data science phd programs in usa

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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

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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

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PhD in Data Science

Students conduct research on cutting edge problems alongside preeminent faculty at UChicago and explore the emerging field of Data Science. As an emerging discipline, Data Science addresses foundational problems across the entire data life cycle. Tackling issues of inequity, climate change, and sustainability will require cutting edge research in artificial intelligence and data usage combined with innovative educational programs to train students in the concepts of information systems. Students of Data Science will not only immerse themselves in a rapidly evolving field; they will help redefine it altogether.

Research Excellence:

As a PhD student in Data Science, you will learn from faculty who have developed research programs that span a wide variety of data science and AI topics, from theory to applications, with a focus on making a societal impact.

Research Topics:

• Artificial Intelligence
• Data, AI, and Society
• Data Systems
• Human-Centered Data Science
• Machine Learning and Statistics
• Use-Inspired Data Science

For more information, including a link to the application, see the Committee on Data Science website .

Admissions criteria for the Ph.D. in Data Science

Admissions Considerations 

We're crafting a community of scholars who are: 

• Intellectually curious and prepared to engage in the intensive study of data science  
• Proficient in high-level quantitative and technical skills  
• Passionate about understanding how data is captured and communicated to make informed decisions 
• Motivated, studious, and mature; willing to contribute to our diverse community as teaching assistants, researchers, and colleagues

Who should apply?

As the nation's first standalone School of Data Science to offer a Ph.D. program, we are seeking candidates who wish to study cutting-edge methods for learning from data or the impact of data driven decisions on society. Ph.D. students are expected to be the next generation of data science researchers and experts, contributing to and pushing the discipline forward.

The Ph.D. in Data Science is a rigorous program, and applicants must demonstrate their ability to succeed in our educational environment. We are seeking candidates from a variety of majors, disciplines, and backgrounds, so no specific program of study is required. A master’s degree is not required and does not make an applicant more competitive in the admissions process. However, completion of prerequisite courses is required prior to matriculation.

Doctoral students typically receive financial support from the School of Data Science for the duration of their enrollment in the program, pending satisfactory completion of requirements and progression through the program. This includes full remission of tuition, fees, and the premium for single-person coverage through the University of Virginia’s student health insurance plan.  Tuition and fees  are set annually by the University of Virginia Board of Visitors in early spring. In addition, Ph.D. students will receive either a fellowship, research assistantship, or teaching assistantship that includes annual living support in the approximate amount of $35,000 per year, distributed over 12 months. 

Prerequisite Courses and Minimum Qualifications

We welcome applicants from all undergraduate majors or programs of study who have earned their bachelor's degree prior to matriculation from a three- or four-year accredited institution.

The Ph.D. in Data Science program requires several prerequisite courses. You can still apply without having all prerequisites, but they must be completed prior to matriculation. Proof of completion will be required for any incomplete prerequisites if an applicant is admitted and accepts their offer of admission. 

The following are required upon matriculation:

Multivariable Calculus

• A course or courses from an accredited college or university that covers concepts through multivariable calculus and functions in more than one dimension. In the U.S., this is typically a three-course sequence (Calculus I, Calculus II, Calculus III).

Matrix Algebra or Linear Algebra

• Evidence of proficiency in matrix algebra via a linear algebra or similar mathematics course from an accredited college or university, or completion of Linear Algebra for Data Scientists ( NEW PROGRAM offered online by the School of Data Science ).
• At least one course from an accredited college or university that covers concepts in probability and statistical inference. 

Programming Experience 

• This experience can be demonstrated by completion of a course in computer science from an accredited college or university or substantial experience working with a programming language (such as Python, R, Matlab, C++, or Java). We will ask you to detail this experience in your application.

Application Requirements

The following materials are required for review of your application: 

Essay and Short Answer Prompts

Please familiarize yourself with how the School of Data Science organizes its curriculum using the Domains of Data Science Model . What are the practice areas that currently ignite your enthusiasm and curiosity, and which domains or specialized areas do you aspire to focus on during your graduate training? (500 word limit) 

In addition to your statement, please answer the short supplementary questions below:

Required: Our students are expected to push the boundaries of data science with their research. In what ways do you want to shape the practice of data science? (250 word limit)    Required: What about your individual background, perspective, or experience will serve as a source of strength for you or those around you at UVA?  Feel free to write about any past experience or part of your background that has shaped your perspective and will be a source of strength, including but not limited to those related to your community, upbringing, educational environment, race, gender, or other aspects of your background that are important to you. (250 words or fewer)   OPTIONAL: An application addendum is not required; however, some applicants may have additional information that would be useful for the admissions committee. Use this space to discuss positive dimensions of your background not otherwise highlighted in this application, or to provide insight and/or context regarding other aspects, including, but not limited to, aberrant grades, gaps in your academic record or resume, or other extenuating circumstances. (250 words or fewer) 

Please upload an audio file of yourself answering the following prompt: In your experience, what differentiates data science from other fields? (45 seconds maximum)   

Transcripts

Upload all unofficial transcripts from your entire post-secondary academic record, including all undergraduate- and graduate-level coursework. 

• If you earned an undergraduate degree from an institution (or institutions) in which English is the primary language, unofficial transcripts are sufficient. 
• If you earned an undergraduate degree from an institution (or institutions) in which English is not the primary language of instruction, a course-by-course credential evaluation is required to help us better understand your educational background and capabilities. IEE and  SpanTran  are NACES-member companies that have created custom applications for the School of Data Science at a discounted rate. We accept transcript evaluations from all  NACES -member evaluators.

If admitted to the School of Data Science and you decide to accept your offer, you will be required to submit official transcripts and proof of degree conferral prior to enrolling. Send official transcripts electronically to [email protected] or by mail to:

Mailing Address   School of Data Science Admissions  P.O. Box 401109  Charlottesville, VA 22904

Physical Address (for DHL and FedEx)   School of Data Science Admissions  Room 232  1001 Emmet Street North  Charlottesville, VA 22903

Standardized Tests   The Ph.D. in Data Science does not review standardized test scores (i.e.,  GRE , GMAT , MCAT ) in its holistic evaluation of applicants.

English Language Tests

• If you earned an undergraduate degree from an institution (or institutions) in which English is the primary language, you need not submit English Language Test scores, and the TOEFL / IELTS requirement will be automatically waived.
• If you earned an undergraduate degree from an institution (or institutions) in which English is not the primary language of instruction, you must self-report TOEFL or IELTS scores. Official scores are only required if admitted to the program. TOEFL and I ELTS scores are valid for two years after test date.

TOEFL   Send official TOEFL scores via ETS to institution code B875 (no department code needed). The minimum TOEFL (iBT) score requirement is 100 (including minimum section scores of 22 in speaking, 22 in writing, 23 in reading and 23 in listening). 

IELTS   Request that your test scores be sent electronically via the IELTS system by contacting your IELTS center directly. No paper Test Report Forms will be accepted. No institution code or department code is needed. The minimum IELTS score requirement is 7.0 (including minimum section scores of 6.5). 

Letters of Recommendation   Three letters of recommendation are required as part of the online application. Once you have saved the contact information for a reference, the individual will receive email instructions to submit a letter of recommendation on your behalf. It is preferable (but not required) for at least two letters to be from an individual with substantial knowledge of your academic accomplishments.

Curriculum Vitae  

Your Curriculum Vitae (CV) will demonstrate your preparedness for graduate study, including involvement in activities outside of school or work (such as leadership, service, civic engagement), coding or research projects, and any other accomplishments you’d like to share. You can find guidance about your CV here .

Application Fee   A non-refundable application fee of $85 is required prior to submission of the application. We do not grant individual fee waiver codes outside of the following exceptions. 

Fee Waivers (U.S. Citizens and Permanent Residents)  

Per UVA policy, we provide application fee waivers for US citizens and permanent residents who meet the  Application Fee Waiver Eligibility  criteria and submit an  Application Fee Waiver Request Form . In addition, application fees are waived for full-time UVA employees who have worked more than 90 days at the University (contact the  admissions team  for the employee fee waiver code). You cannot be granted a fee waiver after payment of the application fee. An application will not be considered submitted until the fee is paid. 

Fee Waivers (International Applicants)

The School of Data Science is committed to diversity which we define as excellence expressing itself through every person’s perspective and lived experience. 

We acknowledge that financial disparities exist that may discourage prospective students from applying. In hopes of creating and increasing access to our graduate programs, we currently provide application fee waivers to: 

• Recipients of DACA, Deferred Enforced Departure, or Temporary Protected Status (TPS). Please contact Degi Betcher if you meet this eligibility.

Citizens from the countries listed below. The application system will automatically waive the payment based on the citizenship you select on the application. International applications from countries not listed as LDCs will not be eligible to receive need-based application fee waivers.

• Afghanistan
• Burkina Faso
• Central African Republic
• Democratic Republic of the Congo
• Guinea-Bissau
• Lao People's Democratic Republic
• Sao Tome and Principe
• Sierra Leone
• Solomon Islands
• South Sudan
• Timor-Leste
• United Republic of Tanzania

For technical questions about the application, contact  [email protected] . For all other questions,  connect  with us.

International Applicants

The School of Data Science values the backgrounds, perspectives, and experiences that our international students contribute to our community, and we welcome applications from around the world. 

The following information is provided to help prospective international students through the Ph.D. in Data Science application process. All students are considered in the same pool regardless of the prospective student’s citizenship and the country of the student’s undergraduate institution. 

International applicants must hold a bachelor's degree from an accredited institution equivalent to a four-year American degree. The School of Data Science will also recognize undergraduate degrees that were earned in three years of study. 

Prerequisites & Transcripts 

International applicants who completed the prerequisites under a different title (e.g., instead of Calculus III, the course is listed as Mathematics IV on your transcript) must submit a course description and/or syllabus of the course to confirm the completion of the prerequisites. Please note that transcripts may be unofficial for the application, but all enrolling students must submit official transcripts upon matriculation.

OPT/STEM OPT 

Ph.D. graduates will be eligible to apply for Optional Practical Training (12 months) and consecutively STEM OPT Extension (24 months).

F-1 Visa/I-20 

Admitted international applicants will be required to submit supplemental documentation to the International Student & Scholars Program confirming the financial resources available for their study in the United States. Timely submission of the I-20 request form and financial forms will be important in receiving the I-20 form and applying for the F-1 student visa or transferring the SEVIS record. Connect with the International Student Office with all questions related to student visas & immigration statuses. Do not submit financial documentation with your application. 

Financial Aid 

Admitted applicants will receive financial support from the School of Data Science for the duration of their enrollment in the program, pending satisfactory completion of requirements and progression through the program. This includes full remission of tuition, fees, and the premium for single-person coverage through the University of Virginia’s student health insurance plan. Read more on the Tuition and Fees page . 

Health Insurance 

All UVA students, both domestic and international, charged the full comprehensive fees with their tuition must meet the health insurance verification requirements. Read the details of the health insurance requirements here . The health insurance premium will be covered under the financial aid package.

