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2024 Best Statistics Doctor's Degree Schools

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20 Best Doctor of Statistics Graduate Schools

Reviewed by David Krug David Krug is a seasoned expert with 20 years in educational technology (EdTech). His career spans the pivotal years of technology integration in education, where he has played a key role in advancing student-centric learning solutions. David's expertise lies in marrying technological innovation with pedagogical effectiveness, making him a valuable asset in transforming educational experiences. As an advisor for enrollment startups, David provides strategic guidance, helping these companies navigate the complexities of the education sector. His insights are crucial in developing impactful and sustainable enrollment strategies.

Updated: March 17, 2024 , Reading time: 33 minutes

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Statistics is a field that has grown more relevant today because of the growing footprint of big data and, subsequently, data science and machine learning. But before big data in the information and computing sciences, data has always been the primary currency of statistics.

A Doctor of Statistics degree is a postgraduate degree program designed to offer students an in-depth understanding of quantitative methods, theories, and applications. Students learn about principles of data modeling, including probability theory and multivariate calculus, the development of statistical models, algorithms for inference, and data analysis.

They also learn about the application of data analysis in organizations and industries, including the healthcare, finance, insurance, telecommunications, and education sectors.

This discipline encompasses data collection, sampling, analysis, and interpretation. The discipline also touches on experimental design, simulation of conditions, computational analysis, predictions (through the concept of probability), and many more. 

In addition to coursework, students often take part in industry internships and collaborative research opportunities. Doctoral programs in Statistics typically culminate in a comprehensive dissertation or research project. With a Doctor of Statistics degree, professionals are prepared to pursue top jobs in academia, government, industry, and commerce.

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This elemental nature of statistics is what makes it versatile across industries, from agriculture to food science, to natural sciences like botany, geography, oceanography, and even meteorology, to engineering and the applied sciences, medicine, life sciences, athletics, and of course, social sciences, which is one of the first disciplines to utilize statistics.

Aside from providing empirical and quantitative results, it can also create simulated environments for intangible sample data, where the conditions affecting the data cannot be captured physically or in real-time.

An example would be statistical research involving meteorology, anthropology, microbiology, and even social science, where quantifying human behavior remains an ever-expanding and evolving domain.

Statistics also help government agencies and policy-making institutions do just that and form evidence-based policies and regulations based on quantitative data.

Check out this career guide on becoming a Statistician:

METHODOLOGY

For the top Doctor of Statistics Graduate Schools, we’ve based our selections on the following points:

• Preference is given to schools with active research and initiatives across other disciplines or departments. The involvement may be at the faculty level or the department level through the availability of research centers and institutes or their affiliations. 
• Preference is also given to programs with a long roster of faculty involved in research, whether in core or theoretical statistics research or interdisciplinary work. It is valuable for students looking to engage with a prospective adviser as they prepare for dissertation work. A faculty with vast experience in a similar discipline or field of study as the student’s budding research will help the student be nudged in the right direction regarding references, insights, and overall study design.
• Preference is also given to programs that offer statistical consultancy services, as most of these initiatives are helmed by graduate students themselves, thus allowing them to work with non-statisticians within a project.
• Additionally, a factor in the rankings is its Ph.D. completion census over the last ten years, as collected by the National Center for Education Statistics (NCES) and published by the American Statistical Association (ASA).

For more information, see our Methodology page.

20 BEST DOCTOR OF STATISTICS GRADUATE SCHOOLS

Boston university.

Ph.D. in Statistics

Boston University (BU) was established in 1839 in Vermont before moving to its permanent home in Boston, MA. Shortly after, one of its faculty members successfully invented the telephone in one of its laboratories – Alexander Graham Bell in 1876.

BU is classified as an R1 research university. It has also produced several acclaimed scholars and graduates – from Nobel prize laureates to MacArthur fellows to Pulitzer winners. 

• BU’s Ph.D. program comes in three tracks: Statistics, Probability, and Mathematics. 
• Students under the statistics track must complete at least eight core courses at a Ph.D. level proper. Those who have not earned a master’s degree yet must complete sixteen courses, with a maximum of four courses allowed for transfer.
• Proficiency in at least one foreign language—French, German, or another major foreign language—is required.
• Students must complete the following coursework in sequence: Probability Theory I and II, Estimation Theory, Hypothesis Testing, and Advanced Statistical Methods I and II.
• To qualify for doctoral candidacy, they must satisfactorily complete the exams on applied statistics, mathematics, and probability. 

Standout Features of the Program:

BU is committed to funding students’ first five years of doctoral study, which, with good academic standing, could go longer than that. In addition, first-year students are eligible for teaching fellowships and the Dean’s Fellowship. Both provide an annual stipend of $23,340, on top of an $11K stipend for the summer term. 

Doctoral students in statistics are eligible for a free membership from the Society for Industrial and Applied Mathematics ( SIAM ), the American Mathematical Society ( AMS ), and the Association for Women in Mathematics ( AWM ).  They can also avail themselves of discounted memberships from the ASA and other related professional societies. 

North Carolina State University 

NC State’s Department of Statistics was established in 1941 with statistician Gertrude Cox, famous for pioneering the statistical tenet Experimental Design, at the helm. Its graduate program would later become a research powerhouse, posting great numbers in post-grad career placement.

From 2003 to 2020, NC State has surpassed other universities for the total number of Ph.D. in Statistics degrees conferred, with 332 graduates. It has also topped the list for 2019 and 2020, with 23 and 25 graduates, respectively. 

• The Ph.D. in Statistics program requires the completion of 54 to 72 credit units (depending on where the student earned the master’s degree), which combines coursework and a dissertation. 
• The featured required courses in Statistical Consulting expose students to professional consulting activities involving the faculty and prospective or current clients.
• Aside from the usual funding sources – TA, RA, fellowship, and grants – a  Graduate Industrial Traineeship (GIT) is a unique funding opportunity made possible by the department’s collaborations with government and private firms.  

Stat Ph.D. students looking to apply to the GIT funding program can apply to the multi-industrial facilities and companies at Research Triangle Park . This economic hub, which has a longstanding partnership with NC State, can also be a good jump-off point for a career after grad school. 

The best-selling statistical software SAS was developed at NC State as a project of the Department of Agriculture in 1966. Ten years and 100 clients later, the founding developers established the SAS Institute , one of the biggest private software companies globally and a major benefactor of the SAS Hall, which houses the Department of Statistics. 

Purdue University

The Purdue Department of Statistics is well represented in various mathematical and statistical professional circles, such as the Institute of Mathematical Statistics (IMS), the American Association for the Advancement of Science or AAAS, the International Statistical Institute (ISI), an NSF CAREER award, with five Department of Statistics alumni emerging as winners from 2009 to 2012. Also, two of the department’s professors emeriti have sat as president of the ASA, which welcomed nine fellows from Purdue. 

• Applicants to the Ph.D. in Statistics program must have a working knowledge of probability, mathematical statistics, and regression. 
• The program is as follows: four qualifying exams covering the core coursework, followed by a preliminary exam to determine the student’s readiness for research. A final examination or oral dissertation defense follows.
• All doctoral candidates must hold at least one semester-long teaching experience. 

Statistics Ph.D. students can avail of the vast IT resources available both at the department and university levels. At least 35 servers running on Ubuntu OS are available, with the largest having 168 TB to boot. Software programs running on R and Python are also available, and statistical computing applications like SAS, Matlab, and Minitab. 

