well as practical pursuits in a wide range of application domains.

Applicants to the PhD program in CDS are expected to have earned a bachelor’s or master’s degree in one of the methodological or applied disciplines relating to the computational and data-driven areas of scholarship in CDS. They are expected to possess basic mathematical and computational competencies, and demonstrable propensity for cross-disciplinary work. To accommodate a diversity of student backgrounds and preparations, a holistic admission review is utilized. As such, GRE tests and scores are not required, but could be optionally provided and considered as part of the applicant’s portfolio, which may also include evidence of prior, relevant preparation, including creative works, software code repositories, etc. Special attention will be paid to applicants from underrepresented backgrounds in computing and data science disciplines.

Completion of the PhD degree in CDS requires coursework covering breadth and depth topics spanning the foundational, applied, and sociotechnical dimensions of computing and data science; completion of research rotations that expose students to ongoing projects; completion of a cohort-based training on ethical and responsible computing; and successful proposal and defense of a doctoral thesis.

For their thesis work, and in preparation for careers in academia, industry, and government, CDS PhD students are expected to pursue theoretical, applied, or empirical studies leading to solution of new problems and synthesis of new knowledge in a topic area determined in consultation with their mentors and collaborators, which may include external researchers and practitioners in industrial and academic research laboratories.

Upon completion of the program, students will be prepared to pursue careers in which they lead independent cutting-edge research and development agendas, whether in academia (by teaching, mentoring, and supervising teams of students engaged in scholarly pursuits) or in industry (by collaborating, directing, and effectively managing diverse teams of practitioners working at the forefront of industrial R&D).

Learning Outcomes

The following learning outcomes explain what you will be able to do at the end of your time as a CDS PhD candidate, as a result of earning your degree.

• Exhibit a strong grasp of the principles governing the design and implementation of the methodological approaches for computational and data-driven inquiry.
• Identify the literature and demonstrate mastery of the compendium of works relevant to a well-defined area of research inquiry in computing and data sciences.
• Show capacity to engage meaningfully in and materially contribute to multidisciplinary research and development endeavors.
• Evidence a strong sense of social and professional responsibility for decisions related to the development and deployment of computational and data-driven technologies.
• Assess and argue the merits, limitations, and possibilities of new research work in a specialized area at the level commensurate with standards of scholarly venues in that area.
• Formulate and pursue a research agenda leading to solution of new problems and to synthesis of new knowledge shared through peer-reviewed publications.

Course Requirements

Sixteen term courses (64 units) are required for post-BA/BS students and 12 term courses (48 units) are required for post-MA/MS students. Students with prior graduate work (including master’s degrees) may be able to transfer up to two courses (8 units) as long as these units were not used to fulfill matriculation requirements, upon the recommendation of the student’s academic advisor, and subject to approval by the Associate Provost for CDS.

Of the 16 courses, up to 3 undergraduate courses (12 units) may be counted as background courses, selected in consultation with the student’s academic advisor and subject to approval by the Associate Provost for CDS. Other than these remedial courses, all other courses must be graduate-level courses or directed studies offered by CDS or by other BU departments in order to satisfy the following degree requirements.

The methodology core requirement ensures that students possess foundational knowledge and competencies in a subset of the following eight methodological areas of CDS:

• Mathematical Foundations of Data Science
• Statistical Modeling and Inference
• Efficient and Scalable Algorithms
• Predictive Analytics and Machine Learning
• Combinatorial Optimization and Algorithms
• Computational Complexity
• Programming and Software Design
• Large-scale Data Management

A list of courses that can be used to satisfy these competencies will be maintained on the website for CDS. Students who start their PhD program in CDS are expected to satisfy at least six of these competencies. Students who complete the course requirement for the PhD program in a cognate discipline are expected to satisfy at least four of these competencies.

The subject core requirement ensures that students establish depth in one area of inquiry that is aligned with either the methodological or applied dimensions of CDS. Subject areas are defined by groups of CDS faculty members working in related disciplinary and/or interdisciplinary areas of research who expect their prospective students to have enough depth in the subset of topics to enable them to tackle doctoral-level research in these topics. The set of subject areas as well as a list of preapproved graduate-level courses offered in CDS or elsewhere at BU that can be used to satisfy each subject area will be maintained on the website for CDS.

During the first two years in the program, all PhD candidates in CDS must complete three cohort-based requirements; namely, a two-term training course (4 units) covering various aspects of the responsible and ethical conduct of computational and data-driven research, a two-term doctoral seminar (4 units) that introduces them to the research portfolios of CDS faculty members as well as to the skills and capacities needed for success as scholars, and at least two research or lab rotations (8 units) that expose them to real-world computational and data-driven applications that must be tackled through effective multidisciplinary teamwork.

A cumulative GPA not less than 3.3 must be maintained for all non-Pass/Fail courses taken to satisfy the methodology core requirement and the subject core requirement of the degree, excluding any background courses and excluding any transferred units. Students who receive grades of B– or lower in any three courses taken at BU will be withdrawn from the program.

Language Requirement

There is no foreign language requirement for the PhD degree in CDS.

Qualifying Examinations

No later than the end of the sixth term (third year), all PhD candidates in CDS must pass a public oral examination administered by a committee of three faculty members, chaired by the student’s research (and presumptive thesis) advisor or coadvisors. The oral area exam is meant to establish the student mastery of a well-defined area of scholarship and preparedness to pursue original research in that area. The oral area examination may require completion of a survey paper or completion of a pilot project ahead of the examination. The scope as well as any additional requirements needed for the examination should be developed in consultation with and approval of the research advisor(s), at least one term prior to the exam.

Dissertation and Final Oral Examination

Candidates shall demonstrate their abilities for independent study in a dissertation representing original research or creative scholarship. A prospectus for the dissertation must be successfully defended no later than the end of the eighth term (fourth year) of study.

Candidates must undergo a final oral examination no later than the end of the 10th term (fifth year) of study in which they defend their dissertation as a valuable contribution to knowledge in their field and demonstrate a mastery of their field of specialization in relation to their dissertation.

Both the prospectus and final dissertation must be administered by a dissertation committee of at least three readers (including the dissertation advisor or coadvisors) and chaired by a CDS faculty member who is not one of the readers.

Related Bulletin Pages

• Abbreviations and Symbols

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• Faculty of Computing & Data Sciences
• Data Science for Good
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• BS in Data Science
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• MS in Data Science
• MS in Data Science (Online)
• PhD in Computing & Data Sciences
• Minor in Data Science
• BS in Data Science/MS in Bioinformatics
• MS in Bioinformatics
• PhD in Bioinformatics

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Note that this information may change at any time. Read the full terms of use .

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Boston University is accredited by the New England Commission of Higher Education (NECHE).

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Home / Data Science Programs / PhD in Data Science

Data Science PhD Programs

If you’re passionate about big data and interested in an advanced degree, you may be wondering which degree is right for you. Should you go with a Master of Science (M.S.) or a PhD in data science?

Our guide to getting a PhD in data science is here to help. Here, we’ll break down potential pros and cons of choosing either option, related job opportunities, dissertation topics, courses, costs and more.

SPONSORED SCHOOLS

Syracuse university, master of science in applied data science.

Syracuse University’s online Master of Science in Data Science can be completed in as few as 18 months.

• Complete in as little as 18 months
• No GRE scores required to apply

Southern Methodist University

Master of science in data science.

Earn your MS in Data Science at SMU, where you can specialize in Machine Learning or Business Analytics, and complete in as few as 20 months.

• No GRE required.
• Complete in as little as 20 months.

University of California, Berkeley

Master of information and data science.

Earn your Master’s in Data Science online from UC Berkeley in as few as 12 months.

• Complete in as few as 12 months
• No GRE required

info SPONSORED

Just want the schools? Skip ahead to our  complete list of data-related PhD programs .

Why Earn a PhD in Data Science?

A PhD in Data Science is a research degree designed to equip you with knowledge of statistics, programming, data analysis and subjects relevant to your area of interest (e.g. machine learning, artificial intelligence, etc.).

The keyword here is  research . Throughout the course of your studies, you’ll likely:

• Conduct your own experiments in a specific field.
• Focus on theory—both pure and applied—to discover why certain methodologies are used.
• Examine tools and technologies to determine how they’re built.

PhD Benefits vs. Downsides

There are a number of benefits and downsides to earning a PhD in data science. Let’s explore some of them below.

Benefits of a PhD in Data Science

In a PhD in data science program, you may have the opportunity to:

• Research an area in data science that may potentially change the industry, have unexpected applications or help solve a long-standing problem.
• Collaborate with academic advisors in data science institutes and centers.
• Become a critical thinker—knowing when, where and why to apply theoretical concepts.
• Specialize in an upcoming field (e.g.  biomedical informatics ).
• Gain access to real-world data sets through university partnerships.
• Work with cutting-edge technologies and systems.
• Automatically earn a master’s degree on your way to completing a PhD.
• Qualify for high-level executive or leadership positions.

Downsides of a PhD in Data Science

On the other hand, some PhDs in data science programs may:

• Take four to five years on a full-time schedule to complete. These are years you could be earning money and learning real-world skills.
• Be expensive if you don’t find or have a way to fund it.
• Entail many solitary hours spent reading and writing
• Not give you “on-the-job” knowledge of corporate problems and demands.

Is a PhD in Data Science Worth It?

A PhD in data science may open the door to a number of career opportunities which align with your personal interests. These include, but aren’t limited to:

• Data scientist.   Data scientists  leverage large amounts of technical information to observe repeatable patterns which organizations can strategically leverage.
• Applications architect.  When you work as an applications architect, your main goal is to design key business applications.
• Infrastructure architect.  Unlike an applications architect, infrastructure architects monitor the functionality of business systems to support new technological developments.
• Data engineer.   Data engineers  perform operations on large amounts of data at once for business purposes, while also building pipelines for data connectivity at the organizational level.
• Statisticians :  Statisticians  analyze and interpret data to identify recurring trends and data relationships which can be used to help inform key business decisions.

At the end of a day, whether a data science PhD is worth it will be entirely dependent upon your personal interests and career goals.

Do You Need a PhD to Land a Job?

In most cases, you don’t need a PhD in data science to land a job. Most  computer and information research-related careers  require a master’s degree, such as an  online master’s in data science .

As you begin your search, pay attention to prospective employers and qualifications for your desired position:

• Companies and labs that specialize in data science—and tech players like  Amazon  and  Facebook  — may have a reason for specifying a PhD in the education requirements.
• Other industries may be happy with a B.S. or M.S. degree and relevant work experience.

Careers for Data Science PhD Holders

People who hold a PhD in data science typically find careers in academia, industry and university research labs,  government  and tech companies. These places are most likely seeking job candidates who can:

• Research and develop new methodologies.
• Build core products, tools and technologies that are based on data science (e.g.  machine learning  or  artificial intelligence  algorithms for Google or the next generation of  big data management systems ).
• Reinvent existing methods and tools for specific purposes.
• Translate research findings and adopt theory to practice (e.g. evaluating the latest discoveries and finding ways to implement them in the corporate world).
• Design research projects for teams of statisticians and data scientists.

Sample job titles include:

• Director of Research
• Senior Data Scientist/Analyst
• Data/Analytics Manager
• Data Science Consultant
• Laboratory Researcher
• Strategic Innovation Manager
• Tenured Professor of Data Science
• Chief Data Officer (CDO)

PhD in Data Science Curriculum

Typical Program Structure Data science PhDs are similar to most doctoral programs. That means you’ll typically have to:

• Complete at least two years of full-time coursework.
• Pass a comprehensive exam—comprising oral and written portions—that shows you have mastered the subject matter.
• Submit a dissertation proposal and have it approved.
• Devote 2-3 years to conducting independent research and writing your dissertation. You may be teaching undergraduate classes at the same time.
• Defend your work in a “dissertation defense”—usually an oral presentation to academics and the public.

During these years, you’ll likely engage in professional activities that may help improve your career prospects. Such opportunities include attending and speaking at conferences, applying for summer fellowships, consulting, paid part-time research and more.

Dissertation

PhD students are expected to make a creative contribution to the field of data science—that means you’re encouraged not to go over old ground or rehash what’s already out there. Your contribution will be summed up in your dissertation, which is a written record of your original research.

Some students go into a PhD program already knowing what they want to research. Others use the first couple of years to explore the field and settle on a dissertation topic. Your advisor may be your closest ally in this process.

Data Science vs. Business Analytics vs. Specialties

Doctoral programs in data science may also fall under the related disciplines such as statistics,  computational sciences  and informatics. It is important to evaluate each program’s curriculum. Will the foundation courses and electives prepare you for the research area that you want to explore?

A related degree you may consider is a PhD in Business Analytics (or Decision/Management Sciences). These degree programs are typically administered through a university’s School of Business, which means the curriculum includes corporate topics like management science,  marketing , customer analytics, supply chains, etc.

Interested in a particular subset of data science? Some universities offer specialty PhD programs. Biostatistics and biomedical/health informatics are two examples, but you’ll also find a number of doctoral programs in machine learning (usually run by the Department of Computer Science) and sub-specialties in fields like artificial intelligence and data mining.

Considerations When Choosing a PhD Program

Typical Admissions Requirements PhD candidates typically submit an application form and pay a fee. Universities often look for applicants who have:

• A  Bachelor of Science (BS) in computer science , statistics or a relevant discipline (e.g. engineering) and a similar master’s degree with an official transcript from an accredited institution
• A GPA of 3.0 or higher on a 4.0 scale
• GRE test scores
• TOEFL or IELTS for applicants whose native language is not English
• Letters of recommendation
• Statement of purpose/intent
• Résumé or CV

If you don’t already have certain skills (e.g. stats, calculus, computer programming, etc.), the university may ask you to complete prerequisite courses.

Programs for PhD in Data Science – Online vs. On-Campus Online programs may require you to attend a few campus events (e.g. symposiums), but allow you to complete coursework and conduct research in your own hometown.

While online learning can be a convenient way of obtaining your PhD from the comfort of home, there are a few important factors to consider.

• Are you  extremely  passionate about an area of research?
• Do you mind committing to 4-5 years of study?
• Does your university have funding sources (private and government) for data science research?
• Will you have access to exciting data resources, labs and industry partners?
• Do you know how you’re going to pay for the program?

How Much Does a PhD Cost?

As you research PhD in data science programs, you’ll probably find information on relevant fellowships on some university websites, as well as advice on financial matters. Here are a few ways that you may be able to fund your education:

• PhD Fellowships:  You’ll find a number of fellowships sponsored by the university, by companies and by the government (e.g. National Science Foundation). Be aware that some external fellowships will only cover the years of your dissertation research.
• Teaching/Research Assistantships:  Assistantships are a common way for universities to support PhD students. In return for teaching undergraduates or working as a researcher, you’ll often receive a break on tuition costs and a living stipend.
• In-State Tuition : Public universities may offer in-state students a much lower cost per credit.
• Regional Discounts:  Many state universities have agreements to offer reduced tuition costs to students from neighboring states (e.g.  New England Board of Higher Education Regional Student Program (RSP) . Check to see if this applies to your PhD.
• Travel Grants:  Doctoral students may have the opportunity to attend research conferences and network with future collaborators. Some grants are designed with this purpose in mind.
• Student Loans:  In addition to grants, you can consider applying for student loans to finance your PhD studies. Remember, a doctorate is a long-term commitment—you may not see a financial return on your education for a number of years.

Some PhD students in data science are  fully funded . For example:

• U.S. citizens and permanent residents in  Stanford’s PhD in Biomedical Informatics  are funded by a National Library of Medicine (NLM) Training Grant and Big Data to Knowledge (BD2K) Training Grants

If you’re coming from overseas, try talking to your school about any differences between funding for citizens and international students.

How Long Does a PhD in Data Science Take?

The length of time it takes to obtain a PhD will likely vary depending on your chosen program. Programs for similar or identical degrees can have differing completion requirements at different schools, meaning how many years your PhD program takes will differ as well.

Of course, the amount of time you spend working toward a PhD in data science can also vary depending on whether you choose to take it part-time or full-time. Assuming you consistently pass your classes, a full-time commitment to your PhD program will expedite your way through it.

But a commitment like that won’t fit everyone’s lifestyles. For example, you might need to work to support yourself financially, or you might be raising a family. These sorts of important commitments are time-consuming and can take a lot of energy. So, in that case, a part-time commitment to your PhD program might make more sense for you.

Interested in STEM Careers? 

If you’re looking for information on  career paths that involve STEM , see our guides below:

Data Science and Analytics Careers:

• Data Scientist
• Data Analyst
• Business Analyst

Computer Science, Computer Engineering and Information Careers:

• Computer and Information Research Scientist

Marketing and User Research Careers:

• UX Designer  

Compare Careers and STEM Fields:

• Cybersecurity vs. Computer Science

Related Graduate STEM Degrees

• Master’s in Business Analytics
• Master’s in Information Systems
• Master’s in Computer Engineering
• Master’s in Computer Science  
• Master’s in Cybersecurity Programs
• Master’s Applied Statistics
• Master’s in Data Analytics for Public Policy
• Data Science MBA Programs
• Master’s in Geospatial Science and
• Geographic Information Systems
• Master’s in Health Informatics
• Master of Library and Information Science

Related Undergraduate STEM Degrees

• Online Bachelor’s in Data Science
• Sponsored:  Computer Science at Simmons

PhD in Data Science School Listings

We found 57 universities offering doctorate-level programs in data science. If you represent a university and would like to contact us about editing any of our listings or adding new programs, please send an email to [email protected].