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PhD in Data Science

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 finally four other electives that can come from proposed courses in data science or existing graduate courses in Computer Science or Statistics. Some students, after consulting with the committee graduate 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.

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 hourlong 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 following a successful defense and the electronic submission of the final version of the dissertation to the University’s Dissertation Office.

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PhD Program

Requirements for doctor of philosophy (ph.d.) in data science.

The goal of the doctoral program is to create leaders in the field of Data Science who will lay the foundation and expand the boundaries of knowledge in the field. The doctoral program aims to provide a research-oriented education to students, teaching them knowledge, skills and awareness required to perform data driven research, and enabling them to, using this shared background, carry out research that expands the boundaries of knowledge in Data Science. The doctoral program spans from foundational aspects, including computational methods, machine learning, mathematical models and statistical analysis, to applications in data science.

Course Requirements

https://datascience.ucsd.edu/graduate/phd-program/phd-course-requirements/ 

Research Rotation Program

https://datascience.ucsd.edu/graduate/phd-program/research-rotation/

Preliminary Assessment Examination

The goal of the preliminary assessment examination is to assess students’ preparation for pursuing a PhD in data science, in terms of core knowledge and readiness for conducting research. The preliminary assessment is an advisory examination.

The preliminary assessment is an oral presentation that must be completed before the end of Spring quarter of the second academic year. Students must have a GPA of 3.0 or above to qualify for the assessment and have completed three of four core required courses . The student will choose a committee consisting of three members, one of which will be the HDSI academic advisor of the student. The other two committee members must be HDSI faculty members with  0% or more appointments; we encourage the student to select the second faculty member based on compatibility of research interests and topic of the presentation. The student is responsible for scheduling the meeting and making a room reservation. 

The student may choose to be evaluated based on (A) a scientific literature survey and data analysis or (B) based on a previous rotation project. The student will propose the topic of the presentation. 

• If the student chooses the survey theme, they should select a broad area that is well represented among HDSI faculty members, such as causal inference, responsible AI, optimization, etc. The student should survey at least 10 peer-reviewed conference or journal papers representative of the last (at least) 5 years of the field. The student should present a novel and rigorous original analysis using publicly available data from the surveyed literature: this analysis may aim to answer a related or new research question.
•  If the student chooses the rotation project theme, they should prepare to discuss the motivation for the project, the analysis undertaken, and the outcome of the rotation. 

For both themes, the student will describe their topic to the committee by writing a 1-2 page proposal that must be then approved by the committee. We emphasize that this is not a research proposal. The student will have 50 minutes to give an oral presentation which should include a comprehensive overview of previous work, motivation for the presented work or state-of-the-art studies, a critical assessment of previous work and of their own work, and a future outlook including logical next steps or unanswered questions. The presentation will then be followed by a Q&A session by the committee members; the entire exam is expected to finish within two hours. 

The committee will assess both the oral presentation as well as the student’s academic performance so far (especially in the required core courses). The committee will evaluate preparedness, technical skills, comprehension, critical thinking, and research readiness. Students who do not receive a satisfactory evaluation will receive a recommendation from the Graduate Program Committee regarding ways to remedy the lacking preparation or an opportunity to receive a terminal MS in Data Science degree provided the student can meet the degree requirements of the MS program . If the lack of preparation is course-based, the committee can require that additional course(s) be taken to pass the exam. If the lack of preparation is research-based, the committee can require an evaluation after another quarter of research with an HDSI faculty member; the faculty member will provide this evaluation. The preliminary assessment must be successfully completed no later than completion of two years (or sixth quarter enrollment) in the Ph.D. program. 

The oral presentation must be completed in-person. We recommend the following timeline so that students can plan their preliminary assessments:

• Middle of winter quarter of second year: Student selects committee and proposes preliminary exam topic.  
• Beginning of spring quarter of second year: Scheduling of exam is completed. 
• End of spring quarter of second year: Exam. 

Research Qualifying Examination and Advancing to Candidacy

A research qualifying examination (UQE) is conducted by the dissertation committee consisting of five or more members approved by the graduate division as per senate regulation 715(D). One senate faculty member must have a primary appointment in the department outside of HDSI. Faculty with 25% or less partial appointment in HDSI may be considered for meeting this requirement on an exceptional basis upon approval from the graduate division.

The goal of UQE is to assess the ability of the candidate to perform independent critical research as evidenced by a presentation and writing a technical report at the level of a peer-reviewed journal or conference publication. The examination is taken after the student and his or her adviser have identified a topic for the dissertation and an initial demonstration of feasible progress has been made. The candidate is expected to describe his or her accomplishments to date as well as future work. The research qualifying examination must be completed no later than fourth year or 12 quarters from the start of the degree program; the UQE is tantamount to the advancement to PhD candidacy exam.

A petition to the Graduate Committee is required for students who take UQE after the required 12 quarters deadline. Students who fail the research qualifying examination may file a petition to retake it; if the petition is approved, they will be allowed to retake it one (and only one) more time. Students who fail UQE may also petition to transition to a MS in Data Science track.

Dissertation Defense Examination and Thesis Requirements

Students must successfully complete a final dissertation defense oral presentation and examination to the Dissertation Committee consisting of five or more members approved by the graduate division as per senate regulation 715(D).  One senate faculty member in the Dissertation Committee must have a primary appointment in a department outside of HDSI. Partially appointed faculty in HDSI (at 25% or less) are acceptable in meeting this outside-department requirement as long as their main (lead) department is not HDSI.

A dissertation in the scope of Data Science is required of every candidate for the PhD degree. HDSI PhD program thesis requirements must meet Regulation 715(D) requirements. The final form of the dissertation document must comply with published guidelines by the Graduate Division.

The dissertation topic will be selected by the student, under the advice and guidance of Thesis Adviser and the Dissertation Committee. The dissertation must contain an original contribution of quality that would be acceptable for publication in the academic literature that either extends the theory or methodology of data science, or uses data science methods to solve a scientific problem in applied disciplines.

The entire dissertation committee will conduct a final oral examination, which will deal primarily with questions arising out of the relationship of the dissertation to the field of Data Science. The final examination will be conducted in two parts. The first part consists of a presentation by the candidate followed by a brief period of questions pertaining to the presentation; this part of the examination is open to the public. The second part of the examination will immediately follow the first part; this is a closed session between the student and the committee and will consist of a period of questioning by the committee members.

Special Requirements: Generalization, Reproducibility and Responsibility A candidate for doctoral degree in data science is expected to demonstrate evidence of generalization skills as well as evidence of reproducibility in research results. Evidence of generalization skills may be in the form of — but not limited to — generalization of results arrived at across domains, or across applications within a domain, generalization of applicability of method(s) proposed, or generalization of thesis conclusions rooted in formal or mathematical proof or quantitative reasoning supported by robust statistical measures. Reproducibility requirement may be satisfied by additional supplementary material consisting of code and data repository. The dissertation will also be reviewed for responsible use of data.

Special Requirements: Professional Training and Communications

All graduate students in the doctoral program are required to complete at least one quarter of experience in the classroom as teaching assistants regardless of their eventual career goals. Effective communications and ability to explain deep technical subjects is considered a key measure of a well-rounded doctoral education. Thus, Ph.D. students are also required to take a 1-unit DSC 295 (Academia Survival Skills) course for a Satisfactory grade.

Obtaining an MS in Data Science

PhD students may obtain an MS Degree in Data Science along the way or a terminal MS degree, provided they complete the requirements for the MS degree.

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PhD in Data Science

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Degree Requirements

Degree requirements for the PhD in Data Science can be found in the NYU bulletin –  Doctor of Philosophy in Data Science .

To be awarded the Ph.D. in Data Science, students must, within 10 years of first enrolling:

• Complete 72 credit hours while maintaining a cumulative grade point average of 3.0 (out of 4.0) each semester.
• Complete the teaching requirement  (for incoming students Fall 2020 and later) .
• Pass a Comprehensive Exam.
• Pass the Depth Qualifying Exam (DQE) by May 15 of their fourth semester.
• Complete all the steps for approval of their Ph.D. dissertation.

For more information on the Ph.D.  curriculum and requirements please visit the Ph.D. Student Handbook . Please note you will only be able to access the handbook through your NYU email address.

Required Course Information

Students must successfully complete the following courses by the end of their third semester unless otherwise stated or show evidence that they have taken equivalent coursework elsewhere. Recent course pages are linked below. Course descriptions can be found in NYU’s  Albert Course Search .

• DS-GA 2003 – Introduction to Data Science for PhD Students
• DS- GA 1002 – Probability and Statistics for Data Science
• DS-GA 1003 – Machine Learning
• DS-GA 1004 – Big Data
• DS-GA 1005 – Inference and Representation
• A research rotation is a semester-long guided research experience in which the student will have an opportunity to design and carry out original research in a collaborative setting. The idea is to help students identify research interests. Ph.D. students take this course 6 times.

39 credit hours of elective courses  (for incoming students starting Fall 2020 and later)

Students must successfully complete 39 credit hours of elective courses. Faculty at the Center for Data Science are experts in a broad range of data science topics, and the Center’s course offerings reflect that diversity. For example, students will be able to take courses in Deep Learning, Optimization, and Natural Language Processing.

Some of the electives offered at the Center for Data Science are below. Please see NYU’s  Albert Course Search  for course descriptions.

• Deep Learning (DS-GA 1008)
• Practical Training for Data Science (DS-GA 1009):  Practical Training offers course credit for the academically relevant internship experience. This is an integral part of the Ph.D. Program curriculum and facilitates students with academic and professional development. The course allows students to apply their academic and research knowledge to real-world problems.
• Independent Study (DS-GA 1010)
• Natural Language Processing with Representation Learning (DS-GA 1011)
• Natural Language Understanding and Computational Semantics (DS-GA 1012)
• Mathematical Tools for Data Science (DS-GA 1013)
• Optimization and Computational Linear Algebra (DS-GA 1014)
• Text as Data (DS-GA 1015)
• Computational Cognitive Modeling (DS-GA 1016)
• Responsible Data Science (DS-GA 1017)
• Probabilistic Time Series Analysis (DS-GA 1018)
• Communication Skills (DS-GA 2002)

Students can take electives outside of the Center of Data Science with permission from the Director of Graduate Studies (DGS).

Typical Schedule (Incoming Students Fall 2020 and later)

Typically, a student’s first 3 years will follow a schedule like the one outlined below. The student’s remaining years will consist of electives and work on his or her research and dissertation.

• DS-GA 2003 Introduction to Data Science for PhD Students
• DS-GA 1002 Probability and Statistics for Data Science
• DS-GA-2001 Research Rotation
• DS-GA 1003 Machine Learning
• DS-GA 1004 Big Data
• DS-GA 2001 Research Rotation
• DS-GA 1005 Inference and Representation
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• Approved Elective

Teaching Requirement  (for incoming students starting Fall 2020 and later)

By the end of the fourth year of study, each student must have served as a section leader or instructor for at least two courses at the Center for Data Science (for students starting the program in Fall 2023 or later). For students who started the program between Fall 2020 – Fall 2022, the requirement is at least one course at the Center for Data Science.