At least six interdepartmental research groups are conducting investigative work with Discovery Park , Purdue’s very own research hub. These research groups represent the department’s collaboration with other disciplines like medicine, bioengineering, environmental science, entrepreneurship, and biosciences.

Florida State University (FSU)

Founded in 1959, the FSU Department of Statistics was home to famous statisticians, such as Richard Savage and Frank Wilcoxon. Today, its faculty has expanded the department’s accolades through fellowships in professional societies, holding editorial roles in peer-reviewed journals, and receiving government grants, such as those from the National Science Foundation (NSF). 

• The Department of Statistics offers two tracks towards the doctoral degree: the Statistics and Biostatistics track.
• Students in both tracks must take the Ph.D. qualifying exams for the following courses: Statistics in Applications I and II, Distribution Theory, Statistical Inference, and Advanced Probability and Inference I. Additionally, Computational Methods in Statistics and Epidemiology for Statisticians are required for Statistics majors and Biostatistics majors, respectively.
• An essay exam for doctoral candidacy admission and oral defense for the dissertation are required for both tracks. 
• Students are also required to document their participation in extracurricular activities such as colloquia, utilization of all physical facilities, interdepartmental or group research, and other academic and professional experiences. These are all required for program completion as well. 

The department encourages students to pursue interdisciplinary investigative work. The interdisciplinary option requires students to take at least three additional courses in the secondary discipline of choice in preparation for the research work. Additionally, the Supercomputer Computations Research Institute is at the student’s disposal for research requiring complex computations. 

The Department of Statistics is currently involved in several types of interdisciplinary research involving oceanography, meteorology, and engineering. Two faculty members of the department are currently studying the application of nonlinear time series models in predicting climate changes and forecasts.

Ohio State University

The Department of Statistics at OSU was established in 1974, although the discipline has been in existence since the ’60s through the Department of Mathematics and the Statistics Laboratory. It would later develop and offer interdisciplinary programs such as biostatistics and data analytics in the succeeding decades.

• Applicants to the Ph.D. program in Statistics must have a solid background in Applied Mathematics, Advanced Calculus, and Real Analysis.
• Students must take a course on either Statistical Consulting and Collaboration or Biostatistical Collaboration on top of the required core courses.
• Once admitted to doctoral candidacy, attendance to departmental colloquia is required. 
• There are four exams: two qualifying exams for the coursework, the doctoral candidacy exam, and the oral dissertation defense.

Students can utilize the 11 computers housed by the department, which run on both Linux and Windows servers. They also have access to the Ohio Supercomputer Center and various statistical software programs like SAS, SPSS, Mathematica, and many others. 

The Department also offers an alternative Ph.D. program for statisticians interested in interdisciplinary work. The Interdisciplinary Ph.D. program in Biostatistics is a joint offering from the Department of Statistics and the College of Public Health. It offers two concentrations: Methodology and Public Health.

University of Arizona

From UA’s College of Mines to the School of Mathematics, math and statistics have been deeply embedded into the university curriculum as early as the late 19 th century.

Known today as the Department of Mathematics, its programs are strongly founded on research and interdisciplinary activity, particularly statistics, data science, engineering, and biosciences (biostatistics).

• The department offers two tracks for its Ph.D. in Statistics offering – the regular and the statistical informatics track. Both require the completion of 71 credit units, which includes an 18-unit dissertation. 
• Core courses include Theoretical Statistics and Theory of Linear Models
• Of the 50+ elective courses available, at least 12 elective units are required
• Core courses include Statistical Machine Learning
• Elective requirement: at least 21 credit units are required. The choices of electives are grouped as follows: general, bioinformatics, business and management informatics, computing, geographic information systems, medical informatics, and an individualized theme as per department approval. Six units should come from the general group and another six units from the other course groups. The rest of the remaining units can come from any of the seven-course groups. 
• Both tracks require students to take Scientific Writing Presentation and Bioethics, Statistical Computing, and Scientific Grantsmanship courses.

A Ph.D. minor is required for both tracks. At least three units are required under any of the following minors:

• Computer Science
• Mathematics
• Applied Mathematics
• Ecology and Evolutionary Biology
• Biostatistics
• Information Resources and Library Science
• Agricultural and Biosystems Engineering ( available with the informatics track only )

Applicants to either Ph.D. in Statistics program are required to have the following:

• Have taken multivariable/vector calculus courses (worth at least three semesters), 
• Have taken a course on linear algebra, and,
• Has solid experience in computing systems and processes, such as data analytics and mining, among others.

University of California Los Angeles (UCLA)

The discipline of Statistics at UCLA started from four academic channels during the 1930s: the Departments of Mathematics, Biostatistics, and Biomathematics, and the Division of Social Sciences.

The Department of Statistics would later be launched more than fifty years later in 1998, boasting programs and research that combine theoretical and computational coursework with interdisciplinary applications. 

• Aside from mathematics and statistics, preference will be given to Ph.D. applicants with solid backgrounds in computer science, engineering, and other related disciplines like public health or bioinformatics. 
• The core of the curriculum is structured into theoretical, application, and computational courses related to the discipline.
• Admission to the Ph.D. program is only every fall term.
• Students are also required to render teaching assistantship services for at least one term.

Students can choose to research the following areas under which many of the Statistics faculty have done their research:

• Experimental design 
• Environmental Statistics
• Computational statistics, which includes AI and machine learning
• Social statistics
• Bioinformatics
• Applied multivariate analysis

Students can choose to engage with faculty who have researched any of the following areas in an advisory or consultancy role.

The Department is home to three research centers that collaborate across different disciplines to produce studies and literature that demonstrate the value of statistics in these different industries. These are the:

• Center for Statistical Research in Computational Biology , where statistics intersects with health and life sciences
• Center for Vision, Cognition, Learning, and Autonomy ,  where statistics  intersects with information systems and adaptive technologies, and,
• Center for Social Statistics , where statistics intersects with its oldest collaborators – the social sciences and related disciplines.

Virginia Polytechnic Institute & State University (Virginia Tech)

The Department of Statistics was established in 1949. The department offers Statistics programs that are well-rounded, touching on every industry and other possible applications of the discipline.

• Sports Analytics
• Statistics for Business, Government and Industry
• Computational Statistics
• General  Methodology and Theory (classical track)
• It requires the completion of 90 course credits, which combines coursework and research for both the master’s and doctoral levels. 
• Ph.D. students are required to render teaching duties for one term and to undergo three semester-long professional training. The training involves exposure to statistical collaboration under a relevant industry based on the student’s choice of concentration.

The department has bilateral partnerships with multinational companies such as Shell and Capital One. Employees from partner companies can avail of the courses for free or pursue a statistics degree at a reduced rate. VT graduates can take advantage of this partnership to leverage themselves and their credentials for career placement.

Recognizing that Statistics is not for everybody, the department offers two offices that offer free tutorial and consultancy services for non-statisticians. The STAT Lab (Statistics Tutoring All Together) is a free tutorial service helmed by statistics graduate students for statistics undergraduates. On the other hand, the Statistical Applications and Innovations Group (SAIG) is a consultancy office that offers free statistical aid to VT students, faculty, and staff outside the department. It also offers free short courses , such as utilizing R software and machine learning.

University of Michigan Ann Arbor

Ph.D. in  Statistics

U-M’s Statistics program has consistently been hailed as one of the best in the country, ranking among the top 10 across all school-ranking publications, including the National Research Council’s 2010 rankings.