Last updated August 2021. The program’s website is always best for most up to date program information.

PhD in Data Science/Analytics Online

Looking for on-campus programs? See the  full list of on-campus PhD in Data Science/Analytics programs .

Colorado Technical University

Doctor of computer science – big data analytics, colorado springs, colorado.

Name of Degree: Doctor of Computer Science – Big Data Analytics

Enrollment Type: Self-paced

Length of Program: 4 years

Credits: 100

Admission Requirements:

Carnegie Mellon University

School of computer science, ph.d. program in machine learning, pittsburgh, pennsylvania.

Name of Degree: Ph.D. Program in Machine Learning

Enrollment Type: N/A

Length of Program: 2 years

Credits: N/A

• Recent transcripts
• Statement of purpose
• Three letters of recommendation
• TOEFL scores if your native language is not English

Chapman University

Schmid college, ph.d. in computational and data sciences, orange, california.

Name of Degree: Ph.D. in Computational and Data Sciences

Enrollment Type: Full-Time and Part-Time

Credits: 70

• GRE required
• Statement of intent 
• Resume or curriculum CV.                                       
• TOEFL score for international students

Indiana University – Indianapolis

School of informatics and computing, ph.d. in data science, indianapolis, indiana.

Name of Degree: Ph.D. in Data Science

Credits: 90

• Bachelor’s degree; master’s preferred
• Transcripts
• TOEFL or IELTS

Kennesaw State University

School of data science analytics, doctoral degree in analytics and data science, kennesaw, georgia.

Name of Degree: Doctoral Degree in Analytics and Data Science

Enrollment Type: Full-Time

Credits: 78

• Statement of how this degree facilitates your career goals

PhD in Data Science/Analytics On-Campus

Looking for online programs? See the  full list of online PhD in Data Science/Analytics programs .

New York University

Center for data science, new york , new york.

Credits: 72

• Resume or curriculum CV
• TOEFL or IELTS (TOEFL Preferred)
• Statement of Academic purpose

Institute for Computational and Data Sciences

Phd computational and data enabled science and engineering, buffalo, new york.

Name of Degree: PhD Computational and Data Enabled Science and Engineering

Computational Data Sciences  

• Master’s degree
• Resume or CV
• GRE scores (Temporarily suspended)

University of Maryland

College of information studies, doctor of philosophy in information studies, college park, maryland.

Name of Degree: Doctor of Philosophy in Information Studies

Credits: 60

• Transcripts 
• Resume or CV or CV
• academic writing sample
• TOEFL/IELTS/PTE (required for most international applicants)

University of Massachusetts in Boston

College of management, doctor of philosophy in information systemaster of science for data science and management, boston, massachusetts.

Name of Degree: Doctor of Philosophy in Information SysteMaster of Science for Data Science and Management

Credits: 42

• Official transcripts official
• GMAT or GRE scores scores
• Official TOEFL or IELTS score.

University of Nevada – Reno

College of science, ph.d. in statistics and data science, reno, nevada.

Name of Degree: Ph.D. in Statistics and Data Science

Length of Program: 4+ years

• Undergraduate/Graduate Transcripts
• TOEFL/IELTS (only required for international students)

University of Southern California

School of business, ph.d. in data sciences & operations, los angeles, california.

Name of Degree: Ph.D. in Data Sciences & Operations

• Undergraduate/Graduate Transcripts 
• GRE or GMAT
• (3) letters of recommendation
• Passport Copy

University of Washington

Mechanical engineering, doctor of philosophy in mechanical engineering: data science, seattle, washington.

Name of Degree: Doctor of Philosophy in Mechanical Engineering: Data Science

Worcester Polytechnic Institute

Worcester, massachusetts.

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PhD in Data Science and Analytics

Degrees & Programs

• Doctoral Degree in Data Science and Analytics
• Certificates

We launched the first formal PhD program in Data Science in 2015.  Our program sits at the intersection ofcomputer science, statistics, mathematics, and business.  Our students engage in relevant research with faculty from across our eleven colleges.  As one of the institutions on the forefront of the development of data science as an academic discipline, we are committed to developing the next generation of Data Science leaders, researchers, and educators. Culturally, we are committed to the discipline of Data Science, through ethical practices, attention to fairness, to a diverse student body, to academic excellence, and research which makes positive contributions to our local, regional, and global community.   

Herman Ray , Director, Ph.D. in Data Science and Analytics

About the Doctoral Degree in Data Science and Analytics

This degree will train individuals to translate and facilitate new innovative research, structured and unstructured, complex data into information to improve decision making. This curriculum includes heavy emphasis on programming, data mining, statistical modeling, and the mathematical foundations to support these concepts. Importantly, the program also emphasizes communication skills – both oral and written – as well as application and tying results to business and research problems.

Because this degree is a Ph.D., it creates flexibility. Graduates can either pursue a position in the private or public sector as a "practicing" Data Scientist – where continued demand is expected to greatly outpace the supply - or pursue a position within academia, where they would be uniquely qualified to teach these skills to the next generation.

Information Sessions for Fall 2025 Admission

To be announced

Data Science and Analytics PhD Curriculum

Stage One: Pre-Program Requirements

• Successful applicants will have completed a masters degree in a computational field (e.g., engineering, computer science, statistics, economics, finance, etc.)
• Applicants are expected to have deep proficiency in at least one analytical programming language (e.g., SAS, R, Python). SQL and Java are helpful but not required.
• Interested applicants who have earned an undergraduate degree are encouraged to apply to the Ph.D. Program with the embedded MS in Computer Science or with the MS in Applied Statistics.

Stage Two: Coursework

The Ph.D. in Data Science and Analytics requires 78 total credit hours spread over four years of study. Example Program of Study: 

• CS 8265  - Big Data Analytics
• CS 8267  - Machine Learning
• MATH 8010  - Theory of Linear Models (optional)
• MATH 8020  - Graph Theory
• MATH 8030  - Applied Discrete and Combinatorial Mathematics 
• STAT 8240  - Data Mining I
• STAT 8250  - Data Mining II
• Comprehensive Exam 
• 21 credit hours of electives/concentration

Students take up to 9 credit hours of 6000- or 7000-level courses in DS, STAT, or CS with permission of the program director. Students take any 8000- or 9000-level course in DS, STAT, MATH, CS or IT, or the HHS courses in the mHealth concentration.

• at least 15 credit hours in CS courses at 8000 or 9000 levels (except CS 9900)
• at least 15 credit hours in STAT courses at 8000 or 9000 levels
• HHS 8000 - Introduction to mHealth
• HHS 8010 - Ethical Issues in mHealth, Healthcare and Human Subjects Research
• STAT 8235 - Advanced Longitudinal Data Analysis
• HHS 8050 - Advanced Research in mHealth
• HHS 8020 - mHealth Applications or HHS 8030 - Advanced Special Topics in mHealth
• Develop Dissertation Research Proposal 
• DS 9700 Doctoral Internship/Research Lab
• DS 9900 Dissertation
• Dissertation Proposal Defense
• DS 9900 DissertationFinal Dissertation Defense

Stage Three: Project Engagement and Research/Dissertation

Relevant, interdisciplinary research forms the foundation of the Ph.D. in Data Science and Analytics. While students are encouraged to engage in research from their first semester, the last two years of the program are structured to help students transition into becoming independent, lead researchers. In this last stage of the program, students will work with research faculty, including their advisor, in one of our data science research labs.

Program Student Learning Outcomes

At the end of the program, students will be able to:

• Demonstrate their understanding of the research process
• Demonstrate mastery of core concepts relevant to three key areas in mathematics, statistics and computer science
• Develop themselves as professionals prepared for work as a doctoral-educated individual beyond graduation

Admission Requirements and Application

Frequently Asked Questions (FAQ)

How long will the program take?

How much does the program cost?

Who would be successful in the program?

Where do these graduates work after graduation?

What are the publication/research requirements?

What did Science Doctoral Students Study?

• Applied Computer Science
• Applied Economics and Statistics
• Applied Statistics
• Applied Mathematics
• Bioinformatics
• Business Analytics
• Chemical Biology
• Computer Science
• Data Science
• Forecasting & Strategic Management
• Integrative Biology
• Public Admin in Economic Policy Mgmt
• Mathematics
• Mechanical Engineering
• Software Engineering

What is the Project Engagement requirement?

Can I pursue the program part- time while I am working full-time?

Can I live on campus?

Are the courses online?

Do I have to have a masters degree to apply?

Where did Data Doctoral Students Study?

• Ajou University, South Korea
• Albert-Ludwigs University of Freiburg
• Auburn University
• Bowling Green State University
• Clemson University
• Columbia University
• Columbus State University
• Florida State University
• Georgia Southern University
• Georgia State
• Georgia Tech
• Iran University of Science and Technology
• Kennesaw State University
• Marshall University
• Michigan State University
• Murray State University
• North Carolina State University
• St. Petersburg State University, Russia
• University of KwaZulu-Natal, South Africa
• University of Michigan
• University of North Carolina
• University of Toledo

Ph.D. in Data Science and Analytics Student Cohorts

2024 - 2025

Charles Fanning

Bachelor's Degree: Mathematics, Lewis University

Master's Degree: Data Science from Lewis University

Professional Objective: to make meaningful contributions to a wide breadth of interdisciplinary fields through the medium of data science, both in academia and industry. Right now, I am interested in the applications of deep learning on medical imaging data across various modalities and in topological data analysis.

Sharanya Dv

Bachelor's Degree: Physics, Computer Science and Mathematics, St. Aloysius College, Mangalore, India

Master's Degree: Data Science, VIT-AP University, Amaravati, Andhra Pradesh, India

Work History: Data Modernization Team Intern, Google Cloud Partner, Niveus Solutions Pvt. Ltd.

Professional Objective: My goal is to contribute effectively to data-driven decision-making processes and to continuously develop my expertise in the ever-evolving field of data science.

Mohsin Md Abdul Karim

Bachelor's Degree: Mathematics, Jahangirnagar University, Dhaka, Bangladesh

Master's Degrees: Mathematics, Jahangirnagar University, Dhaka, Bangladesh;  Mathematics, Eastern Illinois University; Mathematics, University of Louisiana at Lafayette

Work History:  Export Officer, Jamuna Bank Ltd., Dhaka, Bangladesh; Business Intelligence Team, Nagad Ltd., Dhaka, Bangladesh

Professional Objective: Create a distinct value for myself in an organization so that I can be treated as an asset to them.

Faruk Muritala

Bachelor's Degree: Mathematics, Federal University of Technology, Minna, Niger State Nigeria

Master's Degrees: Mathematics, Kwara State University, Malete, Nigeria; Data Science and Analytics, Kennesaw State University

Work History: 

• Graduate Research Assistant and Teacher of Record, Kennesaw State University
• Mathematics Instructor, Al-Ihsan Group of School, Offa Kwara Nigeria
• Data Entry Officer, National Examination Council (NECo), Niger State Nigeria

Courses Taught:  Introduction to Data Science

Publications:  

• Muritala, F., Olayiwola, R.O., Oyedeji, A.A., Akande, S.O. (2021). “ Mathematical Modeling of Heat Transfer in Micro-channels ”. Journal of Science, Technology, Mathematics, and Education (JOSTMED), 17(3), September 2021.
• Muritala, F. (2022). K-Step Block Hybrid Method for Numerical Solution of Fourth Order Ordinary Differential Equations (Accessed ProQuest). Kwara State University, Malete, Nigeria.
• Muritala, F., Kolawole, M.K., Oyedeji A.A., Lawal, J.O., Alaje A.I., “ Development of an Order (K+3) Block-Hybrid Linear Multistep Method for Direct Solution of General Second Order Initial Value Problems ”, UNIOSUN Journal of Engineering and Environmental Sciences. Vol. 4 No. 2. Sept. 2022. DOI: 10.36108/ujees/2202.40.0230.
• Muritala F., Jimoh A.K., Ogunniran M.O., Oyedeji A.A., Lawal J.O., “ Coherent Hybrid Block Method for Approximating Fourth-Order Ordinary Differential Equation”, Journal of Amasya University the Institute of Sciences and Technology 2023, DOI: 10.54559/jauist.1262994.
• Lawal J., Zhiri A., Muritala F., Ibrahim R., Lukonde A., “Mathematical Modeling for Mycobacterium Tuberculosis” Journal of Balkan Science and Technology 2024. DOI: 10.55848/jbst.2024.41
• Austin. R.B., Muritala F., “A Nonparametric Process Capability Index for Multiple Stream Processes”. 2024 Joint Statistical Meeting Conference Paper, August 2024, Portland, Oregon. 

Award: J. Stephen and Jennifer Lewis Priestley Doctoral Endowed Scholarship, Kennesaw State University. August 2024.

Professional Objective:  to utilize my mathematics, data science, and educational skills to become a leading data scientist and researcher and make a positive impact. I am open to exploring opportunities in academia and industry and eager to learn, relearn, and grow in a dynamic research environment.

Joseph Richardson

Bachelor's Degree:  Actuarial Science (Statistics), University of West GA

Master's Degree: Analytics, Georgia Tech

Professional Objective: I aim to teach and conduct research at an academic institution while also consulting privately.

David Stabler

Bachelor's Degree:  Computer Science, Southern Polytechnic State University

Master's Degree:  Computer Science, Southern Polytechnic State University/Kennesaw State University

Work History: Four decades of IT, currently in the Research Division of the Federal Reserve Bank of Atlanta 

Professional Objective: prepare to be a more effective professor

Benjamin Watson

Bachelor's Degree: Mathematics, Morehouse College

Master's Degrees: Mathematics Education, Georgia State University; Data Science and Analytics, Kennesaw State University (in progress)

• Limited Term Instructor, Kennesaw State University
• Reporting Business Analyst, Northeast Georgia Health System
• Virtual Mathematics and Computer Science Instructor, Imagine Learning
• Mathematics Instructor, Dekalb County School District

Professional Objective: I am seeking to apply combinatorial data analysis to drive innovation in healthcare through patient subtyping, drug discovery, and generative AI. I am also committed to advancing data science research and contributing to academic instruction.

Qiuyuan Zhang

Bachelor's Degree:  Electronic Information Engineering, Xidian University, China

Master's Degree:  Data Science, Georgia State University

Work History:  Graduate Research Assistant, Georgia State University

Professional Objective:  to evolve into a research-oriented professional contributing significantly to academia or industry

Venkata Abhiram Chitty

Bachelor's Degree:   Mathematics, Statistics and Computer Science, Osmania University, Telangana, India

Master's Degree:   Data Science, VIT-AP University, Amaravati, Andhra Pradesh, India

Professional Objective:   To apply my Data Science skills in public health domain and help the society

Caleb Greski

Bachelor's Degree: 

Master's Degree: 

Courses Taught: 

Publications: 

Professional Objective: 

Bachelor's Degree:   Civil Engineering, Huazhong University of Science and Technology, China

Master's Degree:   Business Analytics, Syracuse University

Work History:  

• Credit Modeling Analyst, Agricultural Development Bank of China
• Research Assistant, Changjiang Securities
• Graduate Assistant, Syracuse University

Courses Taught:  Calculus I, Marketing Analytics, Data Mining

Awards:   Merit-Based Scholarship, Syracuse University

Professional Objective:   To secure a challenging position in a reputable organization to expand myself within the field of Artificial Intelligence.

Kausar Perveen

Bachelor's Degree:   Bachelor in Engineering Software Engineering, National University of Sciences and Technology, Pakistan

Master's Degree:   Masters in Data Science, Illinois Institute of Technology, Chicago

• Fullstack Developer at ItRunsInMyFamily, Charleston, South Carolina
• Software Engineer II , Xgrid Pakistan
• Senior Research Coordinator, Aga Khan University Pakistan
• Machine Learning Engineer, Agoda Thailand

Publications:  National cervical cancer burden estimation through systematic review and analysis of publicly available data in Pakistan 

Service and Awards:

• Fulbright Scholarship award for Master’s degree in Data Science
• Aga Khan Education Service Pakistan, merit cumulative need based scholarship for Bachelors in Software Engineering 

Professional Objective:  My main motivation behind getting a degree in Data Science is to receive and perform qualified research experience in Data Science and public health

Bachelor's Degree:   Statistics, University of Dhaka, Dhaka, Bangladesh

Master's Degree:   Mathematics (Statistics Concentration), University of Toledo, Ohio

• Analytics Engineer Intern, Cooper Smith, Toledo, Ohio
• Business AnalystAkij Food and Beverage Limited, Dhaka, Bangladesh

Courses Taught:   Introduction to Statistics

Professional Objective:   I am interested to work as a data scientist in the industry

Ayomide Isaac Afolabi

Bachelor's Degree:  Chemical Engineering, Ladoke Akintola University of Technology 

Master's Degree:  Data Science, Auburn University 

Work History:   Graduate Research Assistant, Auburn University 

Courses Taught:   Python Programming 

Publications:   Larson EA, Afolabi A, Zheng J, Ojeda AS. Sterols and sterol ratios to trace fecal contamination: pitfalls and potential solutions. Environ Sci Pollut Res Int. 2022 Jul;29(35):53395-53402.  doi: 10.1007/s11356-022-19611-2 . Epub 2022 Mar 14. PMID: 35287190

Professional Objective:  To work as a research data scientist in the industry

Dinesh Chowdary Attota

Bachelor's Degree:   Computer Science, Jawaharlal Nehru Technological University Kakinada (JNTUK), India

Master's Degree:   Computer Science, Kennesaw State University

Work History:   Associate Consultant, SL Techknow Solutions India Pvt Ltd, India  2018 - 2020

• An Ensemble Multi-View Federated Learning Intrusion Detection for IoT
• A Conversational Recommender System for Exploring Pedagogical Design Patterns
• An Ensembled Method For Diabetic Retinopathy Classification using Transfer Learning  

Professional Objective:   I'd like to be a faculty member at a university so that I can continue to do research.