Courses on related topics outside the Center may also be used to satisfy this requirement subject to approval by the DGS. The student must also participate in the Center’s teacher training session at or prior to the semester in which they teach. In certain circumstances, the DGS may allow the student to satisfy this requirement by serving as a course assistant or as a grader.  These exceptions will be determined by the DGS based on the availability of suitable recitations.

Comprehensive Exam

The comprehensive exam is designed to determine whether the candidate displays the requisite data science knowledge to pursue their research.

For students starting the program in Fall 2024 and later: To fulfill this requirement, students will submit a 4-page report describing their work during their first year and a plan of their future research at the end of their second semester. The student will also give a 10-minute presentation in front of a pre-committee of three faculty (which will include their research advisors). The committee will determine whether the student is progressing adequately based on their academic performance (including grades and feedback from course instructors), the presentation, and the report.

For students who started the program prior to Fall 2024: The comprehensive exam consists of material from DS-GA 1003 Machine Learning and DS-GA 1004 Big Data. To fulfill this requirement, students must receive an A- or above as their final grade for each of the courses above  (for students starting Fall 2020 – Fall 2023) . Students are expected to complete this requirement by the end of their second semester.

Depth Qualifying Exam (DQE)

No later than the end of the third semester, each student must:

• Agree with a research advisor. The student is responsible for finding a research advisor, obtaining an agreement to advise the student, and informing the Director of Graduate Studies (DGS) of the agreement. Students must reach an agreement with the DGS and the Manager of Academic Affairs if they wish to change research advisors. If a research advisor determines that he or she no longer wishes to advise a student, the research advisor informs the DGS who will begin working with the student to find another research advisor.
• Agree with his or her research advisor on a research project, an exam topic, and a Depth Qualifying Exam (DQE) committee.
• Obtain the approval of the DGS on the research project, exam topic, and DQE committee, as well as the date of the DQE exam.

No later than the end of his fourth semester, the student must pass the depth qualifying exam (DQE). The exam may be taken no more than twice. The content of the exam is defined by the student’s DQE Committee, which must present a syllabus to the student at least 2 months before the date of the exam.

For incoming students Fall 2020 and later, the exam itself consists of a presentation by the student on original research carried out independently or in collaboration with faculty, research staff, or other students. This can include research done in the research rotations or other research conducted by the student in their area of interest. The goal of the DQE is to confirm the student’s knowledge of research in their area of interest.

Ph.D. Dissertation

Dissertation proposal approval.

CDS PhD students are encouraged to identify their dissertation proposal committee by the end of their second year. Students should consult with their advisor and/or the DGS. The student works with their research advisor to select a dissertation proposal approval committee, obtains approval of this committee from the DGS, submits a written dissertation proposal to the committee, and obtains the approval of the committee. The committee consists of at least three members, which may consist of individuals with similar standing outside of CDS. At least one member must be a CDS faculty member (CDS joint faculty member, member of the CDS PhD Advisory Group, or CDS affiliated (see the Areas & Faculty page ). Students should have their dissertation proposal approved no later than the end of their third year. However, this is a guideline. Students are encouraged to identify timing of the dissertation proposal in consultation with their advisor and/or the DGS.

DISSERTATION APPROVAL

A successful defense is required for award of the PhD. 

The PhD defense committee must have at least 5 members, including the advisor(s), three of whom must be CDS faculty (CDS joint faculty member, member of the CDS PhD Advisory Group, or CDS affiliated (see Areas & Faculty page ), and 1 external member (in related area from another NYU department or from an area institution, with approval from DGS). The membership of the defense committee is proposed by the student and approved by the DGS.

In addition, students must comply with all of the procedures of  NYU’s Graduate of School of Arts and Science related to the submission of their dissertation.

Analytics Insight

Top 10 Universities in USA Offering Ph.D In Data Science

Brown University – Providence, Rhode Island

Indiana university-purdue university indianapolis  – indianapolis, indiana, new york university – new york, new york, yale university – new haven, connecticut, the university of maryland, college park, maryland, kennesaw state university – kennesaw, georgia, university of massachusetts boston – boston, massachusetts, california institute of technology, pasadena, california, university at buffalo, buffalo, new york, clemson university / medical university of south carolina (musc) – joint program– clemson, south carolina & charleston, south carolina.

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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 solution 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 minorities 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 semester courses (64 credits) are required for post-BA/BS students and 12 semester courses (48 credits) 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 credits) as long as these credits 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 credits) 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-semester training course (4 credits) covering various aspects of the responsible and ethical conduct of computational and data-driven research, a two-semester doctoral seminar (4 credits) 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 credits) 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 credits. 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 semester (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 semester 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 semester (fourth year) of study.

Candidates must undergo a final oral examination no later than the end of the 10th semester (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.

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Department of Statistics and Data Science

Ph.d. program.

Fields of study include the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak convergence), information theory, bioinformatics and genetics, classification, data mining and machine learning, neural nets, network science, optimization, statistical computing, and graphical models and methods.

With this background, graduates of the program have found excellent positions in universities, industry, and government. See the list of alumni for examples.

DEPARTMENT OF STATISTICS AND DATA SCIENCE

Phd program, phd program overview.

The doctoral program in Statistics and Data Science is designed to provide students with comprehensive training in theory and methodology in statistics and data science, and their applications to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals. Cross-disciplinary work is encouraged. The PhD program prepares students for careers as university teachers and researchers and as research statisticians or data scientists in industry, government and the non-profit sector.

Requirements

Students are required to fulfill the Department requirements in addition to those specified by The Graduate School (TGS).

From the Graduate School’s webpage outlining the general requirements for a PhD :

In order to receive a doctoral degree, students must:

• Complete all required coursework. .
• Gain admittance to candidacy.
• Submit a prospectus to be approved by a faculty committee.
• Present a dissertation with original research. Review the Dissertation Publication page for more information.
• Complete the necessary teaching requirement
• Submit necessary forms to file for graduation
• Complete degree requirements within the approved timeline

PhD degrees must be approved by the student's academic program. Consult with your program directly regarding specific degree requirements.

The Department requires that students in the Statistics and Data Science PhD program:

• Meet the department minimum residency requirement of 2 years
• STAT 344-0 Statistical Computing
• STAT 350-0 Regression Analysis
• STAT 353-0 Advanced Regression (new 2021-22)
• STAT 415-0 I ntroduction to Machine Learning
• STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3
• STAT 430-1, STAT 430-2, STAT 440 (new courses in 2022-23 on probability and stochastic processes for statistics students)
• STAT 457-0 Applied Bayesian Inference

Students generally complete the required coursework during their first two years in the PhD program. *note that required courses changed in the 2021-22 academic year, previous required courses can be found at the end of this page.

• Pass the Qualifying Exam. This comprehensive examination covers basic topics in statistics and is typically taken in fall quarter of the second year.

Pass the Prospectus presentation/examination and be admitted for PhD candidacy by the end of year 3 . The statistics department requires that students must complete their Prospectus (proposal of dissertation topic) before the end of year 3, which is earlier than The Graduate School deadline of the end of year 4. The prospectus must be approved by a faculty committee comprised of a committee chair and a minimum of 2 other faculty members. Students usually first find an adviser through independent studies who will then typically serve as the committee chair. When necessary, exceptions may be made upon the approval of the committee chair and the director of graduate studies, to extend the due date of the prospectus exam until the end of year 4.

• Successfully complete and defend a doctoral dissertation. After the prospectus is approved, students begin work on the doctoral dissertation, which must demonstrate an original contribution to a chosen area of specialization. A final examination (thesis defense) is given based on the dissertation. Students typically complete the PhD program in 5 years.
• Attend all seminars in the department and participate in other research activities . In addition to these academic requirements, students are expected to participate in other research activities and attend all department seminars every year they are in the program.

Optional MS degree en route to PhD

Students admitted to the Statistics and Data Science PhD program can obtain an optional MS (Master of Science) degree en route to their PhD. The MS degree requires 12 courses: STAT 350-0 Regression Analysis, STAT 353 Advanced Regression, STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3, STAT 415-0 I ntroduction to Machine Learning , and at least 6 more courses approved by the department of which two must be 400 level STAT elective courses, no more than 3 can be non-STAT courses. For the optional MS degree, students must also pass the qualifying exam offered at the beginning of the second year at the MS level.

*Prior to 2021-2022, the course requirements for the PhD were:

• STAT 351-0 Design and Analysis of Experiments
• STAT 425 Sampling Theory and Applications
• MATH 450-1,2 Probability 1, 2 or MATH 450-1 Probability 1 and IEMS 460-1,2 Stochastic Processes 1, 2
• Six additional 300/400 graduate-level Statistics courses, at least two must be 400 -level

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Data Science Ph.D.

A dynamic data science environment.

Graduates of our program—the first of its kind in both Indiana and the Big Ten—develop the skills to make pioneering research contributions to data science theory and practice in academic and the industrial sectors.

Our students acquire the skills to develop inventive and creative solutions to data research problems—solutions that demonstrate a high degree of intellectual merit and the potential for broader impact. The Ph.D. curriculum also prepares students to make research contributions that advance the theory and practice of data science.

A leader in data science research

The Data Science Ph.D. Program at IU Indianapolis provides a world-class education and research opportunities. Ph.D. students in the program learn fundamental Data Science methods while pursuing independent, original research in a broad variety of topics, including:

• Novel techniques for Natural Language Processing and Text Analytics.
• Applications of AI to social welfare, digital governance, cultural heritage, biomedical sciences, and environmental sustainability.
• Intelligent conversational agents and models of Human-AI collaboration.
• Data Visualization and Human-Data Interaction.

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The program is in the midst of a major expansion, with over 50 graduate students joining the program in the past year alone. Multiple faculty in our department have secured high-profile research grants, including three    active   CAREER awards, the National Science Foundation’s most prestigious award for early-career faculty. The IU Indianapolis campus hosts the newly created Institute of Integrative Artificial Intelligence, providing an interdisciplinary nexus between Data Science, AI, and various science and engineering fields.

Sunandan Chakraborty

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Sarath Chandra Janga

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Leon Johnson

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Kyle M. L. Jones

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Angela Murillo

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Saptarshi Purkayastha

Associate Professor, Data Science, Health Informatics

Khairi Reda

Associate Professor, Data Science, Human-Computer Interaction

Elie Salomon

Lecturer, Data Science; Library and Information Science

Ayoung Yoon

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Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.

Choosing a Field of Study

There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics.

There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology . You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics . Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research .

Residency Requirements

Students admitted to PhD program must be "in residence" for at least four semesters, although it is generally expected that a PhD will require between 8 and 10 semesters to complete. The chair of your Special Committee awards one residence unit after the satisfactory completion of each semester of full-time study. Fractional units may be awarded for unsatisfactory progress.