The department has always collaborated with other U-M departments and schools, such as but not limited to the Ross School of Business and the Department of Industrial & Operations Engineering , to offer a holistic and relevant Statistics program at all levels. 

• All courses under the “Methods” and “Practice” groups must be completed
• At least two courses each must be completed under the “Statistical Theory” and “Probability” groups
• At least one course must be completed under the “Computing” group
• The following courses are also required: Research Ethics and Introduction to Research Tools, and, Technical Writing in Statistics. 
• A minimum of three cognate courses or non-statistic and non-departmental courses are also required before taking the doctoral candidacy exam. 

Standout Features of the Program

The NSF recently awarded the department a research training grant to study modern methodologies for analyzing dynamic and complicatedly structured big data, which is ubiquitous today, thanks to social media. It also aims to train U-M students of all levels, from undergraduate to post-doc, on these new techniques and subsequently apply them to their research or professional work. 

The Ph.D. in Statistics student council actively maintains a Wiki page for all things statistics and its relevant applications and developments. Access to this page requires a U-M online login credential.

University of Wisconsin Madison

UW’s Department of Statistics was instituted in 1960. Since then, it has conferred close to a thousand graduate degrees, with about half being doctoral degrees. The department prides itself in offering research and training programs, thus preparing graduates of the discipline for statistical work or research in any industry or field.

• The Department offers two Ph.D.-leading tracks: Statistics and Biostatistics tracks. The application process for both tracks is uniform, and students can switch tracks seamlessly. 
• GRE scores are currently not required for application for the Fall 2022 term.
• The documentary requirements for the application are transcripts, CV, letters of recommendation, a statement of purpose, and supplemental information as required by the online application. All documentary requirements should be submitted online.

The faculty of the Department of Statistics is also associated with the Departments of Mathematics, Computer Sciences, Electrical and Computer Engineering, and Botany. Students looking to engage in interdisciplinary research are very fortunate to have many faculty members as advisers because of their breadth of knowledge and expertise that spans beyond statistics. 

In line with the department’s longstanding tradition of collaborative work within UW, it is currently involved in five interdisciplinary initiatives. These are:

• Biometry – a collaboration with the College of Agricultural and Life Sciences (CALS) , which also houses the Statistical Consulting Service , which aids all UW students with their statistical and computational needs
• Biostatistics and Medical Informatics 
• Machine Learning for Medical Imaging (ML4MI)
• Data Science Hub
• Institute for Foundations of Data Science

Iowa State University

ISU’s Department of Statistics has its roots embedded in the Iowa Agriculture Experiment Station when the latter instituted the Statistical Section in 1935, later becoming the Department of Statistics.

Since 2000, the department has consistently ranked among the top 10 statistics programs in the country regarding research activity  (especially interdisciplinary research), curriculum rigor, job placement, and student census. 

• The Ph.D. program in Statistics requires the completion of 72 credit units. 
• To be admitted for doctoral candidacy, students must complete the preliminary exams, one written and one oral. The oral prelims represent the dissertation topic proposal.
• Foundations of Probability Theory
• Advanced Probability Theory
• Advanced Statistical Methods
• Advanced Theory of Statistical Inference
• Plus 9 to 12 elective credits.

Students can opt to co-major in another area or discipline while also pursuing a doctorate in Statistics. It has been a common pathway for many students who are keen on interdisciplinary studies. The common co-majors include engineering, agriculture, genetics, and computer science. With this option, students will have to choose a Statistics track that is more relevant to the second major, Applied Statistics or Theoretical Statistics.

The department is home to three research centers that collaborate across the academic ecosystem, such as forensic science , health informatics, botany and biology , and experimental design , to push the envelope and demonstrate the value of statistics in fields that require statistical analysis to derive empirical results. 

Stanford University

Stanford’s Department of Statistics has always been motivated by interdisciplinary collaboration since its establishment in 1948. As a start, two members of its pioneering faculty are also affiliated with the Departments of Psychology and Economics – Quinn McNemar and Kenneth Arrow, respectively.

The succeeding decades saw the continuation of joint faculty appointments, which resulted in the seamless integration of Stanford Statistics with other schools and departments like Engineering, Medicine, Liberal Arts, Natural Sciences, Business, and Food Science.

• The doctoral program in Statistics requires the completion of 135 credit units, with at least ten units taken every term. Stanford observes a quarter-term academic calendar.
• Linear algebra
• Matrix theory
• Real variable functions
• Probability theory
• Statistical Inference
• Python Programming
• For doctoral candidacy admission, students must pass two of the three exams in the following areas: theoretical statistics, applied statistics, and probability theory.
• To prepare students for interdisciplinary work , they must take at least three courses in a discipline that is outside yet relevant to statistics, like engineering, information science, social science, natural science, biology, or mathematics. 

While Stanford observes a quarter system, students of the Department of Statistics are not required to be in residence or enroll for the summer terms, except for first-year students. Despite this, the department still holds lectures and other campus sessions, with which students can electively attend.

Stanford Statistics emphasizes the increasing value and relevancy of interdisciplinary research. It strongly encourages its students to join such projects or research groups, or form their own, as the department has received generous funding from the NSF for this very purpose. 

University of Connecticut

UConn’s Department of Statistics was instituted in 1962, with a faculty of seven professors. Today, most of its 23 professors are recipients of NSF and NIH grants. They are also recipients of the Microsoft Azure Research Award and the prestigious NSF CAREER Award.

• Doctoral students in Statistics are required to complete 18 courses, especially those coming from the undergraduate level. 
• Mathematical Statistics
• Applied Statistics
• Linear Models
• Theory of Statistics
• Measure Theory and Probability Theory
• Design of Experiments
• Investigation of Special Topics
• As for the electives, students are strongly encouraged to look into Biology, Computer Science, Mathematics, or Economics courses. One to two courses should suffice. 
• The department will provide funding for 4 to 5 years (maximum).

The faculty of the Department of Statistics is composed of 20+ professors with a vast breadth of experience in interdisciplinary research; from theoretical statistical subdisciplines like multivariate analysis, probability, and statistical computation to interdisciplinary statistical approaches such as biostatistics, bioinformatics, statistical genomics or econometrics, there is no shortage of research advisers from the department, regardless of the student’s chosen field of study or research. 

Ph.D. Statistics students lend their time to the Statistical Consulting Service (SCS) , which fields inquiries from UConn and non-UConn clients. From experimental design to grant writing assistance, data modeling, statistical computation, and analysis results in interpretation, or software troubleshooting, the SCS team, is ready to assist statistically, regardless of the nature of the research or project. Simple queries are free of charge, while project engagements usually cost $40 per hour.

The University of Chicago

UChicago’s Department of Statistics was formally launched in 1949 with two main objectives: providing further insight into advanced statistics through research and applying statistics in other fields of study.

Today, both the faculty and the students are guided by these same motivations as they expand the utilization of statistics to modern disciplines (such as adaptive technologies and medicine) beyond the traditional ones (natural and social sciences). 

• Probability
• Computational Mathematics and Machine Learning
• Each course is accompanied by a preliminary exam held at the beginning of the sophomore year. 
• The program takes as short as three to five years. 
• Applicants to the Ph.D. program are encouraged to develop a solid background in advanced mathematics courses like calculus, linear algebra, and real analysis. Experience in computer programming and another discipline that involves empirical data analysis is also preferable but not required. 