Nzubechukwu Ohalete

Bachelor's Degree:   Mathematics,University of Nigeria, Nsukka

Master's Degree:   Applied Statistics, Bowling Green State University

Work History:   Graduate Assistant/Data Analyst, Federal University of Technology, Owerri - Mathematics Department

Courses Taught:  Elementary Mathematics, Mathematical Methods

Awards:   James A. Sullivan Outstanding Graduate Student Award, Applied Statistics and Operations Research Department, April 2022

Professional Objective:   To use data science techniques to solve problems which makes our lives better and also makes our world a better place

Ryan Parker

Bachelor's Degree:  Microbiology, University of Tennessee - Knoxville

Master's Degree:   Integrative Biology, Kennesaw State University

Work History:  Instructor of Biology, Kennesaw State University

Courses Taught:   Nursing Microbiology Lectures and Labs, Introductory Biology Labs, Biotechnology Lectures and Labs

• Parker RA, Gabriel KT, Graham K, Cornelison CT. Validation of methylene blue viability staining with the emerging pathogen Candida auris. J Microbiol Methods. 2020 Feb;169:105829.   doi: 10.1016/j.mimet.2019.105829 . Epub 2019 Dec 27. PMID: 31884053.
• Parker RA, Gabriel KT, Graham KD, Butts BK, Cornelison CT. Antifungal Activity of Select Essential Oils against Candida auris and Their Interactions with Antifungal Drugs. Pathogens. 2022 Jul 22;11(8):821.   doi: 10.3390/pathogens11080821 . PMID: 35894044; PMCID: PMC9331469.

Awards:   Best Graduate Poster: Symposium for Student Scholars hosted by Kennesaw State University (Fall 2018) for Poster: "Antifungal Activity of Select Essential Oils and Synergism with Antifungal Drugs against Candida auris"

Professional Objective : To apply Data Science techniques to large scientific datasets, such as genomic and astronomical data, and to help bridge the gap between disparate fields by working in an interdisciplinary space to offer integrative and data-driven solutions to the increasingly complex problems presented to the traditional Sciences.

Askhat Yktybaev

Bachelor's Degree:   Forecasting and Strategic Management, Saint-Petersburg State University of Economics and Finance, Russia

Master's Degree:   Forecasting and Strategic Management, Saint-Petersburg State University of Economics and Finance, Russia; Public Administration in Economic Policy Management, School of International and Public Affairs, Columbia University

Work History:

• from Data Analyst to Head of Research Unit, Central Bank of Kyrgyz Republic
• Sr. Data Scientist in OJSC, Aiyl Bank, Kyrgyzstan
• Consultant, The World Bank, Washington D.C.

Courses Taught:   Financial Programing in the Central Bank, Monetary Policy Transmission Mechanism

Service and Awards:   Winner of the Joint Japan/World Bank Graduate Scholarship Program, National Bank Silver Medal for Best Forecast

Professional Objective:   I want to found a successful Fintech startup one day.

Sanad Biswas

Bachelor's Degree:   Statistics, Biostatistics and Informatics, University of Dhaka, Bangladesh

Master's Degree:   Statistics, University of Toledo, OH

• Research Assistant: US Army Research Lab, Kennesaw State University
• Consultant, Statistical Consulting Service, University of Toledo
• Graduate Teaching Assistant, University of Toledo

Courses Taught:   Calculus and Business Calculus, Facilitated students’ study of Statistics courses at the University of Toledo.

Professional Objective:   To work as a researcher in the industry or as a faculty. I am primarily interested in the application of machine learning in different fields.

Mallika Boyapati

Bachelor's Degree:  Electronics and Computer Engineering, K L University, India

Master's Degree:  Applied Computer Science, Columbus State University

• T-Mobile, Seattle, WA, USA: Sr. Data analyst, 2018- 2021
• UITS, Columbus State University, Columbus, GA, USA: Data Analyst -Graduate assistant, 2016-2018
• Menlo Technologies, India: Jr. Data Analyst, Intern, 2014- 2016

Courses Taught:   DATA 4310 - Statistical Data Mining

Publications:

• Anti-Phishing Approaches in the Era of the Internet of Things. In: Pathan, AS.K. (eds) Towards a Wireless Connected World: Achievements and New Technologies. Springer, Cham -   https://doi.org/10.1007/978-3-031-04321-5_3
• An empirical analysis of image augmentation against model inversion attack in federated learning -   https://doi.org/10.1007/s10586-022-03596-1
• M. Boyapati and R. Aygun, "Phishing Web Page Detection using Web Scraping," SoutheastCon 2023, Orlando, FL, USA, 2023, pp. 167-174, doi: 10.1109/SoutheastCon51012.2023.10115148.
• M. Boyapati and R. Aygun, "Default Prediction on Commercial Credit Big Data Using Graph-based Variable Clustering," 2023 IEEE 17th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 2023, pp. 139-142, doi: 10.1109/ICSC56153.2023.00029.
• Boyapati, M., Aygun, R. (2023) Explainable Machine Learning for Default Prediction on Commercial Credit Big Data Using Graph-based Variable Clustering. In Encyclopedia with Semantic Computing and Robotic Intelligence VOL. 0 https://doi.org/10.1142/S2529737623500119
• Winners of Dataiku March Madness Bracket-thon, 2021 in predicting the NBA bracket
• Winners of 2021 Analytics Day Ph.D. level research poster presentation 

Professional Objective:   To leverage strong analytical and technical abilities to research and develop effective data models, visualize data, and uncover insights that makes an impact in field of data science

Nina Grundlingh

Bachelor's Degree:   Applied Mathematics and Statistics, University of KwaZulu-Natal, South Africa

Master's Degree:   Statistics, University of KwaZulu-Natal, South Africa

Courses Taught:   Introduction to Statistics, University of KwaZulu-Natal

• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling diabetes in South Africa. The 61st conference of the South African Statistical Association, 27-29 November 2019, Nelson Mandela University, South Africa.
• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling diabetes in the South African population. College of Agriculture, Engineering and Science Postgraduate Research & Innovation Symposium 2019, 17 October 2019, University of KwaZulu-Natal, Westville, South Africa (the award for best MSc presentation was also received for this).
• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling risk factors of diabetes and pre-diabetes in South Africa. IBS SUSAN-SSACAB 2019 Conference, 8-11 September 2019, Cape Town, South Africa.
• University of KwaZulu-Natal Postgraduate Research & Innovation Symposium 2019 – Best Masters oral presentation
• South African Statistical Association Honours Project Competition 2018/2019 – 2nd place and special prize for best use of SAS

Professional Objective:   To work in a teaching position – sharing how data science can be applied to different fields and the positive impact it could have. I would like to use my theological background and passion to bring insight, clarity, and wisdom to data science problems. 

Namazbai Ishmakhametov

Bachelor's Degree:   Specialist in Mathematical Methods in Economics, Kyrgyz-Russian Slavic University

Master's Degree:   Analytics, Institute for Advanced Analytics at North Carolina State University

• Expert at the Centre for Economic Research, National bank of the Kyrgyz Republic
• Consultant in World Bank project dedicated to strengthening the regulatory practices in Kyrgyz Republic
• Consultant at Deloitte Consulting LLP, Science Based Services group, Analytics & Cognitive offering
• Macroeconomic modeling expert in the Economic Department, National bank of the Kyrgyz Republic

Courses Taught:   Introductory statistics and econometrics (cross-sections, times series and panels) lecturer at Ata-Turk Alatoo International University, Kyrgyzstan

• Ishmakhametov Namazbai, Abdygulov Tolkunbek, Jenish Nurbek. 2020. “ Impact of 2014-2015 shocks on economic behavior of the households in the Kyrgyz Republic ". Working Paper of the National Bank of the Kyrgyz Republic
• Sherrill W. Hayes, Jennifer L. Priestley, Namazbai Ishmakhametov, Herman E. Ray. 2020. “ I’m not Working from Home, I’m Living at Work ”: Perceived Stress and Work-Related Burnout before and during COVID-19”. PsyArxiv Preprints
• Ishmakhametov Namazbai, Arykov Ruslan. 2016. “ Credit Risk Model on the Example of the Commercial Banks of the Kyrgyz Republic ”. Working Paper of the National Bank of the Kyrgyz Republic
• Namazbai Ishmakhametov, Anvar Muratkhanov.2015. “Modeling strategy of the Bank of the Kyrgyz Republic”. National bank of Poland – Swiss National bank joint seminar. Zurich, Switzerland

Professional Objective:   To apply my quantitative skills in the field of biotech either in corporate or government sector

Symon Kimitei

Bachelor's Degrees:   Mathematics, Kennesaw State University, and Computer Science,  Kennesaw State University

Master's Degree:   Mathematics (Scientific Computing Concentration), Georgia State University 

Work History:   Senior Lecturer and Math Department Coordinator of Supplemental Instruction, Kennesaw State University

Courses Taught:   Calculus 1, Precalculus, Applied Calculus & College Algebra 

• Haskin, S., Kimitei, S., Chowdhury, M., Rahman, F., Longitudinal Predictive Curves of Health-Risk Factors for American Adolescent Girls. Journal of Adolescent Health.  JAH-2021-00601R1
• Symon K Kimitei,   Algorithms for Toeplitz Matrices with Applications to Image Deblurring . 2008. Georgia State University, Masters thesis. ScholarWorks 

Poster Presentations:

• Kimitei, Symon & Sammie Haskin. "Nadaraya-Watson Kernel Regression Longitudinal Analysis of Healthcare Risk Factors of African American and Caucasian American Girls." Kennesaw State University R Day Presentation.  11 Nov. 2019. Poster presentation.
• Kimitei, Symon. " Social Network Analysis in Supreme Court Case Rulings by Precedence Using SAS Optgraph/Python." 23rd Annual Symposium of Scholars. Kennesaw State University.  19 April. 2018. Poster presentation.

Professional Objective:   As a Ph.D. student in Analytics & Data Science, I hope to gain skills in the program that will propel me into a Data Scientist / Machine Learning Engineer with a specialization in the design and implementation of deep learning & machine learning algorithms.

Jitendra Sai Kota

Bachelor's Degree:   Computer Science & Engineering, Amrita Vishwa Vidyapeetham, India

Master's Degree:   Computer Science, Florida State University

Work History:   Teaching Assistant Professor in Computer Science at an Engineering College in India

Courses Taught:   Problem Solving & Program Design through C, Artificial Intelligence, Data Mining

Publications:  Kota, Jitendra Sai, Vayelapelli, Mamatha. 2020. "Predicting the Outcome of a T20 Cricket Game Based on the Players' Abilities to Perform Under Pressure". IEIE Transactions on Smart Processing and Computing 9(3):230-237.   DOI: 10.5573/IEIESPC.2020.9.3.230

Professional Objective:   to work in Data Science in a Corporate Environment

ResearchGate

Catrice Taylor

Bachelor's Degree:   Economics, Clemson University 

Master's Degrees:  Applied Economics and Statistics, Clemson University, and Applied Statistics, Kennesaw State University 

Professional Objective:   To work as an industry data scientist in a corporate environment 

Sahar Yarmohammadtoosky

Bachelor's Degree:   Applied Mathematics, Sheikh Bahaei University, Isfahan, Iran 

Master's Degree:   Applied Mathematics, Iran University of Science & Technology, Tehran, Iran

Courses Taught:  Numerical Analysis and Linear Algebra, Iran University of Science & Technology

Publications:   Noah, G., Sahar, Y., Anthony P. & Hung, C.C. "ISODS: An ISODATA-Based Initial Centroid Algorithm". Accepted to: 10th International Conference on Information, March 6 - 8, 2021, Hosei University, Tokyo, Japan

Professional Objective:   My goal is to become a competent Data Science specialist capable of using my skills to bring meaning to data, getting a faculty position at a university

Martin Brown

Graduation Date: Spring 2024

Dissertation: A Holistic and Collaborative Behavioral Health Detection Framework Using Sensitive Police Narratives

Dissertation Advisors: Dr. Dominic Thomas and Dr. Md Abdullah Al Hafiz Khan

Inchan Hwang

Graduation Date: Summer 2024 

Dissertation: Next-Generation Medical Imaging Dataset Management Leveraging Deep Learning Frameworks in Breast Cancer Screening

Dissertation Advisor: Dr. MinJae Woo

Current Position: Assistant Professor of Cybersecurity, Montreat College

Duleep Prasanna Rathgamage Don

Bachelor's degree:   Physics and Mathematics, The Open University of Sri Lanka

Master's degree:   Mathematics, Georgia Southern University

• Graduate Teaching Assistant, Georgia Southern University, 2016 - 2018
• Graduate Teaching Assistant, University of Wyoming, 2019 - 2020

Courses Taught:   Trigonometry, and Calculus I & II

Publications/Presentations:

• Don, R. D. and Iacob, I. E., ‘DCSVM: Fast Multi-class Classification using Support Vector Machines’,   International Journal of Machine Learning and Cybernetics .
• Rathgamage Don, D., Iacob, E., ‘Divide and Conquer Support Vector Machine for Multiclass Classification’, Research Symposium (2018), Georgia Southern University.
• Rathgamage Don, D., Iacob, E., ‘Multiclass Classification using Support Vector Machines’, MAA Southeastern Section Meeting (2018), Clemson University.

Professional Objective:   To work in big data analytics, and research and development of machine learning in engineering, and medicine

Linglin Zhang

Graduation Date: Summer 2024

Dissertation: Innovative Approaches for Identifying and Reducing Disparity in Machine Learning Model Performance – Bridging the Gap in Binary Classification for Health Informatics

Current Position: Data and Analytics RDP Associate, Equifax

Yihong Zhang

Bachelor’s Degree:   Psychology Mathematics Interdisciplinary, Chatham University

Master’s Degree:   Mathematics and Statistics Allied with Computer Science, Georgia State University

• Research Assistant - Collaborated with biomedical department to analyze and visualize microarray gene expression data, Facilitated in data pre-processing and machine learning modeling of clinical liver cirrhosis image data, Assisted in feature engineering of image analysis in deep learning for pathology diagnosis with Mayo Clinic’s pilot project.
• Graduate Lab Assistant - Tutored students with statistics and math subjects.

Professional Objective:   Make better use of data in healthcare and bioinformatic industry as a data scientist.

2019 - 2020

Trent Geisler

Graduation Date:   Summer 2022

Dissertation:   Novel Instance-Level Weighted Loss Function for Imbalanced Learning

Dissertation Advisor:   Dr. Herman Ray

Current Position:   Assistant Professor, Department of Systems Engineering, United States Military Academy West Point

Srivatsa Mallapragada

Dissertation: Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap

Dissertation Advisor: Dr. Ying Xie

Current Position: Data Scientist, Rue Gilt Groupe (RGG)

Sudhashree Sayenju

Graduation Date:   Spring 2023

Dissertation:   Quantification and Mitigation of Various Types of Biases in Deep NLP Models

Dissertation Advisor:   Dr. Ramazan Aygun

Current Position: Lecturer, Data Science and Analytics, Kennesaw State University

Christina Stradwick

Bachelor’s Degree:  Music Performance and Mathematics, Marshall University

Master’s Degree:  Mathematics with Emphasis in Statistics, Marshall University

Courses Taught:  Prep for College Algebra at Marshall University

Selected Presentations:

• Stradwick, C. Exploring the Variance of the Sample Variance. Spring Meeting of the Mathematical Association of America Ohio Section, University of Akron, 2019.
• Stradwick, C., Vaughn, L., Hanan Khan, A. Data Modeling on Insurance Beneficiary Dataset. College of Science Research Expo 2018, Marshall University, 2018. Poster Presentation.
• Stradwick, C. Disease modeling on networks. The 13th Annual UNCG Regional Mathematics and Statistics Conference, University of North Carolina at Greensboro, 2017. Poster Presentation.

Professional Objectives:  To work as a researcher in industry or in a laboratory setting. I would like to use my background in mathematics and statistics to develop novel solutions that address limitations in current data science techniques and to apply known data science methods to solve real-world problems.