Your Advisor and Special Committee

The Director of Graduate Studies is in charge of general issues pertaining to graduate students in the field of Statistics. Upon arrival, a temporary Special Committee is also declared for you, consisting of the Director of Graduate Studies (chair) and two other faculty members in the field of Statistics. This temporary committee shall remain in place until you form your own Special Committee for the purposes of writing your doctoral dissertation. The chair of your Special Committee serves as your primary academic advisor; however, you should always feel free to contact and/or chat with any of the graduate faculty in the field of Statistics.

The formation of a Special Committee for your dissertation research should serve your objective of writing the best possible dissertation. The Graduate School requires that this committee contain at least three members that simultaneously represent a certain combination of subjects and concentrations. The chair of the committee is your principal dissertation advisor and always represents a specified concentration within the subject & field of Statistics. The Graduate School additionally requires PhD students to have at least two minor subjects represented on your special committee. For students in the field of Statistics, these remaining two members must either represent (i) a second concentration within the subject of Statistics, and one external minor subject; or, (ii) two external minor subjects. Each minor advisor must agree to serve on your special committee; as a result, the identification of these minor members should occur at least 6 months prior to your A examination.

Some examples of external minors include Computational Biology, Demography, Computer Science, Economics, Epidemiology, Mathematics, Applied Mathematics and Operations Research. The declaration of an external minor entails selecting (i) a field other than Statistics in which to minor; (ii) a subject & concentration within the specified field; and, (iii) a minor advisor representing this field/subject/concentration that will work with you in setting the minor requirements. Typically, external minors involve gaining knowledge in 3-5 graduate courses in the specified field/subject, though expectations can vary by field and even by the choice of advisor. While any choice of external minor subject is technically acceptable, the requirement that the minor representative serve on your Special Committee strongly suggests that the ideal choice(s) should share some natural connection with your choice of dissertation topic.

The fields, subjects and concentrations represented on your committee must be officially recognized by the Graduate School ; the Degrees, Subjects & Concentrations tab listed under each field of study provides this information. Information on the concentrations available for committee members chosen to represent the subject of Statistics can be found on the Graduate School webpage . 

Statistics PhD Travel Support

The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences. Please review the Graduate Student Travel Award Policy website for more information. 

Completion of the PhD Degree

In addition to the specified residency requirements, students must meet all program requirements as outlined in Program Course Requirements and Timetables and Evaluations and Examinations, as well as complete a doctoral dissertation approved by your Special Committee. The target time to PhD completion is between 4 and 5 years; the actual time to completion varies by student.

Students should consult both the Guide to Graduate Study and Code of Legislation of the Graduate Faculty (available at www.gradschool.cornell.edu ) for further information on all academic and procedural matters pertinent to pursuing a graduate degree at Cornell University.

Data Science, Analytics and Engineering

MS Program information – All PhD Program information PhD Admissions PhD Faculty Tuition and fees Career services Request information – PhD How and when to apply

Program description

Data scientists are consistently ranked among the top jobs in the USA, and there is an increasing need for all engineers to make use of data science tools like statistics, machine learning, artificial neural networks and artificial intelligence. Yet, the majority of engineering occupations require subject matter expertise beyond data science.

Degree programs 

Data science, analytics and engineering ms.

The MS program in data science, analytics and engineering with a concentration in electrical engineering provides an advanced education in high-demand data science and electrical engineering. A focus on probability and statistics, machine learning, data mining and data engineering is complemented by electrical engineering-specific courses to ensure breadth and depth in data science and electrical engineering.

All students must choose a concentration:

• Computing and Decision Analysis
• Electrical Engineering
• Materials Science and Engineering
• Sustainable Engineering and Built Environment
• Bayesian Machine Learning
• Computational Models and Data
• Human Centered Applications
• Mechanical and Aerospace Engineering

Application deadlines

Fall: December 31 Spring: July 31

Data Science, Analytics and Engineering PhD

The PhD program in data science, analytics and engineering engages students in fundamental and applied research as preparation for careers in academia, government or industry. The program’s educational objective is to develop each student’s ability to perform original research in the development and execution of data-driven methods for solving major societal problems. This includes the ability to identify research needs, adapt existing methods and create new methods as needed for data analytics and engineering.

This degree program is a collaboration between the School of Computing and Augmented Intelligence  and the School of Mathematical and Statistical Sciences (SoMSS) and provides a rigorous education with research and educational experiences that allow students to pursue careers in advanced research, teaching or state-of-the-art practice. Graduates demonstrate proficiency with existing methodology and significant accomplishment at advancing the state of the art in their chosen area of data science, analytics and engineering.

Fall: January 15 Spring: September 15

Contacts for the MS program are specific to the desired concentration. See the DSAE MS website contacts list for the correct contact.

PhD program

School of Computing and Augmented Intelligence Graduate advising office

• On campus programs: [email protected]
• Online MCS students:  [email protected]

Data science, analytics and engineering

• MS Program information – All
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Read our online brochure for more details about the data science, analytics and engineering graduate program.

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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.

M.S. in Data Science

The m.s. in data science allows students to apply data science techniques to their field of interest..

Ours is one of the most highly-rated and sought-after advanced data science programs in the world.

Program Highlights

Columbia data science students have the opportunity to conduct original research, produce a capstone project , and interact with our industry partners and world-class faculty.

This program is jointly offered in collaboration with the Graduate School of Arts and Sciences’ Department of Statistics, and The Fu Foundation School of Engineering and Applied Science’s Department of Computer Science and Department of Industrial Engineering and Operations Research.

Some students are primarily concerned about data ethics, others are excited about data science as a new evolution in knowledge, but all are interested in how data science is changing our everyday lives.

Where are columbia data science graduates now*.

  *A partial list as of May 2022

Computer Science

Prerequisites: Students are expected to have solid programming experience in Python or with an equivalent programming language. This class is intended to be accessible for students who do not necessarily have a background in databases, operating systems or distributed systems. The goal of this class is to provide data scientists and engineers that work with big data a better understanding of the foundations of how the systems they will be using are built. It will also give them a better understanding of the real-world performance, availability and scalability challenges when using and deploying these systems at scale. In the course we will cover foundational ideas in designing these systems, while focusing on specific popular systems that students are likely to encounter at work or when doing research. 

Spring Semester: 3 credits

COMS 4721 is a graduate-level introduction to machine learning. The course covers basic statistical principles of supervised machine learning, as well as some common algorithmic paradigms. Additional topics, such as representation learning and online learning, may be covered if time permits.

Prerequisites: Background in linear algebra and probability and statistics.

Methods for organizing data, e.g. hashing, trees, queues, lists, priority queues. Streaming algorithms for computing statistics on the data. Sorting and searching. Basic graph models and algorithms for searching, shortest paths, and matching. Dynamic programming. Linear and convex programming. Floating point arithmetic, stability of numerical algorithms, Eigenvalues, singular values, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Conjugate gradient, Newton and quasi-Newton methods. Large scale applications from signal processing, collaborative filtering, recommendations systems, etc.

Prerequisites: basic knowledge in programming (e.g., at the level of COMS W1007), a basic grounding in calculus and linear algebra.

Engineering

Prerequisites: CSOR W4246 Algorithms for Data Science, STAT W4105 Probability, COMS W4121 Computer Systems for Data Science, or equivalent as approved by faculty advisor. Co-requisites: to be completed alongside or after: STAT W4702 Statistical Inference and Modeling, COMS W4721 Machine Learning for Data Science, STAT W4701 Exploratory Data Analysis and Visualization, or equivalent as approved by faculty advisor.

This course provides a unique opportunity for students in the M.S. in Data Science program to apply their knowledge of the foundations, theory and methods of data science to address data science problems in industry, government and the non-profit sector. The course activities focus on a semester-length data science project sponsored by a faculty member or local organization. The project synthesizes the statistical, computational, engineering challenges and social issues involved in solving complex real-world problems.

Fall and Spring Semesters: 3 credits

This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression.

Prerequisite: Calculus.

Prerequisite: Programming, fundamentals of data visualization, layered grammar of graphics, perception of discrete and continuous variables, introduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification.

Course covers fundamentals of statistical inference and testing, and gives an introduction to statistical modeling. The first half of the course will be focused on inference and testing, covering topics such as maximum likelihood estimates, hypothesis testing, likelihood ratio test, Bayesian inference, etc. The second half of the course will provide introduction to statistical modeling via introductory lectures on linear regression models, generalized linear regression models, nonparametric regression, and statistical computing. Throughout the course, real-data examples will be used in lecture discussion and homework problems.

Prerequisites: Working knowledge of calculus and linear algebra (vectors and matrices) and STAT GR5203 or equivalent.

In addition to the 21 credits of core classes, M.S. in Data Science students are required to complete a minimum of nine (9) credits of electives. In addition to the DSI elective courses, MS students are encouraged to explore courses offered across the university and take advantage of the expertise in a wide range of disciplines at Columbia. Prior to registration, students receive advisement to determine if a course of interest is relevant and meets the criteria of a 4000-level or higher, technical course completed for a letter grade. You are welcome to explore the  Columbia Directory of Classes  for possible courses.

The following courses are examples of classes that MS students have used for elective credit. Elective courses and schedules are dependent on faculty availability and may vary each semester. Past course offerings are not guaranteed to be offered in the future.

Please note that many departments, including DSI, give registration priority to their students. Space permitting, courses are then opened up to students outside the department.

This class offers a hands-on approach to machine learning and data science. The class discusses the application of machine learning methods like SVMs, Random Forests, Gradient Boosting and neural networks on real world dataset, including data preparation, model selection and evaluation. This class complements COMS W4721 in that it relies entirely on available open source implementations in scikit-learn and tensor flow for all implementations. Apart from applying models, we will also discuss software development tools and practices relevant to productionizing machine learning models.

This course provides a practical, hands-on introduction to Deep Learning. We aim to help students understand the fundamentals of neural networks (DNNs, CNNs, and RNNs), and prepare students to successfully apply them in practice. This course will be taught using open-source software, including TensorFlow 2.0. In addition to covering the fundamental methods, we will discuss the rapidly developing space of frameworks and applications, including deep learning on the web. This course includes an emphasis on fairness and testing, and teaches best practices with these in mind.

Data scientists often have to answer questions that will lead to decisions about actions a company might take. Often, they will be able to run an experiment, and see the effect the decision might have by testing it first.  Other times, they will only have observational data at their disposal. In both cases, they need to infer the causal effect of an action on some outcomes of interest. Causal inference is an essential skill for a data scientist. Without a proper understanding, potential biases as large as 1000% have been observed in practice! This course will cover the basics of the potential outcomes framework, the Pearlian framework, and a collection of methods for observational and experimental causal inference. We’ll use examples from industry applications throughout the course, especially focused on web applications.