The faculty of the Department of Statistics built a portfolio of work intersecting the fields of biostatistics, neuroscience, genetics, machine learning, chemistry, environmental science,  econometrics, and finance. 

The department offers free statistical consultative services to other UC students or faculty members who need statistical assistance with their projects. Helmed by the department’s graduate students, mostly doctoral candidates, the services are confined to theoretical and applied statistical insight at every level of research – from experimental design, sampling, prediction, computation, and simulation to interpretation. Statistical software assistance or troubleshooting is not included in the service. 

Carnegie Mellon University

Ph.D. in Statistics (regular and joint degrees)

In 1966, CMU instituted the Department of Statistics (now known as the Department of Statistics and Data Science) and consistently produced graduates with advanced degrees. The introduction of joint Ph.D. degrees started in the mid-’90s, with Public Policy and Machine Learning as the pioneer offerings.

• Engineering and Public Policy – where statistics meets risk analysis in formulating policies governing communication technology infrastructures, sustainable resources policies, and overall R&D regulations. 
• Neural Computation – where statistics meets neuroscience experiments involving cognition, among others.
• Public Policy – where statistics meets research that forms the basis for various public policies for various sectors and industries.
• Machine Learning – where statistics meets computational science and data analysis in the design of experiments to further develop adaptive technologies. 
• The core coursework comprised of seven courses is required for all Ph.D. students.
• All Ph.D. students must complete an Advanced Data Analysis (ADA) Project, different from the dissertation requirement. One of its objectives is for students to learn to effectively engage and work with non-statistical professionals on any project of any nature. 

Before a student can deliver a dissertation proposal, they must first demonstrate a forte, or an “Area of Strength,” a course or a subdiscipline where they excel with exemplary grades as proof. Common “areas of strength” demonstrated by previous students include theoretical, applied, and computational statistics.

Unlike other Statistics programs in the country, the department does not require qualifying exams for coursework competency and doctoral candidacy admission. 

Cornell University

Statistics at Cornell University has been taught since pre-WWII. The discipline grew from then on and can be attributed to the different notable faculty who came in and subsequently left, like Prof. Jacob Wolfowitz, who pioneered the concept of interdisciplinary collaboration.

This led to the decentralization of the field within Cornell, with various statistic research groups existing in different departments. In 2005, the Department of Statistical Sciences was formally launched, housed by the Faculty of Computing and Information Science (CIS). 

• The Ph.D. program in Statistics requires applicants to have, at the very least, taken coursework or have a solid background in statistics, computer science, or advanced mathematics. 
• Upon satisfactory completion of the required coursework, students must undergo two exams: one for doctoral candidacy admission (A exam) and one for dissertation defense (B exam).
• Applicants with only a bachelor’s degree are also accepted, as the program also confers a master’s degree in statistics, but not as a terminal degree, but rather as a continuing degree towards the doctorate. 
• Admission is only every fall term. Tuition costs $20,800. Two to four recommendation letters are accepted, and GRE scores are also recommended (confirm with admissions if this is optional).

Students are required to run their computations and other modeling tasks via the statistical software program SAS . Software license can be purchased through the Cornell licensing store and is valid for one year. SAS runs on either Linux or Windows platforms. 

The department houses a collection of previously accepted dissertations along with the post-Ph.D. career information of the student-authors. This page is a valuable resource for students preparing for their dissertations or career jump points after earning a doctorate.

University of California Berkeley

Ph.D. in Statistics

Berkeley Statistics, founded in 1938, has always been at the forefront of research and interdisciplinary innovation. Proof of this is its highly acclaimed faculty, many of whom are National Academy of Science members, MacArthur Genius Grant winners, and recipients of the prestigious National Medal of Science. 

• Ph.D. in Statistics students are expected to take courses on Theoretical and Applied Statistics and Probability during their first year of study.
• Computational and Data Science and Engineering
• Computational and Genomic Biology
• Students are required to hold in-campus residence for four semesters.
• Students need to complete only one qualifying exam, the one for doctoral candidacy admission.

During the summer term of the first year of study, students are required to participate in any of the following activities:

• Graduate teaching assistantship
• Reading course
• Short research
• Other relevant research activities as approved by the department.

The recently opened residence hall for first-year students, Blackwell Hall, was named after one of Berkeley Statistics’ founding fathers – David Blackwell. He is also the first professor of African-American descent to be granted tenure status at the university. 

Pennsylvania State University

By the numbers, Penn State Statistics is indeed a powerhouse in the discipline, particularly in scholarly research. Eighteen of its faculty are elected fellows of the ASA, ISI, and the AAAS, members of the National Academy of Sciences, and National Medal of Science award recipients. 

• Statistical computation
• Statistical ecology
• Only one preliminary examination is required, the one for doctoral candidacy admission. The exam tests the student’s competency in probability, applied and theoretical statistics, and the Monte Carlo methods. 
• Students are expected to undertake a written and oral exam for the dissertation proposal by the second and third years.

Standout Features  of  the Program:

A dual Ph.D. degree option is also available. There are two programs to choose from: 

• Operations Research – which combines research techniques from engineering, economics, mathematics, and the sciences. This second major should be useful for Statistics doctorates who aspire to focus on experimental design and simulations.
• Social Data Analytics – largely draws from social science and data science to formulate methodologies to ethically extract, analyze, and utilize big data, both a predictor and an upshot of human behavior online.

Students can apply to any of these degrees once they have been admitted into the Statistics Ph.D. program. A separate admission committee and chair govern each of the programs. 

Penn State Statistics has consistently ranked among the country’s and the world’s best programs. The National Research Council recognized it among the top 15 in 2010, while a more recent world ranking, the 2019 Academic Ranking of World Universities (ARWU), pitted the program as the 23 rd best in the world. 

Columbia University

Long before the department’s inauguration in 1946, statistics had already been taught at Columbia for more than a decade. The department started with four faculty members and conferred its first Ph.D. degree in 1947.

In the early 2000s, it saw an unprecedented rise in enrollees, forcing the department to relocate after it already did once during the ’60s.

• The Ph.D. in Statistics program usually spans four to six years which culminates with a dissertation. Only full-time enrollment is allowed.
• Applicants must have a working knowledge of linear algebra and advanced calculus. 
• Doctoral students can expect full funding for up to five years, provided good academic standing. Other funding sources are fellowships, TA and RA work, and travel subsidies for conferences and other academic activities outside Columbia. 
• A J.D./Ph.D. option
• An M.D./Ph.D. option
• An additional concentration in Mathematical Structures for Environmental and Social Sciences to complement the doctorate in Statistics.

The department is affiliated with three research centers: 

• The Applied Statistics Center , which is under the Institute of Social and Economic Research and  Policy,
• The Center for Applied Probability , which is under the School of Engineering and Applied Science, and,
• The Grossman Center for the Statistics of Mind pursues studies on neuroscience while utilizing statistical methods. 

The department offers a free statistical consultancy service exclusive to students, faculty, and staff of Columbia University. Engagements are on an appointment basis and can be booked by emailing [email protected] . It is advised to lead with the following pertinent information in the email for expedited service: 

• Specific consulting service needed (e.g., experimental design, computation, analysis, interpretation, etc.)
• Source of data and the collection method employed
• Objectives of the research project
• Statistical methods employed and software used (if any), and, 
• The timeline of the project.

Texas A&M University

TAMU’s Department of Statistics was established in 1962 with two main objectives: expanding statistical research and conferring graduate degrees under the discipline.