2018 - 2019

Md Shafiul Alam

Graduation Date:   Fall 2022

Dissertation:   Appley:   App roximate Shap ley   Values for Model Explainability in Linear Time

Dissertation Advisor:   Dr. Ying Xie

Current Position:   AI Framework Engineer, Intel Corporation

Jonathan Boardman

Dissertation:   Ethical Analytics: A Framework for a Practically-Oriented Sub-Discipline of AI Ethics

Current Position:   Data Scientist, Equifax

Tejaswini Mallavarapu

Bachelor’s Degree:   Pharmacy, Acharya Nagarjuna University, India

Master’s Degree:   Computer Science, Kennesaw State University

• Graduate Research Assistant, Kennesaw State University, 2017-present
• Research Analyst, Divis Laboratories, 2013-2014

Selected Publications:

• T. Mallavarapu, Y. Kim, J.H. Oh, and M. Kang, "R-PathCluster: Identifying Cancer Subtype of Glioblastoma Multiforme Using Pathway-Based Restricted Boltzmann Machine," Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2017), International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics, Accepted, 2017.
• M.R. Shivalingam, K.S.G. Arul Kumaran, D. Jeslin, Ch. MadhusudhanaRao, M. Tejaswini, "Design and Evaluation of Binding Properties of Cassia roxburghii Seed Galacto mannan and Moringa oleifera Gum in the Formulation of Paracetamol Tablets," Research Journal of Pharmacy and Technology(RJPT). 3(1): Jan.-Mar. 2010; Page 254-256.
• M.R. Shivalingam, K.S.G. Arul Kumaran, D. Jeslin, Y.V. Kishore Reddy, M. Tejaswini, Ch. MadhusudhanaRao, V. Tejopavan, "Cassia roxburghii Seed Galacto manna— a potential binding agent in the tablet formulation," Journal of Biomedical Science and Research(JBSR), Vol 2 (1), 2010, 18-22

Professional Objective:   To be a data scientist in the field of health care or bioinformatics where I can leverage my analytical skills and knowledge towards the advancement of the research field.

Seema Sangari

Dissertation:   Debiasing Cyber Incidents - Correcting for Reporting Delays and Under-reporting

Dissertation Advisor:   Dr. Michael Whitman

Current Position:   Principal Modeler, HSB 

Srivarna Settisara Janney

Bachelor’s Degree:   Mechanical Engineering, Visveswaraiah Technological University, India

• Graduate Research Assistant, Kennesaw State University, 2016-2018
• Senior Software Engineer, Torry Harris Business Solutions (THBS), United Kingdom, 2010-2012 and India, 2012-2014
• Software Engineer, Torry Harris Business Solutions (THBS), India, 2007-2010

Selected Publications/Presentations:

• S.S. Janney, S. Chakravarty, “New Algorithms for CS – MRI: WTWTS, DWTS, WDWTS”, One-page research paper, 40th International Conference of IEEE Engineering in Medicine and Biology Society (IEEE EMBC), Jul 2018
• Master thesis presented at Southeast Symposium on Contemporary Engineering Topics (SSCET), UAH Engineering Forum, Alabama, Aug 2018
• Master thesis poster is accepted to be presented at Biomedical Engineering Society (BMES) 2018 Annual Meeting, Oct 2018
• Submitted draft copy for book chapter contribution on “Bioelectronics and Medical Devices”, Elsevier Publisher, May 2018
• Showcased 3MT, Georgia Council of Graduate Schools (GCGS), Apr 2018
• Master thesis presented in workshop for “Medical Signal and Image Processing” at Department of Biotechnology & Medical Engineering, NIT Rourkella, Feb 2018
• S.S. Janney, I. Karim, J. Yang, C.C Hung, Y. Wang, “Monitoring and Assessing Traffic Safety Using Live Video Images”, GDOT project showcase, 4th Annual Transportation Research Expo, Sept 2016
• 1st Place Winner, Graduate Research Project, C-day Poster Presentation, Kennesaw State University, Spring 2018
• People's Choice Award, 3 Minute Thesis (3MT), Apr 2018
• CCSE Dean’s 4.0 Club, Jan 2018
• 3rd Place Winner, Hackathon 2017 - HPCC Systems Big Data
• Foundation of Computer Science, Certified by Kennesaw State University, Jun 2016
• Fundamental of RESTful API Design, Certified by APIGEE, Nov 2014
• Member of HandsOnAtlanta, since 2014
• SOA Associate, Certified by IBM, Jun 2008

Professional Objective:   I would like to be a researcher in Data Science and Analytics in medical imaging technologies contributing to advancements that would help medical and healthcare professionals provide value-based and personalized health care. I would like to look at career opportunities in industry and academia that fuel my interest in research.

2017 - 2018

Andrew M. Henshaw

Bachelor’s Degree: Electrical Engineering, Georgia Tech

Master’s Degree: Electrical Engineering, Georgia Tech

Master’s Degree: Business Administration, Georgia State University

• Georgia Tech Research Institute, Sr. Research Engineer, 2001-
• APower Solutions, Vice President, 1999-2001
• Georgia Tech Research Institute, Research Engineer II, 1990-1999
• Georgia Tech, School of Electrical Engineering, Research Engineer I, 1986-1990

Courses Taught: Software-Defined Radio Development with GNU Radio: Theory and Application, Georgia Tech Professional Education

Selected Publications/Presentations: Python Cookbook, Vol 1, 2002, “Sorting Objects Using SQL’s ORDER BY Syntax”

Triangulation Clustering

Lyrical: Complexity Analysis of Pop Song Lyrics

Service and Awards:  International Test and Evaluation Association (ITEA) Atlanta Chapter, President, 1995

Graduation Date: Summer 2021

Dissertation: Incentive-based Data Sharing and Exchanging Mechanism Design

Dissertation Advisor: Dr. Meng Han

Current Position: Assistant Professor, Saint Joseph's University - Erivan K. Haub School of Business

Mohammad Masum

Dissertation: Integrated Machine Learning Approaches to Improve Classification Performance and Feature Extraction Process for EEG Dataset

Dissertation Advisor: Dr. Hossain Shahriar

Current Position: Assistant Professor, San Jose State University

Lauren Staples

Graduation Date: Fall 2021

Dissertation: A Distance-Based Clustering Framework for Categorical Time Series: A Case Study in the Episodes of Care Healthcare Delivery System

Dissertation Advisor: Dr. Joseph DeMaio

Current Position: Senior Data Scientist, Microsoft

2016 - 2017

Shashank Hebbar

Dissertation: Tree-BERT - Advanced Representation Learning for Relation Extraction

Current Position: Data Scientist, Credigy

Jessica Rudd

Graduation Date: Summer 2020

Dissertation: Quantitatively Motivated Model Development Framework: Downstream Analysis Effects of Normalization Strategies

Dissertation Advisor: Dr. Herman Ray

Current Position: Senior Data Engineer, Intuit Mailchimp

Graduation Date: Spring 2020

Dissertation: Data-driven Investment Decisions in P2P Lending: Strategies of Integrating Credit Scoring and Profit Scoring

Dissertation Advisor: Dr. Sherry NI

Current Position: Applied Scientist II, Amazon

Dissertation: A Novel Penalized Log-likelihood Function for Class Imbalance Problem

Current Position: Data Scientist/Research Engineer, Hewlett Packard Enterprise

Dissertation: Attack and Defense in Security Analytics

Dissertation Advisor: Dr. Selena He

Current Position: NLP Data Scientist, NBME

2015 - 2016

Edwin Baidoo

Graduation Date:  Spring 2020

Dissertation: A Credit Analysis of the Unbanked and Underbanked: An Argument for Alternative Data

Dissertation Advisor:  Dr. Stefano Mazzotta

Current Position: Assistant Professor, Business Analytics, Tennessee Technological University

Bogdan Gadidov

Graduation Date:  Summer 2019

Dissertation: One- and Two-Step Estimation of Time Variant Parameters and Nonparametric Quantiles

Dissertation Advisor: Dr. Mohammed Chowdhury

Current Position: Data Scientist, Variant

Dissertation:  Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis

Dissertation Advisor:  Dr. Mingon Kang

Current Position:  Assistant Professor, Chinese Academy of Medical Sciences, Peking Union Medical College

Graduation Date:  Spring 2019

Dissertation:  Deep Embedding Kernel

Current Position: Assistant Professor, Information Technology, Kennesaw State University

Bob Venderheyden

Graduation Date: Fall 2019

Dissertation:  Ordinal Hyperplane Loss

Dissertation Advisor:  Dr. Ying Xie

Current Position:  Principal Data Scientist, Microsoft

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NYU Center for Data Science

Harnessing Data’s Potential for the World

PhD in Data Science

An NRT-sponsored program in Data Science

• Areas & Faculty
• Admission Requirements
• Medical School Track
• NRT FUTURE Program

May I apply for more than one graduate degree at NYU?

That depends on how your multiple applications are split up over the NYU schools. You can apply concurrently to as many NYU schools as you wish. Some schools allow you to apply to multiple programs. However, the PhD in Data Science is part of the Graduate School of Arts and Science, which permits only one application at a time. Please note that you can find the full Graduate School of Arts and Science policy on  the NYU GSAS General Application Policies page .

Can I apply to both MSDS and PhD?

No, both programs are housed within the Graduate School of Arts and Sciences, which permits only one application at a time.

Can I move into the PhD automatically after completing MSDS?

No, you will need to apply to the PhD program.

May I take courses without applying for a degree?

Yes, we do admit non-degree students. For more information on the application process, please see the GSAS Instructions For the Non-Degree and Visiting Student Application for Admission page . There is a limit on the number of such courses you can take as a non-degree student.

I’m currently a student at NYU. How can I apply for the PhD in Data Science?

Students enrolled in any other NYU graduate program must submit a new application. NYU students enrolled in a Courant graduate degree must also submit a new application. Students in a Courant graduate program cannot apply for a transfer.

What are the admission requirements?

See our  PhD Admission Requirements page  for the prerequisite information.

Are international students accepted?

NYU seeks talented students from every corner of the globe.

Are there different requirements for international students?

For more information for international students interested in the PhD in Data Science program, please visit  the NYU Graduate School of Arts and Science website .

Can the test scores be received after the application deadline?

Official test scores should be received by the application deadline.

If I am denied admission, can I apply again?

Yes, you may apply again by submitting a new application and supplemental material. See related GSAS Application Policies here .

I am rejected, can I get a feedback?

Unfortunately, due to a high volume of applications, reviewers cannot provide feedback on why an application was rejected.

Can Data Science students pursue a dual degree?

No, unfortunately, there are no dual data science degrees at this time. However, the data science curriculum allows students to take electives within various departments outside of Data Science.

Applications

Where do i mail official transcripts.

Copies of your official transcripts should be uploaded with your application. If you are accepted, the Graduate School of Arts & Sciences will request a mailed copy of your transcripts. Please note that only electronic submissions will be accepted in the application. See here for more information.

I am having technical problems with the online application system – who should I contact?

[email protected]

What do I do if one of my references can’t use the online recommendation system?

Please review the Graduate School of Arts and Science’s FAQ section for the policies regarding letters of recommendation.

Standardized Testing

What is the minimum cutoff for ielts and toefl.

Many factors are taken into account by the admissions committee. There is no minimum cutoff for standardized tests. However, for the IELTS, there is a recommended minimum band score of 7.0. For the TOEFL, there is a recommended minimum score of 100 on the internet-based test.

Do I have to take GRE?

The GRE is not required for Fall 2023 applicants. We will consider GRE test scores if they are submitted.

Is it possible to waive the TOEFL scores?

The Graduate School requires applicants who are not native English speakers to submit official TOEFL or IELTS score results. The TOEFL/IELTS requirement is waived if your baccalaureate or master’s degree was (or will be) completed at an institution where the language of instruction is English. See here for more information . Please contact [email protected] if you have further questions.

What is the Center for Data Science School Code for the TOEFL?

The school code for the TOEFL is 2596 (New York U Grad Arts Sci).

Does NYU offer financial aid and/or scholarships?

Current and past CDS PhD students admitted to our program have been offered fellowships covering tuition, fees, and health insurance. The fellowship includes a nine-month stipend, which for the 2023-2024 school year, is $39,430.38. These fellowship packages have been five year commitments. In addition to the fellowship, CDS has provided a one-time award for start-up expenses. PhD financial packages are reviewed on a yearly basis for new cohorts. 

Miscellaneous

Can i transfer credits.

As per GSAS policy, admitted students may transfer up to 40 credits into the PhD program pending review and approval by the Director of Graduate Studies. Current students who are considering transfer credits should email Kathryn Angeles at  [email protected] .

Is it possible to do the program part time?

It is not possible to complete the PhD in Data Science program as a part-time student.

Which programming languages will be used in each course?

Programming languages are decided by the professor of each course. However, over the past few years, Python has been widely used.

How many courses should I plan to take each semester?

The suggested full-time workload is three 3-credit courses per semester totaling 9 credits per semester. We do not recommend more than 3 courses per semester, as most courses have both significant mathematical content and programming assignments.

Are there any internships or volunteer opportunities in NYU Data Science?

The NYU Center for Data Science helps to provide robust internship opportunities with business partners in the New York area, including some of the world’s largest companies working in data science, artificial intelligence and machine learning. Many of these companies are within walking distance of the campus.

Are Teaching Assistant (TA) or Research Assistant (RA) positions available for students enrolled in the program?

We do not use the term “Teaching Assistant” but we do sometimes have grading and section leader positions available for qualified students.

Is there an online option?

No, we do not offer the course online.

Ph.D. Specialization in Data Science

The ph.d. specialization in data science is an option within the applied mathematics, computer science, electrical engineering, industrial engineering and operations research, and statistics departments..

Only students already enrolled in one of these doctoral programs at Columbia are eligible to participate in this specialization. Students should fulfill the requirements below in addition to those of their respective department's Ph.D. program. Students should discuss this specialization option with their Ph.D. advisor and their department's director for graduate studies.

Applied Mathematics Doctoral Program

Computer Science Doctoral Program

Decision, Risk, and Operations (DRO) Program

Electrical Engineering Doctoral Program

Industrial Engineering and Operations Research Doctoral Program

Statistics Doctoral Program

The specialization consists of either five (5) courses from the lists below, or four (4) courses plus one (1) additional course approved by the curriculum committee. All courses must be taken for a letter grade and students must pass with a B+ or above. At least three (3) of the courses should come from outside the student’s home department. At least one (1) course has to come from each of the three (3) thematic areas listed below.

Specialization Requirements

• COMS 4231 Analysis of Algorithms I
• COMS 6232 Analysis of Algorithms II
• COMS 4111 Introduction to Databases
• COMS 4113 Distributed Systems Fundamentals
• EECS 6720 Bayesian Models for Machine Learning
• COMS 4771 Machine Learning
• COMS 4772 Advanced Machine Learning
• IEOR E6613 Optimization I
• IEOR E6614 Optimization II
• IEOR E6711 Stochastic Modeling I
• EEOR E6616 Convex Optimization
• STAT 6301 Probability Theory I
• STAT 6201 Theoretical Statistics I
• STAT 6101 Applied Statistics I
• STAT 6104 Computational Statistics
• STAT 5224 Bayesian Statistics
• STCS 6701 Foundations of Graphical Models (joint with Computer Science) 

Information Request Form

Ph.d. specialization committee.

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• Faculty of Arts and Sciences Professor of Statistics
• The Fu Foundation School of Engineering and Applied Science Professor of Computer Science

Richard A. Davis

• Faculty of Arts and Sciences Howard Levene Professor of Statistics

Vineet Goyal

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Industrial Engineering and Operations Research

Garud N. Iyengar

• Data Science Institute Avanessians Director of the Data Science Institute
• The Fu Foundation School of Engineering and Applied Science Professor of Industrial Engineering and Operations Research

Gail Kaiser

Rocco a. servedio, clifford stein.

• The Fu Foundation School of Engineering and Applied Science Wai T. Chang Professor of Industrial Engineering and Operations Research and Professor of Computer Science

John Wright

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Electrical Engineering
• Data Science Institute Associate Director for Research

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Data Science Master's Program Online

Enhance your career as a leader in a data-driven world and get a master’s in data science online—no GRE required. Courses in Computer Science and Applied Mathematics provide a foundation for launching our masters in data science graduates into a variety of specialized careers, including data pipeline and storage and statistical analysis.

• Request Info
• View Info Session

Online Data Science Graduate Program Overview

Johns Hopkins Engineering for Professionals online, part-time Data Science graduate program addresses the huge demand for data scientists qualified to serve as knowledgeable resources in our ever-evolving, data-driven world.

Designed specifically with working professionals in mind, you will engage in a number of modern online courses created to expand your knowledge for advanced career opportunities in data science, including Machine Learning, Data Visualization, Game Theory, and Large-Scale Data Systems. Learn from senior-level engineers and data scientists who will incorporate realistic scenarios in your studies that you have or will encounter as a professional. 

The online master’s degree in data science prepares you to succeed in specialized jobs involving everything from the data pipeline and storage to statistical analysis and eliciting the story the data tells. You will: 

• Gain practical skills and advance your career to meet the growing demand for data scientists.
• Balance both the theory and practice of applied mathematics and computer science to analyze and handle large-scale data sets.
• Manage and manipulate information to discover relationships and insights into complex data sets.
• Create models using formal techniques and methodologies of abstraction that can be automated to solve real-world problems.
• Select the courses that fit your area of interest.
• Become a confident data scientist and leader.