“Data analytics pipeline” focuses on the intersection between data science, data engineering, and agile product development. In this course you’ll learn some common data generating processes, how the data is transported to be stored, how analytics and compute capabilities are built on top of that storage, and how production machine learning and modeling platforms can be built in that context. There is a strong focus on good architecture design patterns, and practical implementation considerations that focus on delivering results over building perfect systems

This course is designed as an introduction to elements that constitutes the skill set of a data scientist. The course will focus on the utility of these elements in common tasks of a data scientist, rather than their theoretical formulation and properties. The course provides a foundation of basic theory and methodology with applied examples to analyze large engineering, business, and social data for data science problems. Hands-on experiments with R or Python will be emphasized.

The world is full of noise and uncertainty. To make sense of it, we collect data and ask questions. Is there a tumor in this x-ray scan? What affects the quality of my manufacturing plant? How old is this planet I see through the telescope? Does this drug actually work? To pose and answer such questions, data scientists must iterate through a cycle: probabilistically model a system, infer hidden patterns from data, and evaluate how well our model describes reality. By the end of this course, you will learn how to use probabilistic programming to effectively iterate through this cycle. Specifically, you will master modeling real-world phenomena using probability models, using advanced algorithms to infer hidden patterns from data, and evaluating the effectiveness of your analysis. You will learn to use (and perhaps even contribute to) Edward throughout this course.

This applied Natural Language Processing course will focus on computational methods for extracting social and interactional meaning from large volumes of text and speech (both traditional media and social media). Topics will include:

• Sentiment Analysis: automatic detection of people’s sentiment towards a topic, event, product, or persons. Practical applications in various domains will be discussed (e.g., predicting stock market prices, or presidential elections)
• Emotion and Mood Analysis: automatic detection of people’s emotions (angry, sad, happy) by analyzing various media such as books, emails, lyrics, online discussion forums. Practical applications in various domains (such as predicting depression, categorization of songs)
• Belief Analysis and Hedging: automatic detection of people’s beliefs (committed belief and non-committed beliefs) from social media. Analysis of the use of hedging as a communicative device in various media: online discussions, scientific writing or legal discussions.
• Deception Detection (e.g., detecting fake reviews online, or deceptive speech in court proceedings)
• Argumentation Mining: automatic detection of arguments from text, such as online discussion or persuasive essays. Practical application for various domains (e.g., political, legal or education (e.g., improving students’ skills in writing persuasive essays)
• Social Power: automatic detection of power structure in organizations by analyzing people’s communications such as emails.
• Extracting Social Networks from text, such as networks of characters from novels, or networks from social media (e.g., people holding particular opinions, or network of friends).
• Personality and Interpersonal Stance

Contact DSI at [email protected] for more information about this course.

Personalization is a key tool for enhancing customer experience across industries, thereby driving user loyalty and customer value. It is therefore no surprise that creating and enhancing personalization systems is also increasingly one of the core responsibilities of data science teams, and a key focus for many of the machine learning algorithms in the sector. This course will focus on common personalization algorithms and theory, including behavior-based and content-based recommendation, commonly encountered issues in scaling and cold-starts, and state of the art research. It will also look at how businesses use, and misuse, these techniques in real world applications.

The vast proliferation of data and increasing technological complexities continue to transform the way industries operate and compete. Over the last two years, 90 percent of the data in the world has been created as a result of the creation of 2.5 quintillion bytes of data on a daily basis. Commonly referred to as big data, this rapid growth and storage creates opportunities for collection, processing and analysis of structured and unstructured data. Financial services, in particular, have widely adopted big data analytics to inform better investment decisions with consistent returns. In conjunction with big data, algorithmic trading uses vast historical data with complex mathematical models to maximize portfolio returns. The continued adoption of big data will inevitably transform the landscape of financial services. However, along with its apparent benefits, significant challenges remain in regards to big data’s ability to capture the mounting volume of data. The increasing volume of market data poses a big challenge for financial institutions. Along with vast historical data, banking and capital markets need to actively manage ticker data. Likewise, investment banks and asset management firms use voluminous data to make sound investment decisions. Insurance and retirement firms can access past policy and claims information for active risk management. The course will be a mix of Theory and practice with real big data cases in finance. We will invite guest lecturers mostly for real Big Data Finance Applications. We will give MATLAB, R, or Python examples.

The course focuses on translating technical expertise into work-place solutions by teaching students to: (1) identify relevant shortfalls in traditional processes; (2) precisely match datasets and machine learning features to overcome these shortfalls; (3) narrowly define value to fit work place processes, analytical framework, and bottom line.  Each class will be structured as an actual end-to-end work-place project and use concrete examples to teach students to design, build and deliver solutions that integrate these considerations. A combination of assignments, presentation, and research paper will be sued to evaluation students’ progress in bridging technical and applied solutions with evaluation criteria matching those of a work-place project.

Images are everywhere. How to deal with image data, especially with big data, is an urgent problem for data analysts.  Machine learning has proven to be a powerful technology to process and analyze such big data.  The course will discuss how machine learning methods are use in the field of image analysis, including biometrics (iris and face recognition), natural images (object identification/recognition), brain images (encoding and decoding), and handwritten digit recognition.  Students will learn how to sue traditional machine learning methods in image data processing and analysis, and develop techniques to improve these methods.  The aim of this course is to prepare students with basis knowledge and skills to explore opportunities using machine learning in the field of image analysis.

Cross-Registration

• Instructions for Non-Data Science Students Please note that Data Science students have priority registration, so enrollment will be dependent on the space available after our student registration. Non-Data Science students will be able to register/join a waitlist via SSOL starting  August 29th  for  Fall 2023 .  Please be sure to obtain your program advisor approval before enrolling.  The Fall 2023 Change of Program Period is Tuesday, September 5, 2023 through Friday, September 15, 2023

I wanted to be at an institution that would truly challange me and put me at the forefront of growing areas of research in data science. Columbia promised the most rigorous and innovative curriculum on the planet...I knew the educational experience here would be like no other.

Kevin Womack

Class of 2021

Explore More from Master of Science in Data Science

Are you ready to apply to the M.S. in Data Science program? Check out our FAQ down below or click the Learn More button.

Course Inventory

The number of data science-related courses available to Columbia students is growing. Here's an updated list.

Capstone Projects

All of our students complete capstone projects with industry affiliates and thought leaders.

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

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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.

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

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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.

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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

May 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.

June PhD Program Overview

July phd program overview, august 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.

view recent placements 

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.

Faculty Links

• Accounting Faculty
• Economic Sociology Faculty
• Finance Faculty
• Information Technology Faculty
• Institute for Work and Employment Research (IWER) Faculty
• Marketing Faculty
• Organization Studies Faculty
• System Dynamics Faculty
• Technological Innovation, Entrepreneurship, and Strategic Management (TIES) Faculty

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.

How Should We Measure the Digital Economy?

2020 PhD Doctoral Research Forum Winner - Avinash Collis

Watch more MIT Sloan PhD Program  Doctoral Forum Videos

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Best Undergraduate Data Science Programs

A data analytics/science specialty prepares students to use computer programming and statistics to scrutinize data for trends and patterns. These are the top undergraduate computer science programs for data analytics/science. Read the methodology »

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University of California, Berkeley

Berkeley, CA

• #1 in Data Analytics/Science
• #2 in Computer Science  (tie)

The University of California, Berkeley overlooks the San Francisco Bay in Berkeley, Calif. Students at this public school have more than 1,000 groups to get involved in, including more than 60 fraternity and sorority chapters.

(out-of-state)

(fall 2022)

SAT, GPA and More

Massachusetts Institute of Technology

Cambridge, MA

• #2 in Data Analytics/Science
• #1 in Computer Science

Though the Massachusetts Institute of Technology may be best known for its math, science and engineering education, this private research university also offers architecture, humanities, management and social science programs. The school is located in Cambridge, Massachusetts, just across the Charles River from downtown Boston.

Carnegie Mellon University

Pittsburgh, PA

• #3 in Data Analytics/Science

Carnegie Mellon University, a private institution in Pittsburgh, is the country’s only school founded by industrialist and philanthropist Andrew Carnegie. The school specializes in academic areas including engineering, business, computer science and fine arts.

Stanford University

Stanford, CA

• #4 in Data Analytics/Science  (tie)

The sunny campus of Stanford University is located in California’s Bay Area, about 30 miles from San Francisco. The private institution stresses a multidisciplinary combination of teaching, learning, and research, and students have many opportunities to get involved in research projects.

University of Washington

Seattle, WA

• #10 in Computer Science

Located north of downtown Seattle, the University of Washington is one of the oldest public universities on the West Coast. It is also a cutting-edge research institution, receiving significant yearly federal funding, and hosting an annual undergraduate research symposium for students to present their work to the community. The university has a highly ranked School of Medicine, College of Engineering and Michael G. Foster School of Business. Known as a commuter school, the university does not require freshmen to live on campus, but it encourages students who do to conserve energy and recycle. Students can join one of the 950-plus student organizations on campus, including about 70 sororities and fraternities. Nearly three-fourths of UW graduates remain in the state post-graduation.

Georgia Institute of Technology

Atlanta, GA

• #6 in Data Analytics/Science
• #6 in Computer Science  (tie)

Georgia Tech, located in the heart of Atlanta, offers a wide range of student activities. The Georgia Tech Yellow Jackets, an NCAA Division I team, compete in the Atlantic Coast Conference and have a fierce rivalry with the University of Georgia. Since 1961, the football team has been led onto the field at home games by the Ramblin' Wreck, a restored 1930 Model A Ford Sport Coupe. Georgia Tech has a small but vibrant Greek community. Freshmen are offered housing, but aren't required to live on campus. In addition to its campuses in Atlanta and Savannah, Georgia Tech has campuses in France, Ireland, Costa Rica, Singapore and China.

University of Michigan--Ann Arbor

Ann Arbor, MI

• #7 in Data Analytics/Science
• #11 in Computer Science  (tie)

The university boasts of Ann Arbor, only 45 minutes from Detroit, as one of the best college towns in the U.S. Freshmen are guaranteed housing but not required to live on campus. Students can join one of the school’s more than 1,500 student organizations or 62 Greek chapters. Athletics play a central role at Michigan, including the football team’s fierce rivalry with Ohio State. Michigan also offers highly ranked graduate programs, including the Stephen M. Ross School of Business, College of Engineering, Law School and Medical School, in addition to the well-regarded School of Dentistry and Taubman College for Architecture and Urban Planning. The University of Michigan Hospitals and Health Centers is ranked among the top hospitals in the country.

Cornell University

• #8 in Data Analytics/Science

Cornell University, a private school in Ithaca, New York, has 14 colleges and schools. Each admits its own students, though every graduate receives a degree from Cornell University. The university has more than 1,000 student organizations on campus.

Harvard University

• #9 in Data Analytics/Science

Harvard University is a private institution in Cambridge, Massachusetts, just outside of Boston. This Ivy League school is the oldest higher education institution in the country and has the largest endowment of any school in the world.