What started as a class of 12 graduate students and five faculty members had grown into a thriving department of 44 faculty members deeply involved in both core and interdisciplinary or collaborative research.

• Depending on the student’s educational attainment, the Statistics program’s required coursework ranges from 96 to as little as 42 credit units, including a 4-unit seminar and a 2-unit statistical consulting requirement.
• The statistical consulting requirement spans two terms and must be completed before the end of Year 4. 
• Five of the seven core courses will be covered in the oral qualifying exam for doctoral candidacy admission. These are Statistical Computations, Statistical Methodology I and II (Bayesian Modeling), Probability, and Theory of Linear Models. 
• Residency is also required for admission to candidacy. The minimum residency is two successive terms or one academic year.

Standout Features of  the Program:

The department is involved in several interdisciplinary studies in agriculture and to x icology , among many others. It is also instrumental in the establishment of the Center for Environmental and Rural Health. It is funded by the National Institute for Environmental Health Sciences – NIEHS. The renewal of the university-wide Superfund Basic Research Program is funded by the Environmental Protection Agency (EPA). 

Aside from the department’s active initiatives in interdisciplinary research, it also houses the Center for Statistical Bioinformatics , which receives generous funding from four national agencies: the National Cancer Institute (NCI), NSF, the National Human Genome Research Institute – NHGRI, and NIEHS. The department is also affiliated with the Institute for Applied Mathematics and Computational Science .

FREQUENTLY ASKED QUESTIONS

Why pursue a ph.d. in statistics.

Recent BLS data projects the median annual salary of statisticians and mathematicians at $99,960, which is just for those who have attained a master’s degree.

With the academic and research pedigree of a doctorate, this figure will skyrocket, especially since the demand for statisticians across industries grew by 30% in 2022, and this projection is expected to go higher as economies and industries reopen.

Most universities, especially those listed below, do not offer terminal master’s degrees in Statistics but rather a continuing degree leading to a doctorate. The master’s degree is conferred once the student becomes a doctoral candidate. 

The short answers to this question? One, because an advanced degree in Statistics can command a high salary, and two, most graduate programs in Statistics lead to a Ph.D. 

What is the difference between Statistics and Mathematics?

Statistics were used to be recognized as a subset of Mathematics. The former, however, has grown into its own discipline as it has become an integral part of various industries that rely heavily on scientific investigations.

The need for real-world integration of the quantitative results also helped statistics grow into its discipline, compared to mathematics’s abstract and theoretical approach. Its heavy utilization of electronic and computational methods also demanded a different methodology distinct from its parent discipline.

It is still safe to say that mathematics, to some extent, subsumes the discipline of statistics, particularly theoretical statistics. However, applied and computational statistics do warrant the argument for statistics being its discipline because of its varied applications, methodologies, and requirements. 

Who can apply to the program? 

Both bachelor’s and master’s degree holders can apply to a Ph.D. program in Statistics, provided they have a working knowledge of advanced mathematics courses, which is usually the trifecta of Advanced  Calculus, Linear Algebra, and Real Analysis.

Knowledge in real variable functions, elementary statistics and probability, experimental design (if the applicant is coming from an applied science program), and programming, particularly Python and R, are not usually required but are advantageous.

Are GRE scores required? What are the other admission requirements?

Most of the programs listed below do not require GRE scores for the 2022 applications. Other admission requirements include transcripts, CV, letters of recommendation, an essay, and a TOEFL exam.

What are the usual degree requirements? 

Most of  the programs listed below follow the following flow: 

• Required coursework completion
• First preliminary examination to test  for coursework competency
• Second preliminary or  qualifying examination for doctoral candidacy admission, which includes the dissertation proposal
• Final examination, which includes the oral dissertation defense
• Acceptance of the dissertation by the committee, which signifies program completion or graduation.

Some programs may only require one qualifying exam, which is for doctoral candidacy. The final examination for the dissertation defense is a staple among all Statistics programs. Other requirements for graduation include participation in colloquia, seminars, consultancy, short research projects, and internships. There is also a residency requirement.

Do I need to be an expert in Mathematics to excel as a Statistician?

No, you do not need to be an expert in mathematics to excel as a statistician. Having a general knowledge of mathematics is helpful; however, much of the work that statisticians do does not require advanced knowledge of complex mathematics. A lot of the work relies on statistical techniques and software programming.

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Ph.D. in Statistics

Our doctoral program in statistics gives future researchers preparation to teach and lead in academic and industry careers.

Program Description

Degree type.

approximately 5 years

The relatively new Ph.D. in Statistics strives to be an exemplar of graduate training in statistics. Students are exposed to cutting edge statistical methodology through the modern curriculum and have the opportunity to work with multiple faculty members to take a deeper dive into special topics, gain experience in working in interdisciplinary teams and learn research skills through flexible research electives. Graduates of our program are prepared to be leaders in statistics and machine learning in both academia and industry.

The Ph.D. in Statistics is expected to take approximately five years to complete, and students participate as full-time graduate students.  Some students are able to finish the program in four years, but all admitted students are guaranteed five years of financial support.  

Within our program, students learn from global leaders in statistics and data sciences and have:

20 credits of required courses in statistical theory and methods, computation, and applications

18 credits of research electives working with two or more faculty members, elective coursework (optional), and a guided reading course

Dissertation research

Coursework Timeline

Year 1: focus on core learning.

The first year consists of the core courses:

• SDS 384.2 Mathematical Statistics I
• SDS 383C Statistical Modeling I
• SDS 387 Linear Models
• SDS 384.11 Theoretical Statistics
• SDS 383D Statistical Modeling II
• SDS 386D Monte Carlo Methods

In addition to the core courses, students of the first year are expected to participate in SDS 190 Readings in Statistics. This class focuses on learning how to read scientific papers and how to grasp the main ideas, as well as on practicing presentations and getting familiar with important statistics literature.

At the end of the first year, students are expected to take a written preliminary exam. The examination has two purposes: to assess the student’s strengths and weaknesses and to determine whether the student should continue in the Ph.D. program. The exam covers the core material covered in the core courses and it consists of two parts: a 3-hour closed book in-class portion and a take-home applied statistics component. The in-class portion is scheduled at the end of the Spring Semester after final exams (usually late May). The take-home problem is distributed at the end of the in-class exam, with a due-time 24 hours later. 

Year 2: Transitioning from Student to Researcher

In the second year of the program, students take the following courses totaling 9 credit hours each semester:

• Required: SDS 190 Readings in Statistics (1 credit hour)
• Required: SDS 389/489 Research Elective* (3 or 4 credit hours) in which the student engages in independent research under the guidance of a member of the Statistics Graduate Studies Committee
• One or more elective courses selected from approved electives ; and/or
• One or more sections of SDS 289/389/489 Research Elective* (2 to 4 credit hours) in which the student engages in independent research with a member(s) of the Statistics Graduate Studies Committee OR guided readings/self-study in an area of statistics or machine learning. 
• Internship course (0 or 1 credit hour; for international students to obtain Curricular Practical Training; contact Graduate Coordinator for appropriate course options)
• GRS 097 Teaching Assistant Fundamentals or NSC 088L Introduction to Evidence-Based Teaching (0 credit hours; for TA and AI preparation)

* Research electives allow students to explore different advising possibilities by working for a semester with a particular professor. These projects can also serve as the beginning of a dissertation research path. No more than six credit hours of research electives can be taken with a single faculty member in a semester.