Data Science Degree Options

We offer three program options for Data Science; you can earn a Master of Science in Data Science or a Post-Master’s Certificate.

Data Science Courses

Get details about course requirements, prerequisites, and electives offered within the program. All courses are taught by subject-matter experts who are executing the technologies and techniques they teach. For exact dates, times, locations, fees, and instructors, please refer to the course schedule published each term.

Proficiency Exams

A proficiency exam is available in Data Science. If you have not completed the necessary prerequisite(s) in a formal college-level course but have extensive experience in these areas, may apply to take a proficiency exam provided by the Engineering for Professionals program. Successful completion of the exam(s) allows you to opt-out of certain prerequisites.

Program Contacts

James spall.

Benjamin Rodriguez

Tony Johnson

Amanda Harwell

Valeria alfaro, tuition and fees.

Did you know that 78 percent of our enrolled students’ tuition is covered by employer contribution programs? Find out more about the cost of tuition for prerequisite and program courses and the Dean’s Fellowship.

Why Hopkins?

We built an online master’s degree in data science specifically for working professionals. Explore what you can do.

Student Resources - Your academic success is important to us. As a Johns Hopkins University student, you’ll have access to a variety of resources to support your successful path to completing your degree. Learn More

Learn on Your Terms - Develop the in-demand knowledge to achieve your personal career goals in your field of choice—on your schedule. Choose modern, relevant courses to design the learning experience that best fits your objectives. Learn More

Career-advancing Education - Coursework incorporates industry-specific knowledge that you can use from day one. As a graduate, you will be prepared to advance your career, cross over into other engineering fields, take on leadership roles, and increase your income-earning potential. Learn More

“ I appreciated that the program is rigorous and teaches current techniques. I always felt my coursework was relevant, and my professors were very knowledgeable and helpful. ”

Data Science FAQs

What can you do with a master’s in data science.

Because of the adaptability and diversity present in the field of data science, you can take your career in a wide variety of directions. Become an AI researcher, a data strategist, a business systems analyst, and more. Career advisors are standing by throughout your education experience to guide you, answer questions, and help you find your exact career path.

Is a Master’s in Data Science worth it?

Most graduates who hold a Master’s in Data Science receive a significant salary bump upon the completion of their degree. The median base salary for master’s holders is $92,500 . Plus, going through the program exposes you to the newest technologies, theories, and techniques that you might not have learned on your own. Add in all the networking opportunities the community provides and a master’s degree.

I don't have an engineering background, can I still apply to this program?

Yes. If we are otherwise willing to accept the student, we will determine which prerequisites are still needed as part of the review process. You will then be admitted provisionally until those courses have been successfully completed.

Academic Calendar

Find out when registration opens, classes start, transcript deadlines and more. Applications are accepted year-round, so you can apply any time.

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In its first-ever partnership with an international company, Johns Hopkins University’s Engineering for Professionals (EP) program is teaming up with the Panama Canal to offer their employees a custom graduate education program in…

Data Science

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Data science is an area of study within the Harvard John A. Paulson School of Engineering and Applied Sciences. Prospective students apply through the Harvard Kenneth C. Griffin Graduate of School of Arts and Sciences (Harvard Griffin GSAS). In the online application, select “Engineering and Applied Sciences” as your program choice and select “SM Data Science” in the area of study menu.

Data is being generated at an ever-increasing speed across all aspects of modern life. The data science master’s program combines computer science and statistics to train students how to analyze, contextualize, and draw insights from that data. The program offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition.

The program focuses on hands-on research projects. In many of the program’s courses, you will demonstrate your mastery of the material covered in the course by applying those methods in a final project. In addition, you will have a deeper research experience by completing a master’s thesis on a computational project under faculty supervision or through the Capstone Project course—in which teams of students work on real-world projects sourced from industry partners, such as working with Spotify on recommender systems and with the Massachusetts Bay Transportation Authority on optimum bus scheduling.

Graduates of the program have taken key positions at large technology companies, major financial institutions, and emerging startups. Others have gone on to doctoral studies in computer science and statistics.

Standardized Tests

GRE General:  Not Accepted

APPLICATION DEADLINE

Questions about the program.

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PhD in Statistics

The STEM-designated PhD in Statistics program provides advanced training in topics including probability, linear models, time series analysis, Bayesian statistics, inference, reliability, statistics in law and regulatory policy and much more.

Nearly all GW statistics PhD graduates have secured job placements in the statistics or data science industry, with employers  including Amazon, Facebook and Capital One. During the program, PhD students work closely with faculty on original research in their area of interest. 

The degree provides training in theory and applications and is suitable for both full-time and part-time students. Most graduate courses are offered in the early evening to accommodate student schedules. 

Apply to GW

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Graduate Program Advisors

Application Requirements

Prospective PhD students typically have earned a master’s degree in statistics or a related discipline. Students need a strong background in mathematics, including courses in advanced calculus, linear algebra and mathematical statistics.

Complete Application Requirements

"GW encouraged me to tap into expertise from within as well as outside the university while researching my dissertation topic. I learned about the value of collaboration throughout my doctoral studies. Collaboration is so important in science, and it’s been instrumental in our success at Emmes."

Anne Lindblad PhD ’90 President, The Emmes Company

Students in their first semester of the PhD in Statistics program must meet with the program director  prior to signing up for classes. Students should continue to seek advice from the advisor throughout the program, particularly when determining whether any previous coursework can be applied toward their degree.

General Examinations

The general examination consists of two parts: a qualifying examination and an examination to determine the student's readiness to carry out the proposed dissertation research.

Each PhD candidate is required to take and pass the PhD qualifying exam. The written exam is given at the beginning of the fall semester each year. It consists of two papers:

• Inference: STAT 6202 and 8263
• Probability: STAT 6201 and 8257

The written exam is required for the first attempt. If a student cannot pass it, then there are two options for the second attempt.

• Option #1 for the second attempt : after approximately a year, the student will retake the written exam (see above for exam description).
• Option #2 for the second attempt : within approximately half a year, based on the scope of the written exam (see above for exam description), the student must demonstrate satisfactory improvements through (open-book, take-home) problem solving and an oral exam (with questions and answers).

No more than two attempts are permitted.

After passing the qualifying examination, the candidate should select a dissertation advisor. In consultation with the advisor, the candidate should pass a readiness examination, usually consisting of a research proposal and an oral examination. A committee of at least two professors should administer the readiness examination.

Dissertation

Students are required to complete a written dissertation that should be defended before an examination committee of at least four examiners. The dissertation should contain original scholarly research and must comply with all other GW rules and regulations. For more guidance on dissertation process, review the CCAS PhD Student Handbook . For formatting and submission guidelines, visit the Electronic Theses and Dissertations Submission website .

Past Theses

Course Requirements 

The program requires 72 credit hours, of which at least 48 must be from coursework and at least 12 must be from dissertation research. Up to 24 credit hours may be transferred from a prior master’s degree (contrary to general GW doctoral program requirements , which allow up to 30 transfer credit hours).

Smart. Open. Grounded. Inventive. Read our Ideas Made to Matter.

Which program is right for you?

Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world.

A rigorous, hands-on program that prepares adaptive problem solvers for premier finance careers.

A 12-month program focused on applying the tools of modern data science, optimization and machine learning to solve real-world business problems.

Earn your MBA and SM in engineering with this transformative two-year program.

Combine an international MBA with a deep dive into management science. A special opportunity for partner and affiliate schools only.

A doctoral program that produces outstanding scholars who are leading in their fields of research.

Bring a business perspective to your technical and quantitative expertise with a bachelor’s degree in management, business analytics, or finance.

A joint program for mid-career professionals that integrates engineering and systems thinking. Earn your master’s degree in engineering and management.

An interdisciplinary program that combines engineering, management, and design, leading to a master’s degree in engineering and management.

Executive Programs

A full-time MBA program for mid-career leaders eager to dedicate one year of discovery for a lifetime of impact.

This 20-month MBA program equips experienced executives to enhance their impact on their organizations and the world.

Non-degree programs for senior executives and high-potential managers.

A non-degree, customizable program for mid-career professionals.

PhD Program

Program overview.

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Rigorous, discipline-based research is the hallmark of the MIT Sloan PhD Program. The program is committed to educating scholars who will lead in their fields of research—those with outstanding intellectual skills who will carry forward productive research on the complex organizational, financial, and technological issues that characterize an increasingly competitive and challenging business world.

Start here.

Learn more about the program, how to apply, and find answers to common questions.

Admissions Events

Check out our event schedule, and learn when you can chat with us in person or online.

Start Your Application

Visit this section to find important admissions deadlines, along with a link to our application.

Click here for answers to many of the most frequently asked questions.

PhD studies at MIT Sloan are intense and individual in nature, demanding a great deal of time, initiative, and discipline from every candidate. But the rewards of such rigor are tremendous:  MIT Sloan PhD graduates go on to teach and conduct research at the world's most prestigious universities.

PhD Program curriculum at MIT Sloan is organized under the following three academic areas: Behavior & Policy Sciences; Economics, Finance & Accounting; and Management Science. Our nine research groups correspond with one of the academic areas, as noted below.

MIT Sloan PhD Research Groups

Behavioral & policy sciences.

Economic Sociology

Institute for Work & Employment Research

Organization Studies

Technological Innovation, Entrepreneurship & Strategic Management

Economics, Finance & Accounting

Accounting  

Management Science

Information Technology

System Dynamics  

Those interested in a PhD in Operations Research should visit the Operations Research Center .  

PhD Program Structure

Additional information including coursework and thesis requirements.

MIT Sloan Predoctoral Opportunities

MIT Sloan is eager to provide a diverse group of talented students with early-career exposure to research techniques as well as support in considering research career paths.

Rising Scholars Conference

The fourth annual Rising Scholars Conference on October 25 and 26 gathers diverse PhD students from across the country to present their research.

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The goal of the MIT Sloan PhD Program's admissions process is to select a small number of people who are most likely to successfully complete our rigorous and demanding program and then thrive in academic research careers. The admission selection process is highly competitive; we aim for a class size of nineteen students, admitted from a pool of hundreds of applicants.

What We Seek

• Outstanding intellectual ability
• Excellent academic records
• Previous work in disciplines related to the intended area of concentration
• Strong commitment to a career in research

MIT Sloan PhD Program Admissions Requirements Common Questions

Dates and Deadlines

Admissions for 2024 is closed. The next opportunity to apply will be for 2025 admission. The 2025 application will open in September 2024. 

More information on program requirements and application components

Students in good academic standing in our program receive a funding package that includes tuition, medical insurance, and a fellowship stipend and/or TA/RA salary. We also provide a new laptop computer and a conference travel/research budget.

Funding Information

Throughout the year, we organize events that give you a chance to learn more about the program and determine if a PhD in Management is right for you.

PhD Program Events

Discover your doctoral path.

An in-person event for prospective students with Boston-area management programs

September 12 PhD Program Overview

During this webinar, you will hear from the PhD Program team and have the chance to ask questions about the application and admissions process.

DocNet Recruiting Forum at University of Minnesota

We will be joining the DocNet consortium for an overview of business academia and a recruitment fair at University of Minnesota, Carlson School of Management.

September 25 PhD Program Overview

Complete PhD Admissions Event Calendar

Unlike formulaic approaches to training scholars, the PhD Program at MIT Sloan allows students to choose their own adventure and develop a unique scholarly identity. This can be daunting, but students are given a wide range of support along the way - most notably having access to world class faculty and coursework both at MIT and in the broader academic community around Boston.

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Profiles of our current students

MIT Sloan produces top-notch PhDs in management. Immersed in MIT Sloan's distinctive culture, upcoming graduates are poised to innovate in management research and education.

Academic Job Market

Doctoral candidates on the current academic market

Academic Placements

Graduates of the MIT Sloan PhD Program are researching and teaching at top schools around the world.

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MIT Sloan Experience

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The PhD Program is integral to the research of MIT Sloan's world-class faculty. With a reputation as risk-takers who are unafraid to embrace the unconventional, they are engaged in exciting disciplinary and interdisciplinary research that often includes PhD students as key team members.

Research centers across MIT Sloan and MIT provide a rich setting for collaboration and exploration. In addition to exposure to the faculty, PhD students also learn from one another in a creative, supportive research community.

Throughout MIT Sloan's history, our professors have devised theories and fields of study that have had a profound impact on management theory and practice.

From Douglas McGregor's Theory X/Theory Y distinction to Nobel-recognized breakthroughs in finance by Franco Modigliani and in option pricing by Robert Merton and Myron Scholes, MIT Sloan's faculty have been unmatched innovators.

This legacy of innovative thinking and dedication to research impacts every faculty member and filters down to the students who work beside them.

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Student Research

“MIT Sloan PhD training is a transformative experience. The heart of the process is the student’s transition from being a consumer of knowledge to being a producer of knowledge. This involves learning to ask precise, tractable questions and addressing them with creativity and rigor. Hard work is required, but the reward is the incomparable exhilaration one feels from having solved a puzzle that had bedeviled the sharpest minds in the world!” -Ezra Zuckerman Sivan Alvin J. Siteman (1948) Professor of Entrepreneurship

Sample Dissertation Abstracts - These sample Dissertation Abstracts provide examples of the work that our students have chosen to study while in the MIT Sloan PhD Program.

We believe that our doctoral program is the heart of MIT Sloan's research community and that it develops some of the best management researchers in the world. At our annual Doctoral Research Forum, we celebrate the great research that our doctoral students do, and the research community that supports that development process.

The videos of their presentations below showcase the work of our students and will give you insight into the topics they choose to research in the program.

Attention To Retention: The Informativeness of Insiders’ Decision to Retain Shares

2024 PhD Doctoral Research Forum Winner - Gabriel Voelcker

Watch more MIT Sloan PhD Program  Doctoral Forum Videos

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Data Science PhD Program

Supervising faculty.

• Program Structure

Current Students

• Application

NYU Shanghai, in partnership with the NYU Graduate School of Arts and Science and the NYU Center for Data Science, invites applications from exceptional students for PhD study and research in Data Science.   Participating students are enrolled in the NYU GSAS Data Science PhD program, complete their coursework at the NYU Center for Data Science in New York, and then transition to full-time residence at NYU Shanghai where they undertake their doctoral research under the supervision of NYU Shanghai faculty.

Highlights of the Program

• NYU degree upon graduation
• Graduate coursework at the NYU Center for Data Science in New York
• Research opportunities with and close mentorship by NYU Shanghai faculty
• Access to the vast intellectual resources of NYU GSAS and NYU Center for Data Science
• Cutting-edge research environment at NYU Shanghai, including the Center for Data Science and Artificial Intelligence, a thriving community of PhD students, post-doctoral fellows, and research associates, activities such as a regular program of seminars and visiting academics, and links with other universities within and outside China
• Financial aid through the NYU Shanghai Doctoral Fellowship , including tuition, fees, and an annual stipend
• Additional benefits exclusive to the NYU Shanghai program, including international health insurance and travel funds

Mathieu Laurière

Computational Methods, Optimal Control, Game Theory, Partial Differential Equations, Stochastic Analysis, Deep Learning, Reinforcement Learning

Shuyang Ling

Applied Mathematics, Optimization, Probability, Signal Processing, Mathematics of Data Science, Machine Learning

Natural Language Processing, Human-Computer Interaction, Machine Learning

Recent Publications by NYU Shanghai Faculty

Carmona, R., Cooney, D., Graves, C., and Laurière, M. Stochastic Graphon Games: I. The Static Case. To appear in Mathematics of Operations Research (2021)

Carmona, R., and Laurière, M. Convergence analysis of machine learning algorithms for the numerical solution of mean field control and games: I - the ergodic case. To appear in SIAM Journal on Numerical Analysis (2021)

Achdou, Y., Laurière, M. , and Lions, P.-L. Optimal control of conditioned processes with feedback controls. Journal de Mathématiques Pures et Appliquées (2020)

Elie, R., Pérolat, J., Laurière, M. , Geist, M., and Pietquin, O. On the convergence of model free learning in mean field games. In 34th AAAI Conference on Artificial Intelligence, AAAI 2020

Perrin, S., Pérolat, J., Laurière, M. , Geist, M., Elie, R., and Pietquin, O. Fictitious play for mean field games: Continuous time analysis and applications. In 34th Conference on Neural Information Processing Systems, NeurIPS 2020 (2020)

Strong consistency, graph Laplacians, and the stochastic block model. S Deng, S Ling , T Strohmer. The Journal of Machine Learning Research 22 (117), 1-44

When do birds of a feather flock together? k-means, proximity, and conic programming. X Li, Y Li, S Ling , T Strohmer, K Wei. Mathematical Programming, Series A 179 (1), 295-341

Shuyang Ling , Ruitu Xu, Afonso S. Bandeira. On the landscape of synchronization networks: a perspective from nonconvex optimization, SIAM Journal on Optimization, Vol.29, No.3, pp.1879-1907, 2019.

Shuyang Ling and Thomas Strohmer. Certifying global optimality of graph cuts via semidefinite relaxation: A performance guarantee for spectral clustering, Foundations of Computational Mathematics, 2019.