Columbia University

New York, NY

• #10 in Data Analytics/Science
• #14 in Computer Science  (tie)

Columbia University has three undergraduate schools: Columbia College, The Fu Foundation School of Engineering and Applied Sciences (SEAS), and the School of General Studies. This Ivy League, private school guarantees students housing for all four years on campus in Manhattan’s Morningside Heights neighborhood in New York City.

University of California, San Diego

La Jolla, CA

• #11 in Data Analytics/Science

The University of California, San Diego lies alongside the Pacific Ocean in the La Jolla community of San Diego. The UCSD Tritons compete in more than 20 NCAA Division II sports, mainly in the California Collegiate Athletic Association. The school has hundreds of student organizations, and the university hosts a thriving Greek community. All freshmen are eligible for guaranteed on-campus housing for two years, but they are not required to live on campus. The campus has an aquarium and is home to the Large High Performance Outdoor Shake Table, which tests structures’ ability to withstand simulated earthquakes.

University of Illinois Urbana-Champaign

Champaign, IL

• #12 in Data Analytics/Science
• #5 in Computer Science

The University of Illinois is located in the twin cities of Urbana and Champaign in east-central Illinois, only a few hours from Chicago, Indianapolis and St. Louis. The school's Fighting Illini participate in more than 20 NCAA Division I varsity sports and are part of the Big Ten Conference. The university boasts one of the largest Greek systems in the country, and almost a quarter of the student body is involved. It’s not hard to find something to do on campus with more than 1,600 student organizations, including professional, political and philanthropic clubs. All freshmen are required to live on campus.

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More Schools in this List (Alphabetical)

California Institute of Technology

Pasadena, CA

• in Data Analytics/Science
• in Computer Science

The California Institute of Technology focuses on science and engineering education and has a low student-to-faculty ratio of 3:1. This private institution in Pasadena, California, is actively involved in research projects with grants from NASA, the National Science Foundation and the U.S. Department of Health and Human Services.

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Duke University

Located in Durham, North Carolina, Duke University is a private institution that has liberal arts and engineering programs for undergraduates. The Duke Blue Devils sports teams have a fierce rivalry with the University of North Carolina—Chapel Hill Tar Heels and are best known for their outstanding men's basketball program.

Johns Hopkins University

Baltimore, MD

Johns Hopkins University is a private institution in Baltimore that offers a wide array of academic programs in the arts, humanities, social and natural sciences, and engineering disciplines. The Hopkins Blue Jays men’s lacrosse team is consistently dominant in the NCAA Division I; other sports teams at Hopkins compete at the Division III level.

New York University

New York University’s primary campus is located in the lively Greenwich Village neighborhood of Manhattan. NYU is a true city school, with no borders separating a distinct campus from the streets of the Big Apple. Students are guaranteed housing for all four years in the many residence halls throughout Manhattan, but many upperclassmen choose to live off campus in apartments around the city. NYU has a small but active Greek life with more than 30 fraternity and sorority chapters. There are more than 300 student organizations on campus, such as WNYU, the student radio station which streams online and broadcasts on a local FM channel to the university community.

Princeton University

Princeton, NJ

The ivy-covered campus of Princeton University, a private institution, is located in the quiet town of Princeton, New Jersey. Princeton was the first university to offer a "no loan" policy to financially needy students, giving grants instead of loans to accepted students who need help paying tuition.

Purdue University--Main Campus

West Lafayette, IN

Purdue University's West Lafayette, Indiana, campus is the main campus in the Purdue University system, which encompasses four other campuses throughout the state. About 20% of students are affiliated with Greek life, and Purdue offers a wide range of activities and organizations. Performance groups include the "All American" Marching Band, six jazz bands and two symphony orchestras. The Boilermakers, Purdue’s athletic teams, compete in the Division I Big Ten Conference and are well known for their dominant men’s and women’s basketball teams. The Boilermaker Special, Purdue’s official mascot, is a railroad locomotive cared for and maintained by the student-run Purdue Reamer Club. Although no students are required to live in university housing, about one-third of undergraduates live on campus.

University of California, Los Angeles

Los Angeles, CA

The University of California, Los Angeles is just five miles away from the Pacific Ocean. The public institution offers 5,000 courses, 140 bachelor's degree programs and 97 minors.

University of Maryland, College Park

College Park, MD

Located between Washington, D.C., and Baltimore, the University of Maryland offers students a suburban lifestyle within easy reach of big-city experiences. The flagship campus in College Park, which has its own subway stop on the D.C.-area Metro transit system, is close to the nation's capital. There are more than 800 clubs and organizations on campus, including nearly 60 fraternities and sororities. Freshmen and all other students are not required to live on campus. Students looking for additional activities can visit the university's "SEE UMD" website, which stands for "Student Entertainment Events" and offers movie screenings, comedy shows, lectures and more. Sports also offer yearlong distractions. The Maryland Terrapins compete in the NCAA Division I Big Ten Conference. The mascot, Testudo, is a Diamondback terrapin — a species of turtle that is the official state reptile. One of several Testudo sculptures on campus sits in front of McKeldin Library, and rubbing its nose is thought to bring good luck, particularly before exams.

University of Pennsylvania

Philadelphia, PA

Founded by Benjamin Franklin, the University of Pennsylvania is a private institution in the University City neighborhood of Philadelphia, Pennsylvania. Students can study in one of four schools that grant undergraduate degrees: Arts and Sciences, Nursing, Engineering and Applied Sciences, and Wharton.

University of Texas at Austin

The University of Texas at Austin is one of the largest schools in the nation. It’s divided into 13 schools and colleges, the biggest of which is the College of Liberal Arts. It also has highly ranked graduate programs, including the McCombs School of Business, Cockrell School of Engineering and School of Nursing. Students can participate in more than 1,000 clubs and organizations or in the sizable UT Greek system. The university has several student media outlets, and its sports teams are notorious competitors in the Division I Big 12 Conference. UT also offers hundreds of study abroad programs, with the most popular destinations being Spain, Italy, the United Kingdom, France and China. Freshmen do not have to live on campus.

15 Best Online Doctoral Programs/PhD Programs – 2024

April 24, 2024

If you’re looking to attain a terminal degree but want to skip the commute to campus, an online doctoral program might be a great option for you. As our world becomes increasingly digital, many top universities have branched out into online learning, offering online PhD and doctoral programs that are just as rigorous as their in-person counterparts. Online graduate education can offer aspiring doctoral students flexibility, a high-quality education, and the option to continue working while pursuing a degree.

Many of the universities below in our round-up of the best online doctoral programs offer degrees in education—a common track for teachers looking to advance their careers. But universities across the country offer a wide range of degrees in everything from engineering management to library science. Read on for everything you need to know as you consider applying.

The Best Online Doctoral Programs – How Long Does It Take to Get a Doctoral Degree Online?

Online doctoral programs are often designed with full-time professionals in mind, prioritizing flexibility so that you can complete your degree at your own pace. Program lengths will vary, but most will take four to seven years. For students who need to take things slower, many programs offer options to spread degree requirements out over time. When applying, here are a few good questions to ask your admissions officer:

• How long do most graduate students take to complete their degrees?
• Are there flexible options for how many courses you’ll take per term?
• Will you pay tuition per semester or per credit hour? If paying by credit hour, will you able to choose how many classes you take each semester?
• Will you be required to write a dissertation? If so, how long do students usually take to write them?

The Best Online Doctoral Programs – Is an Online PhD Program a Good Fit for Me?

There’s no denying it: online doctoral programs are different than traditional ones. Many traditional PhD programs are designed for future academics, aka for graduate students who intend to apply for jobs as university professors after graduation. Traditional PhD programs are full-time, research-focused, in-person, and often offer graduate students funding in exchange for teaching or research assistant responsibilities. Online doctoral programs do also involve research, but most online graduate students are not full-time students. Rather, many students enrolled in online doctoral programs are working professionals who attend classes in the evenings or asynchronously. With that in mind, an online program can be a fantastic opportunity to further your current career.

An online doctorate program could be a great fit for you if:

• You want to advance your career. A doctorate can lead to further opportunities for promotion and certain fields offer automatic pay increases for people with advanced degrees.
• You love research and want to develop expertise in a topic that fascinates you.
• You learn well in online settings, including asynchronous classes.
• You plan to continue working while you study.

Online doctoral programs typically do not offer their students funding, though there may be scholarships and grants available to you.

The Best Online Doctoral Programs – What to Consider When Choosing an Online Doctorate Program

Since a doctorate can take 4-7 years or more to complete, choosing the right school for you is a huge decision. Our list below dives into many details you’ll need to know like acceptance rate, retention rate, and cost of tuition. It’s important to consider big-picture factors such as:

• Time to complete degree
• Professors and mentors—you’ll want to make sure you can study with faculty who have expertise in your academic interests
• Flexibility—does the program fit with your schedule? Do they require any in-person meetings?
• Graduation rate—ask your admissions officer for the most recent figures

You’ll also want to consider whether you want to pursue a PhD or a doctorate degree. If you’re pursuing a PhD, you can expect to focus more significantly on academic research and you’ll need to complete a dissertation. A doctorate is an equivalent degree that is less focused on academic research and may not require a dissertation. Doctorates can often involve more career development or hands-on practicum experience.

The Best Online Doctoral Programs – Are Online PhDs Respected?

Yes, online doctoral programs are just as rigorous and respected as their in-person counterparts. If you hope to earn a tenure-track faculty position at a university, it’s likely a traditional PhD program will be a better route. However, if you’re seeking a doctorate for career advancement and further learning, an online doctoral program can be a great fit. Many of the most prestigious universities offer online programs, and your diploma will likely not specify whether you completed an online or traditional degree.

The Best Online Doctoral Programs – What Do I Need to Apply?

Application requirements will vary depending on the program you’re applying to. All programs will require your academic transcripts, many require letters of recommendation, and some require GRE scores. On top of that, some programs will require a personal statement or writing portfolio. Contrary to what you might think, Master’s degrees are not always required for admission to online doctoral programs.

The Best Online Doctoral Programs/PhD Programs

1) university of florida.

Located in Gainesville, the University of Florida offers 10 different online doctoral programs . Well known for its graduate programs in education, educators can pursue PhDs in educational leadership, special education, computer science education, among other options. UF also offers online programs in nursing, Latin and Roman studies, microbiology, and a few other fields. Established in 1853, UF is a flagship state university with a strong reputation.

• Graduation Rate: 89%
• Acceptance Rate: 31%
• In State Tuition: $6,380
• Out of State Tuition: $28,658
• Application Requirements: Application portfolio (sample essays or projects), GRE scores, minimum undergraduate GPA of 3.4 or graduate GPA of 3.5, letters of recommendation

2) George Washington University

George Washington University ’s Department of Engineering Management and Systems Engineering leads the way with the university’s most comprehensive online PhD offerings . Through them, students can pursue doctoral degrees in cybersecurity analytics, engineering in artificial intelligence, systems engineering, or engineering management. Educators can also pursue well-respected degrees in education leadership and human and organizational learning.