Year 3: Advance to Candidacy

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. At the end of the second year or during their third year, students are expected to present their plan of study for the dissertation in an Oral candidacy exam. During this exam, students should demonstrate their research proficiency to their Ph.D. committee members. Students who successfully complete the candidacy exam can apply for admission to candidacy for the Ph.D. once they have completed their required coursework and satisfied departmental requirements. The steps to advance to candidacy are:

• Discuss potential candidacy exam topics with advisor
• Propose Ph.D. committee: the proposed committee must follow the Graduate School and departmental regulations on committee membership for what will become the Ph.D. Dissertation Committee
•   Application for candidacy

Year 4+: Dissertation Completion and Defense

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. Moreover, they are expected to present part of their work in the framework of the department's Ph.D. poster session.

Students who are admitted to candidacy will be expected to complete and defend their Ph.D. thesis before their Ph.D. committee to be awarded the degree. The final examination, which is oral, is administered only after all coursework, research and dissertation requirements have been fulfilled. It is expected that students will be prepared to defend by the end of their fifth year in the doctoral program.

General Information and Expectations for All Ph.D. students

• 2023-24 Student Handbook
• Annual Review At the end of every spring semester, students in their second year and beyond are expected to fill out an annual review form distributed by the Graduate Program Administrator. 
• Seminar Series All students are expected to attend the SDS Seminar Series
• SDS 189R Course Description (when taken for internship)
• Internship Course Registration form
• Intel Corporation
• Berry Consultants

Attending Conferences 

Students are encouraged to attend conferences to share their work. All research-related travel while in student status require prior authorization.

• Request for Travel Authorization (both domestic and international travel)
• Request for Authorization for International Travel  

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Ph.d. program.

Statistical Science at Duke is the world's leading graduate research and educational environment for Bayesian statistics, emphasizing the major themes of 21st century statistical science: foundational concepts of statistics, theory and methods of complex stochastic modeling, interdisciplinary applications of statistics, computational statistics, big data analytics, and machine learning. Life as a Ph.D. student in Statistical Science at Duke involves immersion in a broad range of research experiences and emphasizes conceptual innovation, as well as building a deep and broad foundation in theory and methods.

Coupled with our core emphases in modeling, computation and the methodologies of modern statistical science is a broad range of interdisciplinary relationships with many other disciplines (biomedical sciences, environmental sciences, genomics, computer science, engineering, finance, neuroscience, social sciences, and others). The rich opportunities for students in interdisciplinary statistical research at Duke are complemented by opportunities for engagement in research in summer projects with nonprofit agencies, industry, and academia.

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Statistics PhD

The Department of Statistics offers the Master of Arts (MA) and Doctor of Philosophy (PhD) degrees.

Master of Arts (MA)

The Statistics MA program prepares students for careers that require statistical skills. It focuses on tackling statistical challenges encountered by industry rather than preparing for a PhD. The program is for full-time students and is designed to be completed in two semesters (fall and spring).

There is no way to transfer into the PhD program from the MA program. Students must apply to the PhD program.

Doctor of Philosophy (PhD)

The Statistics PhD program is rigorous, yet welcoming to students with interdisciplinary interests and different levels of preparation. The standard PhD program in statistics provides a broad background in probability theory and applied and theoretical statistics.

There are three designated emphasis (DE) tracks available to students in the PhD program who wish to pursue interdisciplinary work formally: Computational and Data Science and Engineering , Computational and Genomic Biology and Computational Precision Health .

Contact Info

[email protected]

367 Evans Hall

Berkeley, CA 94720-3860

At a Glance

Department(s)

Admit Term(s)

Application Deadline

December 3, 2024

Degree Type(s)

Doctoral / PhD

Degree Awarded

GRE Requirements

Department of Technology, Operations, and Statistics | Doctoral Program in Statistics

Doctoral program in statistics.

• Program of Study

Program Requirements

• Doctoral Students and Their Research
• Statistics Faculty

Overview of the Doctoral Program in Statistics

The world’s financial markets produce an enormous stream of data, and the understanding of the techniques needed to analyze and extract information from this stream has become critical.   Doctoral work in statistics combines theory and methodology to deal with the large quantity of statistical data.  Here at Stern we use the theoretical and methodological orientation of a traditional statistics with a focus on the applications that are central to the concerns of a business school.  The PhD thesis work at Stern is a mathematically sophisticated enterprise that never loses sight of the real and practical problems of business.

Stern’s curriculum in statistics prepares students for academic positions by preparing them to conduct independent research.  The statistician must be knowledgeable of the basic issues of the intellectual areas in which his or her work will be applied. 

The most popular areas of student interest in the last few years have been mathematical finance, statistical modeling, data mining, stochastic processes, and econometrics.

Students have rigorous course work and participate in special topics seminars.  They work closely with the faculty and also present special PhD student seminars.

Clifford Hurvich Coordinator, Statistics Doctoral Program

Mission Our mission is the education of scholars who will produce first-rate statistics research and who will succeed as faculty members at first-rate universities.

Admissions and performance We enroll one or two students each year;  these are chosen from approximately 100 highly qualified applicants.

Advising and evaluation Each student will meet with a committee of faculty members yearly to assess progress through the program.

Research and interaction with faculty The Stern statistics faculty have a wide range of interests, but there is special emphasis on time series, statistical modeling, stochastic processes, and financial modeling.

PhD students in statistics take courses in statistical inference, stochastic processes, time series, regression analysis, and multivariate analysis.

In addition to course work, doctoral students also participate in research projects in conjunction with faculty members.  The students attend seminars, present seminars on their own work, and submit their work for publication.

The program culminates with the creation of the PhD thesis, through the stages of proposal, writing, and defense.

Most students finish in four to five years.

Statistics Program of Study

Statistics PhD students take their course work in the first two years of study.  These courses are taken within the Statistics Group (both as formal courses and also as independent study), within other departments at the Stern School, at NYU's Courant Institute, and at Columbia University.

In addition to their statistics courses, doctoral students in Statistics often take courses in mathematics, finance, market research, and econometrics.  The individual curriculum will be planned with the help of faculty advisers.

Questions about the PhD Program in Statistics?

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Graduate Student Handbook (Coming Soon: New Graduate Student Handbook)

Phd program overview.

The PhD program prepares students for research careers in probability and statistics in academia and industry. Students admitted to the PhD program earn the MA and MPhil along the way. The first year of the program is spent on foundational courses in theoretical statistics, applied statistics, and probability. In the following years, students take advanced topics courses. Research toward the dissertation typically begins in the second year. Students also have opportunities to take part in a wide variety of projects involving applied probability or applications of statistics.

Students are expected to register continuously until they distribute and successfully defend their dissertation. Our core required and elective curricula in Statistics, Probability, and Machine Learning aim to provide our doctoral students with advanced learning that is both broad and focused. We expect our students to make Satisfactory Academic Progress in their advanced learning and research training by meeting the following program milestones through courseworks, independent research, and dissertation research:

By the end of year 1: passing the qualifying exams;

By the end of year 2: fulfilling all course requirements for the MA degree and finding a dissertation advisor;

By the end of year 3: passing the oral exam (dissertation prospectus) and fulfilling all requirements for the MPhil degree

By the end of year 5: distributing and defending the dissertation.