Xiaodong Li, Shuyang Ling , Thomas Strohmer, and Ke Wei. Rapid, robust, and reliable blind deconvolution via nonconvex optimization. Applied and Computational Harmonic Analysis, Volume 47, Issue 3, pp.893-934, 2019.

Shuyang Ling and Thomas Strohmer. Blind deconvolution meets blind demixing: algorithms and performance bounds. IEEE Transactions on Information Theory, Vol.63, No.7, pp.4497 - 4520, Jul 2017.

Shuyang Ling and Thomas Strohmer. Self-calibration and biconvex compressive sensing. Inverse Problems, Vol. 31(11): 115002, 2015.

Zhao, C. , Su, Y., Pauls, A., & Platanios, E. A.  Bridging the generalization gap in text-to-SQL parsing with schema expansion. ACL 2022.

Zhao, C. , Xiong, C., Boyd-Graber, J., & Daumé III, H. (2021). Distantly-supervised evidence retrieval enables question answering without evidence annotation. EMNLP 2021.

Zhao, C. , Xiong, C., Qian, X., & Boyd-Graber, J. . Complex factoid question answering with a free-text knowledge graph. WWW 2020.

Zhao, C. , Xiong, C., Rosset, C., Song, X., Bennett, P., & Tiwary, S. (2020). Transformer-xh: Multi-evidence reasoning with extra hop attention. ICLR 2020.

Selected Faculty Features

" Harnessing the Power of Data Science and AI " (Center for Data Science and AI )

" NYU Shanghai Establishes New Data Science  PhD " (Shuyang Ling)

" Q&A with Data Science Professor Ling Shuyang " (Shuyang Ling)

Structure of Program

Participating students complete the PhD degree requirements set by the NYU Center for Data Science and in accordance with the academic policies of NYU GSAS. Each student develops an individualized course plan in consultation with the Director of Graduate Study at the NYU Center for Data Science and the student’s NYU Shanghai faculty advisor. A typical sequence follows:

Begin program with funded research rotation, up to 3 months preceding first Fall semester, to familiarize with NYU Shanghai and faculty as well as lay a foundation for future doctoral study.

Pursue PhD coursework at NYU Center for Data Science alongside other NYU PhD students. 

Return to Shanghai for second funded research rotation to solidify relationships with NYU Shanghai faculty and make further progress in research.

Under supervision of NYU Shanghai faculty advisor, pursue dissertation research and continue coursework. Depending on each student’s individualized course of study, return visits to New York may also occur. Complete all required examinations and progress evaluations, both oral and written, leading up to submission and defense of doctoral thesis.

To learn more about the NYU Data Science PhD program degree requirements, please visit this page .

Application Process and Dates

Applications are to be submitted through the NYU GSAS Application portal , within which students should select the Data Science PhD as their program of interest, and then indicate their preference for NYU Shanghai by marking the appropriate checkbox when prompted. Applicants will be evaluated by a joint admissions committee of New York and Shanghai faculty. Application requirements are set by the NYU Center for Data Science and are the same as those for all NYU PhD applicants, no matter their campus preference; however, candidates are recommended to elaborate in their application and personal statements about their specific interests in the NYU Shanghai program and faculty.

For admission in Fall 2024, the application deadline is December 5, 2023.

Interested students are welcome to contact Vivien Du , PhD Program Manager, via email at [email protected] with any inquiries or to request more information.

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DPhil in Social Data Science

• Entry requirements
• Funding and Costs

College preference

• How to Apply

About the course

The DPhil in Social Data Science is an advanced research degree which provides the opportunity to investigate and address novel research questions at the intersection of the computational and social sciences, supported by the multidisciplinary faculty at the OII, Mathematics, Computer Science, Engineering, Statistics,  and other departments across the University of Oxford. The DPhil, normally taking three to four years of full-time study to complete, is known as a PhD at other universities.

The DPhil in Social Data Science at the Oxford Internet Institute (OII) will introduce you to cutting-edge research whilst studying in a beautiful, historic setting that is both student- and family-friendly. During your study at Oxford, you are encouraged to pioneer new approaches to contemporary social and policy issues online, developing new computational and data-driven methodology to inform the development and governance of technology. As a student, you will be part of a diverse cohort of research students, of many nationalities and from a wide range of scientific backgrounds. Research students in Social Data Science are graduates in subjects from computer science and mathematics to physics, as well as transdisciplinary subjects such as human-centred data science and complex systems.

The course combines individual supervision with a selection of lectures, seminars, transferrable skills training, and opportunities to participate in leading-edge research activities. OII faculty are world class experts working in the cutting-edge of their fields, and this innovative research is fully reflected in their course teaching. You will be able to audit courses led by faculty at the OII, as well as courses in other departments.

The programme provides a strong computational foundation, training you to develop new research skills in areas such as machine learning, statistical modelling, large-scale data collection, algorithm auditing, or network science. The DPhil in Social Data Science provides you with a rare grounding in both technical skills and social science research , helping you build critical skills to study digital technologies. There are weekly opportunities for you to interact with DPhil in Information, Communication and the Social Sciences students, providing a rich multidisciplinary environment.

As a full-time student, you are expected to continue working outside of the University terms with an annual holiday of approximately eight weeks.

Part-time study

The DPhil programme at the OII is also available on a part-time basis. The part-time programme is spread over six to eight years of study and research. It offers the flexibility of part-time study with the same high standards and requirements as the full-time DPhil programme. The part-time DPhil also provides an excellent opportunity for professionals in industry and civil society to undertake rigorous long-term research that may be relevant to their career.

As a part-time student, you will be required to attend seminars, supervision meetings, and other obligations in Oxford for a minimum of 30 days each year. Attendance will be required during term-time (a minimum of one day each week). There will be limited flexibility in the dates and pattern of attendance, which will normally be determined by the fixed teaching and seminar schedule during term. Attendance may be required outside of term-time on dates to be determined by mutual agreement with your supervisor. You will have the opportunity to tailor your part-time study in liaison with your supervisor and agree your pattern of attendance.

Supervision

The allocation of graduate supervision for this course is the responsibility of the Oxford Internet Institute and it is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff.

Supervision for the DPhil in Social Data Science spans multiple departments (please see the full list of faculty members  eligible to supervise DPhil students for this programme). A supervisor may be found outside the list on the course web page, and co-supervision is also possible. All students will have at least one supervisor who is a faculty member of the OII.

Students should normally expect to meet with their supervisor at least three to four times a term. A more typical pattern is weekly or bimonthly, at least until you reach the stage of writing up your thesis.

The first year is a probationary year, soon after which, subject to satisfactory progress, you will be expected to transfer from Probationer Research Student (PRS) status to full DPhil status. The Transfer of Status takes place within a maximum of four terms for full-time students or eight terms for part-time students. A second formal assessment of progress, Confirmation of Status, takes place later in the programme, normally at the end of the third year. The Transfer of Status and Confirmation of Status assessments are conducted by two members of staff other than the student’s supervisor(s) or advisors.

The sequence of milestones for a DPhil student are as follows:

• Admission as a Probationer Research Student (PRS)
• Transfer to DPhil status (‘Transfer of Status’)
• Confirmation of DPhil status for DPhil students (‘Confirmation of Status’)
• Submission of thesis

Students initially admitted to the status of Probationer Research Student (PRS) are required to attend and pass core modules from the OII’s training programme. Students who have already completed similar courses in their past academic career should request an exemption from one or more modules by providing sufficient evidence.  

A successful transfer of status from PRS to DPhil status will require the student to show that their proposed thesis represents a viable topic and that their written work and interview show that they have a good knowledge and understanding of the subject. Students are also required to demonstrate satisfactory completion of the foundational courses by this point.

Following successful transfer, students will need to apply for and gain confirmation of DPhil status to show that the work continues to be on track. This will need to be completed within nine terms of admission for full-time students and 18 terms of admission for part-time students.

Both milestones involve an interview with two assessors (other than your supervisor) and therefore provide important experience for the final oral examination.

Full-time students will be expected to submit an original thesis of not more than 100,000 words three or, at most, four years from the date of admission. If you are studying part-time, you be required to submit your thesis after six or, at most, eight years from the date of admission. To be successfully awarded a DPhil in Social Data Science you will need to defend your thesis orally (viva voce) in front of two appointed examiners.

Graduate destinations

The Oxford Internet Institute provides you with skills and opportunities in teaching, research, policymaking and business innovation. Employers recognise the value of a degree from the University of Oxford, and the OII’s doctoral students regularly go on to secure excellent positions in industry, government, and NGOs. 

Alumni who have pursued academic careers have taken up research and teaching positions including notably at the University of Oxford, Cornell University, University of Hong Kong, Imperial College London, and TU Delft. OII DPhil alumni have worked in a wide range of organisations including The World Bank, Open Technology Fund, Oxfam, Cisco, McKinsey, and Google.

The OII Alumni page  features interviews from both MSc and DPhil alumni about their time at the Department and career paths after Oxford.

Changes to this course and your supervision

The University will seek to deliver this course in accordance with the description set out in this course page. However, there may be situations in which it is desirable or necessary for the University to make changes in course provision, either before or after registration. The safety of students, staff and visitors is paramount and major changes to delivery or services may have to be made in circumstances of a pandemic, epidemic or local health emergency. In addition, in certain circumstances, for example due to visa difficulties or because the health needs of students cannot be met, it may be necessary to make adjustments to course requirements for international study.

Where possible your academic supervisor will not change for the duration of your course. However, it may be necessary to assign a new academic supervisor during the course of study or before registration for reasons which might include illness, sabbatical leave, parental leave or change in employment.

For further information please see our page on changes to courses and the provisions of the student contract regarding changes to courses.

Entry requirements for entry in 2024-25

Proven and potential academic excellence.

The requirements described below are specific to this course and apply only in the year of entry that is shown. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

Please be aware that any studentships that are linked to this course may have different or additional requirements and you should read any studentship information carefully before applying. 

Degree-level qualifications

As a minimum, applicants should hold or be predicted to achieve the following UK qualifications or their equivalent:

• a master's degree with a mark of at least 65% ; and
• a first-class or strong upper second-class undergraduate degree with honours  in any subject.

It is expected that all applicants will hold a taught masters or other advanced degree.

For applicants with a degree from the USA, the minimum GPA sought is 3.5 out of 4.0.

If your degree is not from the UK or another country specified above, visit our International Qualifications page for guidance on the qualifications and grades that would usually be considered to meet the University’s minimum entry requirements.

GRE General Test scores

No Graduate Record Examination (GRE) or GMAT scores are sought.

Other qualifications, evidence of excellence and relevant experience

Strong analytical abilities in understanding the social aspects of the internet, World Wide Web and related technologies, as shown by the candidate’s writing sample and/or the reports of referees, are required. It would be expected that graduate applicants would be familiar with the recent published work of their proposed supervisor.

Applicants are expected to demonstrate quantitative aptitude or experience in at least half of the material covered by the MSc in Social Data Science.

Applicants may demonstrate this aptitude/experience in a variety of ways including:

• graduate and undergraduate transcripts;
• on-the-job training and practical experience;
• evidence of the successful completion of online courses.

Applicants are not expected to have published academic work previously, although publication may help the assessors judge your writing ability and thus could help your application.

Academic research related to data science or experience working in related businesses is not required, but may be an advantage.

Part-time applicants will also be expected to demonstrate their ability to commit sufficient time to study and spend a minimum of 30 days in Oxford per year, including attendance of teaching, seminars and departmental events, to complete coursework, and attend course and University events and modules. If applicable, evidence should also be provided of the employer’s commitment to make time available for study, and of the student’s permission to use employers’ data in the proposed research project.

English language proficiency

This course requires proficiency in English at the University's  higher level . If your first language is not English, you may need to provide evidence that you meet this requirement. The minimum scores required to meet the University's higher level are detailed in the table below.

*Previously known as the Cambridge Certificate of Advanced English or Cambridge English: Advanced (CAE) † Previously known as the Cambridge Certificate of Proficiency in English or Cambridge English: Proficiency (CPE)

Your test must have been taken no more than two years before the start date of your course. Our Application Guide provides  further information about the English language test requirement .

Declaring extenuating circumstances

If your ability to meet the entry requirements has been affected by the COVID-19 pandemic (eg you were awarded an unclassified/ungraded degree) or any other exceptional personal circumstance (eg other illness or bereavement), please refer to the guidance on extenuating circumstances in the Application Guide for information about how to declare this so that your application can be considered appropriately.

You will need to register three referees who can give an informed view of your academic ability and suitability for the course. The  How to apply  section of this page provides details of the types of reference that are required in support of your application for this course and how these will be assessed.

Supporting documents

You will be required to supply supporting documents with your application. The  How to apply  section of this page provides details of the supporting documents that are required as part of your application for this course and how these will be assessed.

Performance at interview

Interviews are held as part of the admissions process.

All applications are reviewed by at least two members of faculty with relevant experience and expertise. Applicants are shortlisted based on the quality of the written application. Those who are shortlisted will usually be interviewed.

Interviews are typically held three to six weeks after the application deadline. There is usually only one interview held, which lasts 30 to 40 minutes and can be held via a video conferencing platform. You will be asked questions about your academic background, your research plan, and why you think the Oxford Internet Institute would be the best place to conduct your studies. The interview panel will consist of at least two interviewers which will normally include the potential supervisor.

How your application is assessed

Your application will be assessed purely on your proven and potential academic excellence and other entry requirements described under that heading.

References  and  supporting documents  submitted as part of your application, and your performance at interview (if interviews are held) will be considered as part of the assessment process. Whether or not you have secured funding will not be taken into consideration when your application is assessed.

An overview of the shortlisting and selection process is provided below. Our ' After you apply ' pages provide  more information about how applications are assessed . 

Shortlisting and selection

Students are considered for shortlisting and selected for admission without regard to age, disability, gender reassignment, marital or civil partnership status, pregnancy and maternity, race (including colour, nationality and ethnic or national origins), religion or belief (including lack of belief), sex, sexual orientation, as well as other relevant circumstances including parental or caring responsibilities or social background. However, please note the following:

• socio-economic information may be taken into account in the selection of applicants and award of scholarships for courses that are part of  the University’s pilot selection procedure  and for  scholarships aimed at under-represented groups ;
• country of ordinary residence may be taken into account in the awarding of certain scholarships; and
• protected characteristics may be taken into account during shortlisting for interview or the award of scholarships where the University has approved a positive action case under the Equality Act 2010.

Initiatives to improve access to graduate study

This course is taking part in a continuing pilot programme to improve the selection procedure for graduate applications, in order to ensure that all candidates are evaluated fairly.

For this course, socio-economic data (where it has been provided in the application form) will be used to contextualise applications at the different stages of the selection process.  Further information about how we use your socio-economic data  can be found in our page about initiatives to improve access to graduate study.

Processing your data for shortlisting and selection

Information about  processing special category data for the purposes of positive action  and  using your data to assess your eligibility for funding , can be found in our Postgraduate Applicant Privacy Policy.

Admissions panels and assessors

All recommendations to admit a student involve the judgement of at least two members of the academic staff with relevant experience and expertise, and must also be approved by the Director of Graduate Studies or Admissions Committee (or equivalent within the department).

Admissions panels or committees will always include at least one member of academic staff who has undertaken appropriate training.

Other factors governing whether places can be offered

The following factors will also govern whether candidates can be offered places:

• the ability of the University to provide the appropriate supervision for your studies, as outlined under the 'Supervision' heading in the  About  section of this page;
• the ability of the University to provide appropriate support for your studies (eg through the provision of facilities, resources, teaching and/or research opportunities); and
• minimum and maximum limits to the numbers of students who may be admitted to the University's taught and research programmes.

Offer conditions for successful applications

If you receive an offer of a place at Oxford, your offer will outline any conditions that you need to satisfy and any actions you need to take, together with any associated deadlines. These may include academic conditions, such as achieving a specific final grade in your current degree course. These conditions will usually depend on your individual academic circumstances and may vary between applicants. Our ' After you apply ' pages provide more information about offers and conditions . 

In addition to any academic conditions which are set, you will also be required to meet the following requirements:

Financial Declaration

If you are offered a place, you will be required to complete a  Financial Declaration  in order to meet your financial condition of admission.

Disclosure of criminal convictions

In accordance with the University’s obligations towards students and staff, we will ask you to declare any  relevant, unspent criminal convictions  before you can take up a place at Oxford.

Academic Technology Approval Scheme (ATAS)

Some postgraduate research students in science, engineering and technology subjects will need an Academic Technology Approval Scheme (ATAS) certificate prior to applying for a  Student visa (under the Student Route) . For some courses, the requirement to apply for an ATAS certificate may depend on your research area.

The DPhil in Social Data Science is offered by the Oxford Internet Institute (OII) in partnership with Statistics, Engineering Science, Sociology, and other departments. The OII faculty works at the cutting-edge of their fields, and this innovative research is fully reflected in their course teaching. The department prides itself on providing a stimulating and supportive environment in which all students can flourish. As a fully multidisciplinary department, the OII offers you the opportunity to study academic, practical and policy-related issues that can only be understood by drawing on contributions from across many different fields.

In addition to the formal requirements of the DPhil thesis, all OII doctoral students have access to regular training in the key professional skills necessary to support their research and future employment. These range from classes on advanced research methods as part of the OII’s option course offerings, to professional development training (provided both by the department and the University) such as presentation skills, academic writing and navigating the process of peer review.