• Graduation Rate: 85%
• Acceptance Rate: 43%
• Tuition: $31,770
• Application Requirements: GRE scores, personal statement, letters of recommendation, academic records

3) Johns Hopkins University

• A ten-year-old program , Johns Hopkins ’ online PhD in education allows students to specialize in digital age learning, entrepreneurial leadership, urban leadership, and other topics. Note their high graduation rate: Johns Hopkins is a competitive program , but admitted students are well-supported on their path to graduation.
• Graduation Rate: 94%
• Acceptance Rate: 11%
• Tuition: $57,010
• Application Requirements: Master’s degree, minimum GPA of 3.0

Best Online Doctoral Programs/PhD Programs (Continued)

4) texas tech university.

Texas Tech University offers a wide range of online and hybrid PhD programs that provide their students flexibility as they work toward completing their terminal degree. An affordable university in Lubbock, Texas Tech is a great place for future doctors of education, consumer science, technical communication, engineering management, and financial planning.

• Graduation Rate: 63%
• Acceptance Rate: 70%
• In State Tuition: $6,788
• Out of State Tuition: $14,968
• Application Requirements: Academic transcripts, portfolio and personal statement varies by program

5) Iowa State University

Although Iowa State University ’s online doctorate programs require some in-person meetings, ISU may still be a good fit for online students. With more uncommon online doctoral programs in hospitality management and apparel, merchandising, and design, ISU offers flexible routes to completing your dissertation.

• Graduation Rate: 75%
• Acceptance Rate: 88%
• In State Tuition: $9,758
• Out of State Tuition: $24,720
• Application Requirements: Academic records, minimum 3.0 GPA

6) University of Alabama

Online University of Alabama students may miss out on a lively campus culture and football games, but they can still take advantage of UA’s top-notch academics. Well-known for its online education graduate programs, UA also offers programs in social work, communication and information sciences, and engineering.

• Graduation Rate: 72%
• Acceptance Rate: 80%
• In State Tuition: $11,940
• Out of State Tuition: $32,300
• Application Requirements: GRE scores, academic records

7) University of Missouri

One of the most robust in terms of online offerings, the University of Missouri ’s online PhD classes are taught by the same professors who teach Mizzou’s in-person classes. Mizzou offers programs in education and nursing. They also allow students to pursue doctorates in harder-to-find subjects like health sciences, agriculture, architecture, and library science.

• Graduation Rate: 73%
• Acceptance Rate: 82%
• In State Tuition: $9,478
• Out of State Tuition: $25,946
• Application Requirements: Academic record, minimum GPA of 3.0, portfolio and personal statement varies by program

8) University of North Carolina Chapel Hill

One of the oldest public universities in the U.S., University of North Carolina – Chapel Hill is known as a leader in education. UNC Chapel Hill offers just three online PhDs: public health, nursing, and education. Although some online classes require in-person or proctored final exams, doctoral requirements can mostly be completed online.

• Graduation Rate: 91%
• Acceptance Rate: 25%
• In State Tuition: $9,208
• Out of State Tuition:  $36,891
• Application Requirements: GRE scores, academic records, letters of recommendation, personal statement

9) Georgia Southern University

Located in Savannah, Georgia Southern University works hard to create an environment of support and collaboration, even online. One of the more robust programs out there, GSU offers respected online programs in public health, nursing, education, and engineering.

• Graduation Rate: 54%
• Acceptance Rate: 91%
• In State Tuition: $4,986
• Out of State Tuition: $19,890
• Application Requirements: Minimum GPA of 3.0

10) Indiana University

Indiana University allows students to study at their own pace through flexible online doctoral programs. One of the only schools to offer online programs in music therapy and philanthropy leadership, IU also allows students to pursue tracks in health sciences, education, and computing and technology. Most classes are asynchronous and students can take courses through any IU campus.

• Graduation Rate: 41%
• Acceptance Rate: 92%
• In State Tuition: $9732
• Out of State Tuition: $21,160
• Application Requirements: GRE scores, academic record

11) Mississippi State University

A solid option for future doctors of philosophy, Mississippi State University is a research-focused and inclusive university. An especially strong option for those looking to study engineering or computer science, MSU offers 9 different majors within those fields.  MSU also offers tracks in plant science and education leadership.

• Graduation Rate: 64%
• In State Tuition: $9,398
• Out of State Tuition: $25,444
• Application Requirements: Academic record, letters of recommendation, personal statement

12) Appalachian State University

Although Appalachian State University is known for its beautiful setting in Boone, North Carolina, App State still has much to offer online students. This school offers only one part-time PhD program in education leadership , but its affordability compared to other programs makes this school stand out.

• In State Tuition: $4,839
• Out of State Tuition: $18,271
• Application Requirements: Academic record, GRE scores or 3.0 minimum GPA, Master’s degree

13) Purdue University

An online doctoral program that maintains a high standard of excellence, Purdue is a great place for future doctors of technology, educational leadership and policy, and higher education. Purdue graduate students can expect to co-author papers with faculty and gain hands-on experience in research.

• Graduation Rate: 38%
• Acceptance Rate: 30%
• Tuition: $420 per credit

14) Concordia University Chicago

Concordia University ’s online doctoral programs allow students to complete their degree in three to five years. Through their shortened term system, students take 8-week classes and then write their dissertation in their final three semesters. A leader in online doctoral programs, Concordia offers paths in strategic innovation, healthcare management, education leadership, and organizational leadership.

• Tuition: $9,090
• Application Requirements: Academic record, Master’s degree with minimum 3.0 GPA, letters of recommendation, portfolio and personal statement varies by program

15) Clemson University

A public school in South Carolina, Clemson aims to prepare online doctoral students for diverse career paths. Proudly offering programs that are difficult to find elsewhere, Clemson graduate students can study healthcare genetics and parks, recreation, and tourism management. Education professionals can complete degrees in education systems, learning science, and teaching, literacy, language, and culture.

• Graduation Rate: 84%
• Acceptance Rate: 49%
• In State Tuition: $10,600
• Out of State Tuition: $22,050
• Application Requirements: Academic record, letters of recommendation, portfolio and personal statement vary by program

The Best Online Doctoral Programs – Additional Resources

Looking to learn more about graduate school admissions? We’ve got you covered.

Admissions Tips

• What is a Good GRE Score?
• Write a Grad School Statement of Purpose in 12 Simple Steps
• How to Get into Grad School with a Low GPA
• Do You Need a Master’s to get a PhD?

More about Online Learning

• Best Online MSW Programs
• Best Master’s in Public Health Online Programs
• Hybrid and Online DBA Programs 
• Graduate School Admissions

Christina Wood

Christina Wood holds a BA in Literature & Writing from UC San Diego, an MFA in Creative Writing from Washington University in St. Louis, and is currently a Doctoral Candidate in English at the University of Georgia, where she teaches creative writing and first-year composition courses. Christina has published fiction and nonfiction in numerous publications, including The Paris Review , McSweeney’s , Granta , Virginia Quarterly Review , The Sewanee Review , Mississippi Review , and Puerto del Sol , among others. Her story “The Astronaut” won the 2018 Shirley Jackson Award for short fiction and received a “Distinguished Stories” mention in the 2019 Best American Short Stories anthology.

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Data Tables

These tables present detailed data on the demographic characteristics, educational history, sources of financial support, and postgraduation plans of doctorate recipients. The Survey of Earned Doctorates (SED) data tables were reorganized and renumbered in 2021; see table B-1 in the " Technical Notes " for a crosswalk comparing the current tables with those prior to 2021. Explore SED data further via the interactive data tool and the Restricted Data Analysis System . Kelly Kang Survey Manager, SED NCSES

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• Excel (.zip 1.1 MB)
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Trends in research doctorate recipient characteristics

Trends in postgraduation commitments of research doctorate recipients, field and demographic characteristics of research doctorate recipients, financial support and education-related debt of research doctorate recipients, educational and background characteristics of research doctorate recipients, postgraduation commitments and salaries of research doctorate recipients, doctorate institutions, locations, and countries of origins of research doctorate recipients, statistical profiles of research doctorate recipients, postgraduation plans of research doctorate recipients.

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Data Analytics Certificate Program

UT Tyler Department of Computer Science

Data analytics is a fast-growing field in the computing sciences, and as more and more companies are recognizing the need to implement data analytics into their daily operations, employment opportunities in this industry are abundant. The Data Analytics Certificate Program is designed to broadly enhance students' opportunities in their future professional careers and/or future graduate studies.

A data analytics certificate can enhance prospects for a successful career:  (1) there is a high demand for data analytics professionals, (2) job opportunities increase, (3) prospective higher wages for qualified professionals, (4) data analytics is a top priority in many organizations, and (5) there is flexibility across the professional employment sector.

Certificate Requirements

Required Courses (9 hrs.)

The certificate requires students to complete 9 semester credit hours (3 courses) from the following existing course set with a grade of B or better in each course. Prerequisites for all certificate courses selected will apply. 

    COSC 5347-Business Intelligence and Analysis   CSCI 5348-Quantitative Investing   CSCI 5334-Data Analytics with Python  

 Courses completed for this certification will be listed as a milestone on an official university transcript and a certificate of completion will be awarded by the Department of Computer Science.

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Master in Public Administration in International Development

Join the next generation of global development leaders in this two-year, economics-centered program

Key Program Information

Program Length: Two years (varies for students pursuing joint or concurrent degrees)

Degree Awarded: Master in Public Administration in International Development

Admission Application Deadline: December 2024

Financial Aid Application Deadline: January 2025

Contact the MPA/ID Program

79 John F. Kennedy Street Rubenstein Building, Rooms 122, 124, and 126 Cambridge, Massachusetts 02138

Be a leader in global development

The Master in Public Administration in International Development Program combines rigorous training in economics and quantitative methods with an emphasis on policy and practice.

The Master in Public Administration in International Development (MPA/ID) Program offers unparalleled training for a professional career in development. The mix of theoretical rigor with practical approaches has proven to be a powerful combination. Our graduates hold influential policy, advocacy, and management positions at international organizations, national governments, non-governmental organizations, and private sector companies.

The right fit

The MPA/ID Program may be the right fit for you if you:

• Demonstrate commitment to solving the economic, social, or political problems facing low-income communities, regions, or nations 
• Work in the development field, whether in government, nonprofits, central or regional banks, international development institutions, research organizations, or the private sector
• Want to deepen and broaden your understanding of development problems and acquire the analytical tools and global perspectives to design and implement effective solutions

“The MPA/ID Program expanded my perspectives and equipped me with a set of analytical tools to make the impact I seek to have in the world.” — Jiawen Tang MPA/ID 2021

About the MPA/ID Program

Training for development practitioners.

The MPA/ID Program is a rigorous, economics-centered program designed to train the next generation of practitioners and leaders in the field of global development.

Within a multidisciplinary core curriculum,  you will take advanced economics and quantitative methods sequences  with an emphasis on key policy applications to development. You will also complete core courses in economic development, politics, political philosophy, and management—integrated with the theory and practice of development.