We believe in the Professional Development value of active participation in intellectual exchange and pedagogical practices for future statistical faculty and researchers. Students are required to serve as teaching assistants and present research during their training. In addition, each student is expected to attend seminars regularly and participate in Statistical Practicum activities before graduation.

We provide in the following sections a comprehensive collection of the PhD program requirements and milestones. Also included are policies that outline how these requirements will be enforced with ample flexibility. Questions on these requirements should be directed to ADAA Cindy Meekins at [email protected] and the DGS, Professor John Cunningham at [email protected] .

Applications for Admission

• Our students receive very solid training in all aspects of modern statistics. See Graduate Student Handbook for more information.
• Our students receive Fellowship and full financial support for the entire duration of their PhD. See more details here .
• Our students receive job offers from top academic and non-academic institutions .
• Our students can work with world-class faculty members from Statistics Department or the Data Science Institute .
• Our students have access to high-speed computer clusters for their ambitious, computationally demanding research.
• Our students benefit from a wide range of seminars, workshops, and Boot Camps organized by our department and the data science institute .
• Suggested Prerequisites: A student admitted to the PhD program normally has a background in linear algebra and real analysis, and has taken a few courses in statistics, probability, and programming. Students who are quantitatively trained or have substantial background/experience in other scientific disciplines are also encouraged to apply for admission.
• GRE requirement: Waived for Fall 2024.
• Language requirement: The English Proficiency Test requirement (TOEFL) is a Provost's requirement that cannot be waived.
• The Columbia GSAS minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS. To see if this requirement can be waived for you, please check the frequently asked questions below.
• Deadline: Jan 8, 2024 .
• Application process: Please apply by completing the Application for Admission to the Columbia University Graduate School of Arts & Sciences .
• Timeline: P.hD students begin the program in September only.  Admissions decisions are made in mid-March of each year for the Fall semester.

Frequently Asked Questions

• What is the application deadline? What is the deadline for financial aid? Our application deadline is January 5, 2024 .
• Can I meet with you in person or talk to you on the phone? Unfortunately given the high number of applications we receive, we are unable to meet or speak with our applicants.
• What are the required application materials? Specific admission requirements for our programs can be found here .
• Due to financial hardship, I cannot pay the application fee, can I still apply to your program? Yes. Many of our prospective students are eligible for fee waivers. The Graduate School of Arts and Sciences offers a variety of application fee waivers . If you have further questions regarding the waiver please contact  gsas-admissions@ columbia.edu .
• How many students do you admit each year? It varies year to year. We finalize our numbers between December - early February.
• What is the distribution of students currently enrolled in your program? (their background, GPA, standard tests, etc)? Unfortunately, we are unable to share this information.
• How many accepted students receive financial aid? All students in the PhD program receive, for up to five years, a funding package consisting of tuition, fees, and a stipend. These fellowships are awarded in recognition of academic achievement and in expectation of scholarly success; they are contingent upon the student remaining in good academic standing. Summer support, while not guaranteed, is generally provided. Teaching and research experience are considered important aspects of the training of graduate students. Thus, graduate fellowships include some teaching and research apprenticeship. PhD students are given funds to purchase a laptop PC, and additional computing resources are supplied for research projects as necessary. The Department also subsidizes travel expenses for up to two scientific meetings and/or conferences per year for those students selected to present. Additional matching funds from the Graduate School Arts and Sciences are available to students who have passed the oral qualifying exam.
• Can I contact the department with specific scores and get feedback on my competitiveness for the program? We receive more than 450 applications a year and there are many students in our applicant pool who are qualified for our program. However, we can only admit a few top students. Before seeing the entire applicant pool, we cannot comment on admission probabilities.
• What is the minimum GPA for admissions? While we don’t have a GPA threshold, we will carefully review applicants’ transcripts and grades obtained in individual courses.
• Is there a minimum GRE requirement? No. The general GRE exam is waived for the Fall 2024 admissions cycle. 
• Can I upload a copy of my GRE score to the application? Yes, but make sure you arrange for ETS to send the official score to the Graduate School of Arts and Sciences.
• Is the GRE math subject exam required? No, we do not require the GRE math subject exam.
• What is the minimum TOEFL or IELTS  requirement? The Columbia Graduate School of Arts and Sciences minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS
•  I took the TOEFL and IELTS more than two years ago; is my score valid? Scores more than two years old are not accepted. Applicants are strongly urged to make arrangements to take these examinations early in the fall and before completing their application.
• I am an international student and earned a master’s degree from a US university. Can I obtain a TOEFL or IELTS waiver? You may only request a waiver of the English proficiency requirement from the Graduate School of Arts and Sciences by submitting the English Proficiency Waiver Request form and if you meet any of the criteria described here . If you have further questions regarding the waiver please contact  gsas-admissions@ columbia.edu .
• My transcript is not in English. What should I do? You have to submit a notarized translated copy along with the original transcript.

Can I apply to more than one PhD program? You may not submit more than one PhD application to the Graduate School of Arts and Sciences. However, you may elect to have your application reviewed by a second program or department within the Graduate School of Arts and Sciences if you are not offered admission by your first-choice program. Please see the application instructions for a more detailed explanation of this policy and the various restrictions that apply to a second choice. You may apply concurrently to a program housed at the Graduate School of Arts and Sciences and to programs housed at other divisions of the University. However, since the Graduate School of Arts and Sciences does not share application materials with other divisions, you must complete the application requirements for each school.

How do I apply to a dual- or joint-degree program? The Graduate School of Arts and Sciences refers to these programs as dual-degree programs. Applicants must complete the application requirements for both schools. Application materials are not shared between schools. Students can only apply to an established dual-degree program and may not create their own.

With the sole exception of approved dual-degree programs , students may not pursue a degree in more than one Columbia program concurrently, and may not be registered in more than one degree program at any institution in the same semester. Enrollment in another degree program at Columbia or elsewhere while enrolled in a Graduate School of Arts and Sciences master's or doctoral program is strictly prohibited by the Graduate School. Violation of this policy will lead to the rescission of an offer of admission, or termination for a current student.

When will I receive a decision on my application? Notification of decisions for all PhD applicants generally takes place by the end of March.

Notification of MA decisions varies by department and application deadlines. Some MA decisions are sent out in early spring; others may be released as late as mid-August.

Can I apply to both MA Statistics and PhD statistics simultaneously?  For any given entry term, applicants may elect to apply to up to two programs—either one PhD program and one MA program, or two MA programs—by submitting a single (combined) application to the Graduate School of Arts and Sciences.  Applicants who attempt to submit more than one Graduate School of Arts and Sciences application for the same entry term will be required to withdraw one of the applications.