You will attend a weekly seminar in which you will present your own work for critique, and critique the work of your peers. The OII also provides opportunities for DPhil students to gain teaching experience through mentored assistantship roles in some of its core MSc courses.

The department's busy calendar of seminars and events brings many of the most important people in internet research, innovation and policy to the OII, allowing students to engage with cutting-edge scholarship and debates around the internet and digital technologies.

OII students also take full advantage of the substantial resources available at the University of Oxford, including world-leading research facilities and libraries, and a buzzing student scene. The departmental library provides students access to a range of resources including the texts required for the degree. Other University libraries provide valuable additional resources of which many students choose to take advantage.

Oxford Internet Institute

The Oxford Internet Institute (OII) is a dynamic and innovative department for research and teaching relating to the internet, located in a world-leading traditional research university. The multidisciplinary OII offers the opportunity to study academic, practical and policy-related issues that can only be understood by drawing on contributions from many different fields.

The OII is the only major department in a top-ranked international university to offer multidisciplinary courses in the social sciences dedicated to understanding the impact of the internet, data, and information technologies on society. We offer masters and doctoral level education across several degrees focused on social data science or the social science of the internet and technology.

Digital connections are now embedded in almost every aspect of our daily lives, and research on individual and collective behaviour online is crucial to understanding our social, economic and political world. As a fully multi-disciplinary department, we offer our students the opportunity to study academic, practical and policy-related issues and pursue cutting-edge research into the societal implications of the internet and digital technologies.

Our academic faculty and graduate students are drawn from many different disciplines: we believe this combined approach is essential to tackle society’s big questions. Together, we aim to positively shape the development of our digital world for the public good.

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The University expects to be able to offer over 1,000 full or partial graduate scholarships across the collegiate University in 2024-25. You will be automatically considered for the majority of Oxford scholarships , if you fulfil the eligibility criteria and submit your graduate application by the relevant December or January deadline. Most scholarships are awarded on the basis of academic merit and/or potential. 

For further details about searching for funding as a graduate student visit our dedicated Funding pages, which contain information about how to apply for Oxford scholarships requiring an additional application, details of external funding, loan schemes and other funding sources.

Please ensure that you visit individual college websites for details of any college-specific funding opportunities using the links provided on our college pages or below:

Please note that not all the colleges listed above may accept students on this course. For details of those which do, please refer to the College preference section of this page.

Further information about funding opportunities for this course can be found on the institute's website.

Annual fees for entry in 2024-25

Full-time study.

Further details about fee status eligibility can be found on the fee status webpage.

Information about course fees

Course fees are payable each year, for the duration of your fee liability (your fee liability is the length of time for which you are required to pay course fees). For courses lasting longer than one year, please be aware that fees will usually increase annually. For details, please see our guidance on changes to fees and charges .

Course fees cover your teaching as well as other academic services and facilities provided to support your studies. Unless specified in the additional information section below, course fees do not cover your accommodation, residential costs or other living costs. They also don’t cover any additional costs and charges that are outlined in the additional information below.

Continuation charges

Following the period of fee liability , you may also be required to pay a University continuation charge and a college continuation charge. The University and college continuation charges are shown on the Continuation charges page.

Where can I find further information about fees?

The Fees and Funding  section of this website provides further information about course fees , including information about fee status and eligibility  and your length of fee liability .

Additional information

There are no compulsory elements of this programme that entail additional costs beyond fees and living costs. However, please note that, depending on your choice of research topic and the research required to complete it, you may incur additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.

Please note that you are required to attend in Oxford for a minimum of 30 days each year, and you may incur additional travel and accommodation expenses for this. Also, depending on your choice of research topic and the research required to complete it, you may incur further additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.

Whilst many graduate students do undertake employment to support their studies, please remember that students on the full-time arrangement of the OII's DPhil programme are subject to limits on the number of hours that may be worked each week. Part-time student are not subject to these limitations.

Within these limitations, many of the OII's existing full-time DPhil students have been employed on a short or long-term basis as Research Assistants on grant-funded projects gaining valuable research experience. The OII also offers Teaching Assistant positions on the MSc degree for DPhil students who can display the appropriate skills. In addition, there are employment opportunities within the University (such as teaching, translation, and research assistance) as well as within the OII.

For full information on employment whilst on course, please see the University's  paid work guidelines for Oxford graduate students .

Living costs

In addition to your course fees, you will need to ensure that you have adequate funds to support your living costs for the duration of your course.

For the 2024-25 academic year, the range of likely living costs for full-time study is between c. £1,345 and £1,955 for each month spent in Oxford. Full information, including a breakdown of likely living costs in Oxford for items such as food, accommodation and study costs, is available on our living costs page. The current economic climate and high national rate of inflation make it very hard to estimate potential changes to the cost of living over the next few years. When planning your finances for any future years of study in Oxford beyond 2024-25, it is suggested that you allow for potential increases in living expenses of around 5% each year – although this rate may vary depending on the national economic situation. UK inflationary increases will be kept under review and this page updated.

If you are studying part-time your living costs may vary depending on your personal circumstances but you must still ensure that you will have sufficient funding to meet these costs for the duration of your course.

Students enrolled on this course will belong to both a department/faculty and a college. Please note that ‘college’ and ‘colleges’ refers to all 43 of the University’s colleges, including those designated as societies and permanent private halls (PPHs). 

If you apply for a place on this course you will have the option to express a preference for one of the colleges listed below, or you can ask us to find a college for you. Before deciding, we suggest that you read our brief  introduction to the college system at Oxford  and our  advice about expressing a college preference . For some courses, the department may have provided some additional advice below to help you decide.

The following colleges accept students for full-time study on this course:

• Blackfriars
• Campion Hall
• Christ Church
• Exeter College
• Green Templeton College
• Hertford College
• Jesus College
• Keble College
• Kellogg College
• Linacre College
• Nuffield College
• Reuben College
• St Antony's College
• St Catherine's College
• St Cross College
• St Hilda's College
• Wadham College
• Wolfson College
• Wycliffe Hall

The following colleges accept students for part-time study on this course:

Before you apply

Our  guide to getting started  provides general advice on how to prepare for and start your application. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

If it's important for you to have your application considered under a particular deadline – eg under a December or January deadline in order to be considered for Oxford scholarships – we recommend that you aim to complete and submit your application at least two weeks in advance . Check the deadlines on this page and the  information about deadlines and when to apply  in our Application Guide.

Application fee waivers

An application fee of £75 is payable per course application. Application fee waivers are available for the following applicants who meet the eligibility criteria:

• applicants from low-income countries;
• refugees and displaced persons; 
• UK applicants from low-income backgrounds; and 
• applicants who applied for our Graduate Access Programmes in the past two years and met the eligibility criteria.

You are encouraged to  check whether you're eligible for an application fee waiver  before you apply.

Readmission for current Oxford graduate taught students

If you're currently studying for an Oxford graduate taught course and apply to this course with no break in your studies, you may be eligible to apply to this course as a readmission applicant. The application fee will be waived for an eligible application of this type. Check whether you're eligible to apply for readmission .

Do I need to contact anyone before I apply?

You are recommended to contact a potential supervisor (or supervisors) in the first instance to get feedback on the fit of your proposed research with the expertise of the supervisor before you apply. The full list of faculty members eligible to supervise DPhil students for this course, including their research interests and contact details, can be found on the departmental website. Please note that the Oxford Internet Institute will only admit students where appropriate supervision is available.

Completing your application

You should refer to the information below when completing the application form, paying attention to the specific requirements for the supporting documents .

For this course, the application form will include questions that collect information that would usually be included in a CV/résumé. You should not upload a separate document. If a separate CV/résumé is uploaded, it will be removed from your application .

If any document does not meet the specification, including the stipulated word count, your application may be considered incomplete and not assessed by the academic department. Expand each section to show further details.

Proposed field and title of research project

Under the 'Field and title of research project' please enter your proposed field or area of research if this is known. If the department has advertised a specific research project that you would like to be considered for, please enter the project title here instead.

You should not use this field to type out a full research proposal. You will be able to upload your research supporting materials separately if they are required (as described below).

Proposed supervisor

If known, under 'Proposed supervisor name' enter the name of the academic(s) whom you would like to supervise your research. Otherwise, leave this field blank.

Referees: Three overall, academic and/or professional

Whilst you must register three referees, the department may start the assessment of your application if two of the three references are submitted by the course deadline and your application is otherwise complete. Please note that you may still be required to ensure your third referee supplies a reference for consideration.

Professional references are acceptable, particularly if you have been out of education for some time, but should focus particularly on your intellectual abilities rather than more narrowly on job performance.

Your references will be assessed for:

• your intellectual ability;
• your academic achievement; and 
• your motivation and interest in the course and subject area.

Official transcript(s)

Your transcripts should give detailed information of the individual grades received in your university-level qualifications to date. You should only upload official documents issued by your institution and any transcript not in English should be accompanied by a certified translation.

More information about the transcript requirement is available in the Application Guide.

Personal statement and research proposal: Statement of a maximum of 500 words and a proposal of a maximum of 2,500 words

Your statement of purpose/personal statement and research proposal should be submitted as a single, combined document with clear subheadings. Please ensure that the word counts for each section are clearly visible in the document.

Personal statement

Your statement should explain your motivation for applying for the DPhil course at Oxford and the specific research areas that interest you and/or you intend to specialise in. It should focus on your academic achievements and research interests rather than personal achievements, interests and aspirations. You should also include details of any relevant experience in engaging in social data science related research.

Your statement should be written in English and be a maximum of 500 words.

If possible, please ensure that the word count is clearly displayed on the document.

Your statement will be assessed for:

• interest and commitment for the study of social data science;
• evidence of aptitude for working with data-driven research; and
• alignment of your areas of interest with the availability of supervision, as all students will be assigned a supervisor to guide their research.

Research proposal

A coherent thesis proposal is required in an area of study covered by at least one member of the research staff within the Social Data Science programme. Your proposal should focus on specific research you propose to undertake rather than personal achievements, interests and aspirations.

The proposal should be submitted in English only and be a maximum of 2,500 words. The word count does not need to include any bibliography or brief footnotes.

Your research proposal will be assessed for:

• the coherence of your proposal;
• the relevance of the topic as it relates to the research of the Oxford Internet Institute and collaborating department;
• the clarity of research question(s), and the knowledge gap the proposal intends to fill;
• the appropriateness of the methods and research design as related to the research question(s); and
• the overall quality of the project proposed.

It is normal for your ideas to change in some ways as you commence your research and develop your project. However, you should make the best effort you can to demonstrate the extent of your research question, sources and method at this moment.

Written work: One essay of a maximum of 2,000 words

An academic essay or other writing sample from your most recent qualification, written in English, is required. If you have not previously written on areas closely related to the proposed research topic, you may provide written work on any topic that best demonstrates your academic abilities. The written work does not need to be data science related, but should demonstrate your critical and analytical capabilities and ability to present ideas clearly. 

The word count does not need to include any bibliography or brief footnotes. Extracts of the required length that originally come from longer essays are also acceptable.

This will be assessed for:

• a comprehensive understanding of the subject area, including problems and developments in the subject;
• your ability to construct and defend an argument;
• your aptitude for analysis and expression; and
• your ability to present a reasoned case in proficient academic English.

Start or continue your application

You can start or return to an application using the relevant link below. As you complete the form, please  refer to the requirements above  and  consult our Application Guide for advice . You'll find the answers to most common queries in our FAQs.

Application Guide   Apply - Full time Apply - Part time

ADMISSION STATUS

Closed to applications for entry in 2024-25

Register to be notified via email when the next application cycle opens (for entry in 2025-26)

12:00 midday UK time on:

Friday 5 January 2024 Latest deadline for most Oxford scholarships Final application deadline for entry in 2024-25

*Three-year average (applications for entry in 2021-22 to 2023-24)

Further information and enquiries

This course is offered by the Oxford Internet Institute

• Course page on the institute's website
• Department open days
• Funding information from the institute
• Academic and research staff
• Research at the institute
• Social Sciences Division
• Residence requirements for full-time courses
• Postgraduate applicant privacy policy

Course-related enquiries

Advice about contacting the department can be found in the How to apply section of this page

✉ [email protected] ☎ +44 (0)1865 287210

Application-process enquiries

See the application guide

Other courses to consider

You may also wish to consider applying to other courses that are similar or related to this course:

View related courses

Visa eligibility for part-time study

We are unable to sponsor student visas for part-time study on this course. Part-time students may be able to attend on a visitor visa for short blocks of time only (and leave after each visit) and will need to remain based outside the UK.

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• Career Guide

Important Facts

Ask any Question - CV Forum

Pros & Cons: Full Time vs. Part Time PhD - Which Is Better?

Kopal Srivastava Aug 23, 2024 1K Reads

Studying for a PhD full-time pays you more. It allows you to focus more on your research, and with more time, libraries, research labs, and easier access to university resources such as faculty support, students can always align their programs with university activity variety and attend seminars and workshops that provide them with a learning experience.

In contrast, part-time students are more likely to face distractions from work or other responsibilities, which can lead to delays in their progress and thesis completion. One of the main advantages of a full-time PhD is that a Part-time PhD takes seven years whereas Full-time PhD only takes 3-5 years to complete it.

This early completion allows full-time PhD holders to enter the workforce earlier, find employment, and advance their careers faster. Their benefits include the prestigious title of "Dr," the possibility of higher salaries, and prior opportunities in selected fields.

Overall, a full-time PhD program provides a focused and efficient path to academic and professional success.  This guide will help you look at the good and bad points of both options, so you can pick the one that fits your aims and how you live best.

Part-Time vs. Full-Time PhD: Which One Is Right for Me?

Going back to school when you're already working is a big choice if you're thinking about a PhD. You've got to figure out how you'll juggle your time and job. The key difference between full-time and part-time PhD programs? It's how long it takes to finish. The coursework and what you need to do are often the same.

What is the Difference between a Full-time PhD and a Part-time PhD Program? 

The main difference between part-time and full-time PhD programs is how long it takes to finish the degree. Both need the same research and study. Still, the experience can change a lot:

Full-time PhD students can spend more time on their studies and research.Part-time students might have other things to do like work or family that compete with their coursework. Money help chances can change based on whether you work full-time or part-time.

Full-time students might get to dive deeper into their research. Picking the best program depends on how well you can balance your school with other parts of your life. Let's look at what full-time and part-time PhD programs give you to help you make a smart choice.

Everything about a Full-Time PhD Program?

A full-time PhD program is like a full-time program, in which you have to visit college daily and you can even choose a specialization of your choice, expanding your understanding of a particular subject matter and schooling you in studies and collaboration competencies. Here's an average definition of what to anticipate:

• Coursework: You'll begin with graduate publications in your field, including studies methods and scholarly writing, to build your expertise base and prepare for the writing you'll do later.
• Research: Outside of training, you'll spend loads of time studying your dissertation, the using the abilities discovered in coursework to discover sources, conduct experiments, or other tasks.
• Meetings: Regular meetings together with your PhD manager are important. These periods are for discussing your development, reviewing research, and getting recommendations.
• Teaching or Fieldwork: Depending on your software, you are probably required to train instructions, entire an internship, or do fieldwork.
• Writing the Dissertation: The dissertation is the culmination of your paintings, combining all of your research, evaluation, and writing. It can take years to finish.
• Defending the Dissertation: After completing your dissertation, you may guard it in an oral exam, supplying your studies and answering questions from a committee. They decide if you've surpassed or if changes are wished.After effectively protecting your dissertation and getting committee approval, you may publish the final version and obtain your diploma.

Course Work 

Each PhD program varies depending on your field and institution or university, but all student information should always be as follows.

• Year 1: You will have a full semester of academic work, including core courses in your field and courses in general research design and methods.
• Year 2: This year includes semesters of study, including further research and development of your thesis proposal.
• Year 3: As you begin researching and writing your dissertation, your course load decreases. If necessary, you may participate in teaching, clinical, or laboratory work.
• Fourth-year and above: You may have a few classes left, but you spend most of your time researching and writing until you finish your thesis.

Commitment 

A complete-time PhD program is in-depth, requiring about 35 to 40 hours in line per week, similar to a complete-time process. Most of the time in the first two years may be spent inside the lecture room. After that, you'll give attention to researching, writing, and finishing other crucial obligations.

What is the Duration?

In 2020, there were 55,283 completed PhDs in the U.S., with an average completion time of 5.8 years, according to the National Science Foundation. Most PhD programs take at least 4 years, but it can take longer, even for full-time students.

How long it takes to finish depends on factors like:

• Struggles with research
• Writing delays
• Extensive revisions needed for your dissertation
• Life circumstances affecting your studies

Remember, taking longer to finish doesn’t lessen your achievement. If you need extra time or revisions, don’t get discouraged.

A Full-Time PhD Program Could Be a Good Fit If..

Full-time PhD programs are a big time commitment. They might be ideal for students who:

You can start full-time and switch to part-time later if your situation changes.

Everything About A Part-Time PhD Program? 