In your second year, you’ll choose from elective options at HKS, at the other graduate schools at Harvard—such as  Business ,  Design ,  Education ,  Law , and  Public Health  as well as the  Faculty of Arts and Sciences —and at Massachusetts Institute of Technology.

Core Curriculum

• Advanced Microeconomics ( API-109 ,  API-110 )
• Advanced Macroeconomics  ( API-119 ,  API-120 )
• Advanced Statistics and Econometrics ( API-209 ,  API-210 )
• Economic Development: Theory and Evidence ( DEV-101 ,  DEV-102 )
• Applications and Cases in International Development ( DEV-401 )
• The Politics of Development ( DPI-410 )
• Political Philosophy for Development ( DPI-411 )
• Getting Things Done: Management in a Development Context ( MLD-102 )
• Second Year Policy Analysis Seminar ( DEV-250 )

You will gain professional expertise through the case workshop and speaker series, a required summer internship , and an integrative capstone paper .

Second Year Policy Analysis : Using Your Toolkit 

The Second Year Policy Analysis (SYPA) serves as the capstone experience for the MPA/ID Program. You will choose a current development issue of interest to you; select your faculty advisor; and draw on the tools of economics, management, and political analysis to define the problem, analyze the evidence, develop alternatives, and provide specific policy recommendations for a concrete development problem.

Summer Internships : Out of the classroom, into the field

During the summer between your first and second year, you will engage in a development project, typically in a low- to middle-income country other than your own. This field experience allows you to apply the skills you’ve acquired during your first year and explore a new organization, substantive area of interest, or part of the world.

STEM Eligibility

The MPA/ID Program is a designated STEM-eligible program (Science, Technology, Engineering, and Mathematics). Students with F-1 visas may apply to work in the United States for two additional years  beyond the standard 12-month Optional Practical Training (OPT)  following graduation.

Degree Requirements

The MPA/ID Program consists of four semesters of full-time coursework in residence at HKS. The coursework includes the core curriculum, a minimum of six electives (24 credits), a development-related internship, and the Second-Year Policy Analysis.

To graduate, you must:

• Matriculate as a full-time, in-residence student and take 12-24 credits per semester
• Earn at least 76 credits, which must include the required courses, SYPA, and electives
• Finish with a GPA of B or better
• Earn a B- or higher in all required MPA/ID courses

Combined Degrees

You might consider  pursuing a second degree jointly or concurrently  if you’re interested in how the world’s challenges can be addressed at the intersection of international development and business, law, medicine, design, or other fields.

Pursuing a joint or concurrent degree reduces coursework and residency requirements and makes it possible to earn two degrees in a shorter amount of time.

Joint Degrees

As an MPA/ID student, you can pursue a  joint degree —either an MBA at  Harvard Business School  or a JD at  Harvard Law School —that involves carefully crafted and integrated coursework.

Concurrent Degrees

You can pursue a concurrent degree in business, law, medicine, design, or another field—as long as it is:

• A professional degree (for example, an MBA, MD, or JD; not a PhD or an academic master’s degree)
• At least a two-year program
• Completed at a partner school

The concurrent degree program allows you to pursue degrees at HKS and at a partner school; however, the coursework is not as closely integrated as the joint degree program. As a concurrent degree student, you are responsible for weaving together the two halves of your learning experience.

Faculty & Research

Where ideas meet practice.

Our faculty members are changing the ways in which poverty and underdevelopment are analyzed and approached.

MPA/ID faculty members are scholars  and  practitioners working with governments, international organizations, and NGOs. They are  diagnosing economic woes and helping develop cures , bringing  real-world development and political experience  to bear on complex challenges, and helping people  escape poverty  by understanding what hinders development progress.

MPA/ID Faculty Research

Why taxes are vital to development

Economist Anders Jensen has long been intrigued by differences in state capacity and the role of public finance in building and boosting capacity. 

Looking at the world through a wider lens

The thread running through Professor of Public Policy Eliana La Ferrara’s work is an unwillingness to limit herself to traditional microeconomic models.

Diagnosing economic woes and helping develop cures

Professor Ricardo Hausmann’s Growth Lab is training students and practitioners to develop prescriptions for economic growth.

Bringing real-world experience to bear

Juan Jimenez MPA/ID 2010 has returned to HKS to share valuable wisdom gained from high-level development policy positions in the government of the Dominican Republic.

MPA/ID Core Faculty Members

Dani Rodrik

MPA/ID Faculty Chair; Ford Foundation Professor of International Political Economy

Matthew Andrews

Edward S. Mason Senior Lecturer in International Development

Arthur Applbaum

Adams Professor of Political Leadership and Democratic Values

Luis Armona

Assistant Professor of Public Policy

Jeffrey Frankel

James W. Harpel Professor of Capital Formation and Growth

Jeffrey Cheah Professor of South-East Asia Studies

Ricardo Hausmann

Rafik Hariri Professor of the Practice of International Political Economy

Anders Jensen

Associate Professor of Public Policy

Juan Jimenez

Lecturer in Public Policy

Asim Khwaja

Director, Center for International Development;  Sumitomo-FASID Professor of International Finance and Development

Eliana La Ferrara

Professor of Public Policy

Senior Lecturer in Public Policy

Celestin Monga

Adjunct Professor of Public Policy

Gautam Nair

Carmen Reinhart

Minos A. Zombanakis Professor of the International Financial System

Federico Sturzenegger

Our alumni do development differently

Around the world, MPA/ID graduates are in pivotal roles, leading development.

Inside governments and traditional development organizations, and outside the box in startups and social enterprises, MPA/IDs are changing the way development is done.

Our graduates hold influential policy and management positions in a wide range of international organizations, national governments, central and regional banks, nonprofit and research organizations, and private sector companies. 

Where do MPA/ID graduates work?

Learn more about how MPA/ID alumni are shaping development.

Dalia Al Kadi MPA/ID 2011

Dalia Al Kadi MPA/ID 2011 is a Senior Economist in the Macroeconomics, Trade and Investment Global Practice at the World Bank in Washington, D.C. Prior to joining the World Bank, Dalia worked as a Project Manager at the Abu Dhabi General Secretariat of the Executive Council.

Abdulhamid Haidar MPA/ID 2021

Abdulhamid Haidar MPA/ID 2021 is the founder of  Darsel , a non-profit aimed at bridging the digital divide. In Haidar’s words, “The [MPA/ID] curriculum, faculty, and incredible student community all played an integral role in Darsel’s development and its positive impact on education in developing countries.”

Katherine Koh MPA/ID-MBA 2008

Katherine Koh MPA/ID-MBA 2008 is the Principal Investment Officer and Global Climate Lead for Infrastructure at the International Finance Corporation (IFC).  In  Putting Climate at the Heart of IFC Infrastructure Business , she describes “the transition to a low-carbon and resilient global economy—and the need for climate-smart infrastructure solutions—(as) among the most urgent and important issues of our time.”

Johannes Lohmann MPA/ID 2017

Johannes Lohmann MPA/ID 2017 is an Executive Director at Pollination.  Johannes advises a range of public and private sector clients on their transition to net zero, and on decarbonization and nature positive strategies. Previously, Johannes worked as Head of Work and Financial Behaviour at the Behavioural Insights Team, advising public and private sector partners on topics such as green jobs and sustainable pensions.

He “Charlie” Tian MPA/ID 2015

He “Charlie” Tian MPA/ID 2015 is a Senior Professional/Project Team Leader at New Development Bank in the Project Sector Department. He joined the New Development Bank a few weeks after its establishment in 2015. Since then, he has worked on projects in renewable energy, green transportation, and social infrastructure, totaling $5 billion of the Bank’s investments.

Information sessions

Mpa/id at a glance.

*Statistics are based on a five-year average.

Featured MPA/ID stories

A mission to develop equality.

Economist Ganchimeg Ganpurev MPA/ID 2021 was moved to shift her focus by the startling inequality she saw in her homeland.

Complementing economics with soft skills

Isidro Guardarucci MPA/ID 2018 adds soft skills to his economics toolkit.

Delving deeper into development

A desire for more grounding in economic theory led Zainab Raji MPA/ID 2022 to  the HKS/HBS joint degree program.

“Every day is an opportunity to weave together economic theory and development practice using the insights from my professors and classmates’ own professional and personal experiences.”

Miguel ventura mpa/id 2024 (philippines), applying to the mpa/id program, what we look for, career focus.

Most students admitted to the MPA/ID Program have at least two years of development-related work experience in government, nonprofits, central or regional banks, international development institutions, research organizations, or private businesses. Usually at least some of the work has been in developing countries.

Quantitative Analysis

We also look for applicants who are interested in applying quantitative analysis and economics to development policy design.

Prerequisites

To apply to the MPA/ID Program, y ou must have:

• A bachelor’s degree with a solid academic record, including strong grades in economics and mathematics courses
• Completion of at least one university-level course each in microeconomics, macroeconomics, and calculus through multivariable calculus (usually part of a three-course college sequence). Applicants may satisfy some of these prerequisites after submitting an application as long as they are completed before the program starts. Statistics and linear algebra courses are desirable, but not required. 

How to Apply

A complete application to the MPA/ID Program includes: 

• Online application
• Three letters of recommendation
• GRE or GMAT required; in general, you are most competitive for admission if your quantitative section score is 160 or above on the GRE, or 48 or above on the GMAT.
• Non-native English speakers who did not earn an undergraduate degree conducted in English must submit TOEFL, IELTS, or Cambridge English exam results. We recommend an overall TOEFL score of at least 100 on the iBT or an overall band score of 7 on the IELTS.
• Academic transcripts
• $100 application fee or waiver

Read more about how to apply .

The application for the 2025-2026 academic year will be available in September 2024. There is one admission application deadline and one start date for each degree program per year. You may apply to only one master’s degree program per admissions cycle. 

Tuition & Fees

The cost of attendance for the 2024-2025 academic year is outlined in  Funding Your Master’s Education  to help you plan financially for our master’s degree programs. Living expense costs are based on residence in Cambridge. The 2025-2026 academic year rates will be published in March 2025. HKS tuition and fees are subject to change without notice. 

Financing your education is a partnership—we are here to help guide you. You are strongly encouraged to explore all funding opportunities .

Joint Japan/World Bank Graduate Scholarship Program

The MPA/ID Program is a  participating program  of the  Joint Japan/World Bank Graduate Scholarship Program  (JJ/WBGSP). The scholarship provides tuition, a monthly living stipend, round-trip airfare, health insurance, and travel allowance. The JJ/WBGSP is open to citizens of certain  developing countries  with relevant professional experience and a history of supporting their countries’ development efforts.

Learn more about the HKS community

Center for international development (cid).

CID is the intellectual home of MPA/ID students and faculty members. It seeks to advance the understanding of development challenges and offer viable solutions to problems of global poverty.  Learn more from its director, Professor Asim Khwaja , and read about the work and perspectives of those in the CID community.

Student Life

Student stories, admissions & financial aid blog.