Part-time PhD programs offer students more flexibility. Each student's experience may differ, but common traits include:

• More flexible coursework
• Spread-out costs, reducing the immediate financial burden
• Less disruption to other life commitments
• More time for research

In a part-time program, you have more time for other obligations, but it means being a student for a longer period. The workload is the same as in a full-time program, just spread over more years.

Key differences include:

• Classroom time: You take the same classes but may take three or four years to complete core work instead of one or two.
• Weekly hours: Part-time students spend 15 to 20 hours per week on schoolwork, though this can vary.
• Years to complete: Completion time can range from 5 to 10 years or more.

Part-time PhD programs are variable, especially in terms of how long they take to complete.

Duration of Completion of Part-Time PhD Program

A part-time PhD usually lasts between five and eight years, but this time depends on how much time the university gives you and how much work you put in.

You may end up with more time than you originally thought sooner, or your work and life balance may get in the way So it takes longer.A full-time PhD usually takes three to four years. However, the title period can actually be extended up to four years.

How many hours a week is a part-time PhD?

A part-time PhD usually takes five to eight years, depending on the university's timeline and your effort. You might finish sooner if you have more time than expected, or it might take longer if work and life balance are challenging.

A full-time PhD takes three to four years. However, the thesis deadline can sometimes be extended for up to four years.

A Part-Time PhD Program Might Be Right For You If…

Many students find the flexibility of a part-time PhD program beneficial. You might be a good fit if you:

You can start slowly and gradually take on more work as your circumstances allow.

What Are The Pros And Cons Of Studying A Full-Time Ph.D. program?

Full-time Ph.D. It usually takes three to five years, with lots of research and reading. It offers a full-time Ph.D. The program:

• Research and in-depth understanding: Full-time PhD Students can spend more time researching, which gives them a deeper understanding of the subject. This immersion helps them develop specialized knowledge and skills.
• Frequency of contact with faculty: Being on campus full-time means that students can have frequent contact with their faculty and mentors. This constant interaction provides opportunities to ask questions, seek guidance, and clarify doubts, enhancing the learning experience.
• Access to University Facilities: Full-time students have excellent university facilities such as laboratories and libraries. This process is important for conducting research, obtaining research materials, and other resources needed to learn.
• Early graduation: Always PhD The program can be completed sooner than half-time. Students can focus solely on their studies and may graduate in three to five years.
• Degree value increases: Full-time PhD It seems more radical and comprehensive. This concept can add value to a degree, making it more attractive to employers and educational institutions.

While a full-time Ph.D. While this program has many benefits, it also comes with some demanding situations:

• Lost wages: Being a complete-time student often costs you money because you will be focusing on your research more and would not earn a penny, which can price cash. This can be difficult, in particular when you have financial obligations.
• Duration: Full-time Ph.D. A massive time dedication is needed. You spend hours gaining knowledge of, reading and writing, which can be annoying and leave little time for other things.
• High fees: The costs associated with a complete-time PhD, which include lessons, books, and living prices, can upload up. Without consistent profits, managing those charges may be tough and motive economic stress.
• Emotional challenges: Full-time Ph.D. Sometimes emotions of loneliness, anxiety, and despair get up. Seeing buddies develop their careers while you have a look at them can be hard and have an effect on your intellectual well-being. It is important to be prepared for these emotional ups and downs and try to find help when needed.

What Are The Pros And Cons Of Studying A Part-Time Program?

Let’s start with the positives of a part-time Ph.D.

• More manageable finances: While not cheap, a Ph.D. They are usually very manageable. You pay a lower annual fee, spread over several years, making it easier to balance a part-time job. This process also gives you time to apply for funds that may be available during your study period.
• Less stressful: Part-time PhD Program makes it easier to maintain personal and professional commitments. This is especially useful if you are balancing work or family obligations such as pursuing a full-time Ph.D. It can be overwhelming in situations like this.
• Transition : Part-time PhD Roles provide flexibility. You can adjust your workload to suit your needs, such as working harder to meet deadlines or slowing down when necessary. When circumstances change, they can sometimes switch to full-time study.
• More opportunities: The longer you stay in the program, the more opportunities you can encounter, such as seminars, publications, and collaborative projects. This can enrich your learning experience, although it can also be difficult to keep up with changes in your career.
• Timeline: Part-time PhD It gives you more time to think about your career path. You may discover new interests or extracurricular career opportunities that you wouldn’t have considered if you rushed through a full-time program.

Part-time and full-time Ph.D. It’s not straightforward. This would be an easy decision if part-time PhDs were always good, but they are not. Here are some of the reasons:

• Probably not possible: Not all fields or organizations offer part-time Ph.D. The options you have. While this is common in the humanities, part-time courses in other fields are not, especially for self-funding students. International students should also check visa requirements, as some countries only issue student visas for full-time programs.
• Duration: Part-time Ph.D. It often takes longer to complete, which can delay your career progress. This extended timetable can also make it difficult to keep up with your research, as other, more important aspects of life compete for your attention. Over the course of my career, I faced a difficult period where I almost stopped highlighting how difficult it is to stay focused for long periods of time.
• Life happens: When you enroll in a  part-time Ph.D. At first, it doesn’t seem like much of a problem, and life events can affect your learning over time. Later, you will deal with a variety of distractions, from marriage to petting cats to learning to drive. These events sometimes raised eyebrows for full-time students when their path seemed more straightforward.
• FOMO is Real: Part-time PhD Students may experience a sense of loss (FOMO) when they see their full-time peers making rapid progress. This can be frustrating and can lead to feelings of cheating syndrome. It should be remembered that Ph.D. Learning is not a sprint, and seeking help when needed is normal and encouraged.

What Are The Other Types Of A PhD Program? 

There are various types of a PhD programs. Some of them are mentioned below.

PhD programs come in a variety of forms. A selection of them are listed below:

• Regular PhD Program: Regular PhD Program requires regular attendance and takes a minimum of three to five years to finish. In addition, if you wish to choose a traditional PhD program, you must pass an entrance exam. Moreover, you must devote all of your attention to your research.
• PhD Program on a  Part-Time Basis: Part-Time PhD program is specially personalized for professionals who work full-time. You must show up for evening classes. Part-time PhDs are awarded to candidates who work in reputable research organizations, academic institutions, or businesses close to the school. While students must complete the same academic courses and criteria as full-time students, a part-time PhD takes 7-8 years to accomplish. Only a few classes are needed to be attended by PhD candidates who work part-time. They need to have a NOC from their employer and a minimum of one year of work experience. The focus of a part-time Ph.D. program is on corporately beneficial research as opposed to individual studies.
• Online PhD Program : Although online PhD programs are still not approved by the UGC, you can still choose to pursue an online DBA (Doctor of Business Administration) as an alternative. The curriculum, which can be finished in a minimum of three to five years, is also for doctorates. Moreover, you do not need to attend college to pursue it.
• PhD Programs for Professionals in the Workforce: Pursuing a Ph.D. while holding a full-time job could be challenging. However, many universities offer flexible curriculum choices for working individuals, known as PhD for working professionals. These classes help students balance their studies and careers. You must have job experience, a valid GATE/NET score (if applicable), and a Master's degree with at least 60% to be eligible for this PhD program for working professionals.
• PhD Global:   A Global PhD is a PhD for working professionals that can be pursued from a foreign university. It is a little different from a PhD program offered in an Indian university in terms of eligibility criteria, selection process, fees, syllabus, and job roles. In this mode, you have to pass the entrance exam, and once you pass it, only then you can apply for a PhD Global program.
• PhD Abroad: The duration of a Ph.D. abroad is three to six years, with ample chances. To get admitted, candidates must pass entrance examinations such as the GMAT, SAT, GRE, TOEFL, and IELTS. Ph.D. programs are offered by prestigious universities in the United States, United Kingdom, Germany, Australia, Japan, Canada, Singapore, and France.Integrated PhD Program This integrated doctoral program is comprehensive and incorporates both a master’s and a doctorate program. The duration to complete it is a minimum of 5-6 years. You can apply for this program right after your bachelor’s.

PhD regulations for offering a PhD Part-Time PhD Program

Universities in India have rules for part-time PhDs, but there are some common criteria. Part-time PhD programs are usually for those who want to work or have other significant commitments. Students must meet the same academic requirements as full-time students, including a valid master’s degree and a good academic record.

The length of time required to complete a part-time PhD is longer than a full-time program, usually lasting six to eight years. Universities may require part-time students to attend campus events, such as seminars or workshops, over a period of time.

In addition, applicants must seek permission from their employers if they are employed and provide evidence that their PhD research can be used to supervise their employment. Students should consult with specific university regulations, as requirements and policies may vary. 

Is the Part-Time PhD Program Recognised by the UGC (University Grants Commission)? 

Yes, part-time PhDs are valid and recognized by the University Grants Commission (UGC) of India. The UGC allows universities to offer part-time PhD programs, provided they meet certain quality criteria and guidelines.

These programs are particularly suitable for working professionals who are unable to commit to a full-time study program. A key requirement is that research and learning standards must be equivalent to a full-time program.

Students in part-time PhD programs must therefore meet all necessary criteria, including coursework, thesis, and dissertation, just like their full-time counterparts.

PhD Through an Online or a Distance Mode

No university or college can offer PhD programs through distance education or online mode, according to the current regulations. However, candidates who are already employed can pursue a PhD, as long as they meet all the eligibility requirements specified in the existing PhD regulations. Click on the link to check it on the official website of the  UGC. 

Is a Part-Time PhD valid for an Assistant Professor? 

Yes, a part-time PhD is suitable and valid for those aiming to become an assistant professor. Many universities and colleges accept part-time PhDs, as long as the degree meets both academic and research requirements.

The key is to ensure that a part-time PhD program complies with university rules and standards. Universities generally look for strong research candidates, regardless of whether they completed their PhD full-time or part-time.

However, some institutions or universities may have additional or preferred criteria for part-time PhD holders, so it’s a good idea to check with specific university programs. Moreover, although a part-time PhD is valid, it can take longer to complete compared to a full-time program, which can affect the time you can apply for positions.

Balancing research and other commitments can be a challenge, but many successful teaching assistants completed their PhDs part-time. What ultimately matters most is the research you do and how you can support education.

Guidelines of Admission in PhD 2024 

According to the recent updates, the NET score will be counted for PhD admission. Now, universities do not need to take their own entrance exams.

They have grouped NET scores into three categories. These three categories are: 

• Award for JRF & Assistant Professor Appointment 
• Promotion to Assistant Professor and PhD Admission
• PhD Admission Only

Scope of PhD Program

Below is information on PhD salaries in India for Professors, Associate Professors, and other positions. The table shows the salaries of PhD Professors in India.

What are the Guidelines for Change of Registration from Part-Time to Full-Time Ph.D. Program? 

If a student in a part-time PhD program wishes to pursue full-time study, an application may be made at the beginning of the semester.

This requires the approval of the Doctoral Progress Committee (DPC) and the Dean of Research/PhD. The student can also leave their job for at least two years to focus on their research after completing the course.

The student is required to provide two Certificates of Objection (NOCs):

One is from their current employer so that they can become a full-time research scientist at the institution where their supervisor is located.

If it is not a PG college/institution affiliated to the University, where the invigilator is working, it gives access to the centers.

If the student is unemployed and unable to file an NOC, he/she should be given an undertaking to work as a full-time research scholar in the institution of the sponsor for a minimum of two years.

Conversion from a part-time to a full-time position is permitted only once during the PhD program. During this process, the student cannot change his/her discipline, branch, or research topic. Once the transition to full-time status has been approved, the student must follow all rules and guidelines for full-time research scholars.

To calculate study time, half the time spent as a part-time student counts as full-time study.

Is there an Alternative for a PhD Program? 

Yes, there is an alternative to a PhD Program, and the alternative is a DBA (Doctor of Business Administration) It is also a doctorate program that can be completed in a minimum of 3 to 5 years.

Moreover, if you opt for an online DBA, you will be able to earn your doctorate degree from an international university without even relocating to another country.

Furthermore, you even get an opportunity to study from an international faculties who have years of experience and have published their work in top journals, they guide you to the best.

Moreover, there are many universities offering you an online DBA. Some of them are mentioned below :

Conclusion 

Studying a PhD full-time pays off big time. It allows you to focus more on your research, and with more time, libraries, research labs, and easy access to university resources like faculty support, students can always align their programs with university activities and attend seminars and workshops that provide them with learning experiences. In contrast, part-time students are more likely to face distractions from work or other responsibilities, which can delay their progress and dissertation completion One of the major advantages of a full-time PhD is that a part-time PhD takes seven years while a full-time PhD takes only 3-5 years.

FAQs (Frequently Asked Questions)

⭐ is a full-time phd more valuable than a part-time phd.

Both full-time and part-time PhDs have similar value in terms of credibility. The main difference is that a full-time PhD can be completed faster, which may offer some advantages in career progression. However, both types can lead to similar job opportunities and salaries.

⭐ Which is better, a full-time or part-time PhD?

The credibility of both degrees is the same. A full-time PhD  takes three to five years, while a part-time PhD takes longer. The best choice depends on your personal circumstances and commitments.

⭐ Do you get paid for a part-time PhD?

If you qualify with JRF (Junior Research Fellowship) and Assistant Professorship, you can choose to do a full-time PhD with a stipend or work as an Assistant Professor while doing a part-time PhD without a stipend.

⭐ Can I convert my part-time PhD to a full-time PhD?

Yes, you can switch from part-time to full-time PhD at the beginning of a semester. This requires a recommendation from the Doctoral Progress Committee (DPC) and approval from the Dean of Research/PhD. You must also be able to leave your job for at least two years to focus on your research.

⭐ Is a part-time PhD valid?

Yes, a part-time PhD is recognized as valid according to UGC guidelines. However, you need a "No Objection Certificate" from your employer and must complete at least six months of coursework full-time.

⭐ Is a part-time PhD difficult?

A part-time PhD is not easier than a full-time PhD. Both require significant effort, commitment, and passion to complete. The main difference is the flexibility in scheduling.

⭐ Do we get JRF in a part-time PhD?

No, the UGC JRF fellowship is only available for full-time PhD students. A part-time PhD does not come with a stipend but is more suited for working professionals who want to continue their job while studying.

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Data science

Admissions & Financial Aid

Within this page, you can find information about how to apply for the Master of Science in Data Science or Foundations in Data Science Graduate Certificate.  Review program requirements, as well as up-to-date application deadlines. Additionally, this page contains information on the financial aid opportunities available to online graduate students at Purdue University.  Interested in pursuing both degree options?  Connect with our enrollment specialist to learn more about this option prior to applying. 

Ready to dive into the world of data science?    

Admission Requirements: Master of Science in Data Science

Interested applicants will complete an  online application  with the Purdue University Office of the Vice Provost for Graduate Students and Postdoctoral Scholars. You must submit all the items below to be considered for the Master of Science in Data Science. These documents are reviewed holistically to select well-qualified and prepared students for the program.    Admission Requirements 

• Cumulative GPA of 3.0 or equivalent 
• A bachelor’s degree from an accredited institution  
• Professional resume  
• One (1) letter of recommendation
• Official transcripts from all universities attended, including transfer credits.  
• International Students only: Submit a copy of each diploma received in the original language and English translation.  

The GRE or GMAT exam is not required for admission. Applicants who have taken the GRE or GMAT exam may upload their scores into the online application system for consideration.

Recommendations for Success:  

It is recommended that applicants show successful completion of the following academic coursework, or professional credential:   

• Statistics   
• Linear Algebra
• Probability Theory  
• Programming Languages  

Admission Requirements: Foundations of Data Science

Interested applicants will complete an online application with the Purdue University Office of the Vice Provost for Graduate Students and Postdoctoral Scholars. You must submit all the items below to be considered for the Foundations in Data Science Graduate Certificate. These documents are reviewed holistically to select well-qualified and prepared students for the program.   Once you have started your application, select Foundations of Data Science as your Program of Interest.   Admission Requirements 

Application Deadlines and Start Dates

The MS in Data Science and the Foundations of Data Science Graduate Certificate follow the same application deadlines and program start dates.  

Cost and Financial Assistance Section

At Purdue, we understand that financial assistance is an essential part of starting and completing a degree. We work with our students to make the payment process as easy as possible. Explore the following options to see if they are right for you. 

* The cost of attending Purdue varies depending on where you choose to live, enrollment in a specific program or college, food and travel expenses, and other variables. The  Office of the Bursar  website shows estimated costs for the current aid year for students by semester and academic year. These amounts are used in determining a student’s estimated eligibility for financial aid. You can also use our  tuition calculator  to estimate tuition costs.  

Financial Assistance 

There are  financial assistance options  to consider to help defray the costs of enrolling in a graduate professional program. These include Stafford loans, graduate PLUS loans,  military programs  and loans from private lenders. Through myPurdue, you can monitor your financial aid funding and make sure the proper payments are made in a timely fashion.  

Purdue Office of the Bursar

The  Purdue Bursar’s Office  provides options for payment of fees such as installment plans and third-party payments. Visit the  Purdue Bursar’s Office  for more details. 

Ready to Become a Boilermaker?

Are you ready to join the Purdue  innovators  and  changemakers  always striving to make giant leaps forward in our industries and fields? Start your application today!

You are not alone in taking your next giant leap. Get your questions answered, receive application help, or plan your degree journey by speaking with an enrollment counselor. Request more information today.