The Graduate School of Arts and Sciences permits applicants to be reviewed by a second program if they do not receive an offer of admission from their first-choice program, with the following restrictions:

• This option is only available for fall-term applicants.
• Applicants will be able to view and opt for a second choice (if applicable) after selecting their first choice. Applicants should not submit a second application. (Note: Selecting a second choice will not affect the consideration of your application by your first choice.)
• Applicants must upload a separate Statement of Purpose and submit any additional supporting materials required by the second program. Transcripts, letters, and test scores should only be submitted once.
• An application will be forwarded to the second-choice program only after the first-choice program has completed its review and rendered its decision. An application file will not be reviewed concurrently by both programs.
• Programs may stop considering second-choice applications at any time during the season; Graduate School of Arts and Sciences cannot guarantee that your application will receive a second review.
• What is the mailing address for your PhD admission office? Students are encouraged to apply online . Please note: Materials should not be mailed to the Graduate School of Arts and Sciences unless specifically requested by the Office of Admissions. Unofficial transcripts and other supplemental application materials should be uploaded through the online application system. Graduate School of Arts and Sciences Office of Admissions Columbia University  107 Low Library, MC 4303 535 West 116th Street  New York, NY 10027
• How many years does it take to pursue a PhD degree in your program? Our students usually graduate in 4‐6 years.
• Can the PhD be pursued part-time? No, all of our students are full-time students. We do not offer a part-time option.
• One of the requirements is to have knowledge of linear algebra (through the level of MATH V2020 at Columbia) and advanced calculus (through the level of MATH V1201). I studied these topics; how do I know if I meet the knowledge content requirement? We interview our top candidates and based on the information on your transcripts and your grades, if we are not sure about what you covered in your courses we will ask you during the interview.
• Can I contact faculty members to learn more about their research and hopefully gain their support? Yes, you are more than welcome to contact faculty members and discuss your research interests with them. However, please note that all the applications are processed by a central admission committee, and individual faculty members cannot and will not guarantee admission to our program.
• How do I find out which professors are taking on new students to mentor this year?  Applications are evaluated through a central admissions committee. Openings in individual faculty groups are not considered during the admissions process. Therefore, we suggest contacting the faculty members you would like to work with and asking if they are planning to take on new students.

For more information please contact us at [email protected] .

For more information please contact us at  [email protected]

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• Mathematics and Statistics

Top Stat PhD programs 2021

By Stat Phd March 6, 2021 in Mathematics and Statistics

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Hi all I’m applying this year to a PhD in statistics, and wanted an up to date list of the best PhD programs to apply for. 

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DanielWarlock

It depends on your research interest. I don't know what you want in terms of a "top list". There are numerous rankings out there, but they are overall ranking of the entire statistics department instead of a particular subfield, based on ranking criteria you might or might not think is relevant. 

That said, the newest ranking released 2021 is QS World University rankings by subject from 4 days ago. Under the "Statistics & Operational Research" subject, top 10 is

3. Stanford

6. Cambridge

7. Berkeley (UCB)

9. Georgia Tech

10. Imperial College London

Link:  https://www.topuniversities.com/university-rankings/university-subject-rankings/2021/statistics-operational-research

US news is really as good of a place as any. Then learn about the peculiarities of the programs to find the ones that fit your goals best.  There are no other worthwhile rankings, and if they were, they would probably just be someone on this forum doing a slight re-ranking of US News.

• MLE and Stat Phd

7 hours ago, DanielWarlock said: It depends on your research interest. I don't know what you want in terms of a "top list". There are numerous rankings out there, but they are overall ranking of the entire statistics department instead of a particular subfield, based on ranking criteria you might or might not think is relevant.  That said, the newest ranking released 2021 is QS World University rankings by subject from 4 days ago. Under the "Statistics & Operational Research" subject, top 10 is 1. MIT 2. Harvard 3. Stanford 4. ETH 5. Oxford 6. Cambridge 7. Berkeley (UCB) 8. NUS 9. Georgia Tech 10. Imperial College London Link:  https://www.topuniversities.com/university-rankings/university-subject-rankings/2021/statistics-operational-research      

Thanks. Indeed, Removing the non-US schools, the top 20 US are similar to the US news top 20.

US news for reference:

• University of Chicago
• University of Washington
• Carnegie Mellon

I think will create an overall ranking aggregating all rankings. 

12 hours ago, DanielWarlock said: 1. MIT 2. Harvard 3. Stanford 4. ETH 5. Oxford 6. Cambridge 7. Berkeley (UCB) 8. NUS 9. Georgia Tech 10. Imperial College London

I'm not sure how useful this is for stats specifically, as several of these programmes lack independent statistics departments (MIT has Mathematics, or Operations Research, ETH has Mathematics, Cambridge has the department of Pure Mathematics and Mathematical Statistics) so  a lot of these universities wouldn't suit someone who wanted to do statistics (esp applied statistics)

Here's my very affective (read unscientific, subjective) rankings from  stalking alumni placements and professor productivity. It's pretty much a reranking of US News with the added information of the tiers indicating where the big jumps in quality are. I think within tiers the choice doesn't matter too much.

Stanford Tier:

Stanford. Elite Top:

Berkley, Harvard, CMU, (Likely UChicago but I didn't research them)

Uwashington, Duke, Michigan, Columbia, Cornell, UNC

NCSU , TAMU, UT Austin, UCLA, Wisconsin

2 hours ago, trynagetby said: Here's my very affective (read unscientific, subjective) rankings from  stalking alumni placements and professor productivity. It's pretty much a reranking of US News with the added information of the tiers indicating where the big jumps in quality are. I think within tiers the choice doesn't matter too much. Stanford Tier: Stanford. Elite Top: Berkley, Harvard, CMU, (Likely UChicago but I didn't research them) Top: Uwashington, Duke, Michigan, Columbia, Cornell, UNC Up there: NCSU , TAMU, UT Austin, UCLA, Wisconsin

Probably Stanford on elite top too?

Just now, Stat Phd said: Probably Stanford on elite top too?

Pretty sure he means stanford is a tier above everyone else for stats.

• trynagetby and liyu

Not so sure why people think stanford is better than berkeley.  It is a smaller department than berkeley.  It is mostly theoretical.  Berkeley has much closer ties to EECS and does a lot more applied and methodological research Not getting into the politics of this however A lot of people have complained about the lack of diversity at stanford.  I have heard many female applicants accepted at Stanford have rejected it because of its diversity reputation.   I am not sure if they have ever had a black or Hispanic phd student at stanford.   

• speowi and insert_name_here

12 minutes ago, statsnow said: Not so sure why people think stanford is better than berkeley.  It is a smaller department than berkeley.  It is mostly theoretical.  Berkeley has much closer ties to EECS and does a lot more applied and methodological research Not getting into the politics of this however A lot of people have complained about the lack of diversity at stanford.  I have heard many female applicants accepted at Stanford have rejected it because of its diversity reputation.   I am not sure if they have ever had a black or Hispanic phd student at stanford.   

When people discuss rankings, the only relevant thing is how the program will affect your academic job prospects after.  You can do applied research at a lot of places and get great industry jobs, and you can be just as successful coming from Berkeley, but I don't see how one could argue that Berkeley is better.  Does Berkeley have even 3 statisticians that come close to Tibshirani, Efron, Diaconis, Hastie, Candes, Donoho in terms of influence?  This isn't a dig at Berkeley, so I'm genuinely curious as to why you think this and would be happy to change my mind if you presented some evidence.

1 hour ago, statsnow said: I am not sure if they have ever had a black or Hispanic phd student at stanford.   

I think two of their current first years are Hispanic. Not that it makes the department diverse, but it appears to be a change from previous years.

icantdoalgebra

2 hours ago, bayessays said: When people discuss rankings, the only relevant thing is how the program will affect your academic job prospects after.  You can do applied research at a lot of places and get great industry jobs, and you can be just as successful coming from Berkeley, but I don't see how one could argue that Berkeley is better.  Does Berkeley have even 3 statisticians that come close to Tibshirani, Efron, Diaconis, Hastie, Candes, Donoho in terms of influence?  This isn't a dig at Berkeley, so I'm genuinely curious as to why you think this and would be happy to change my mind if you presented some evidence.

Easy; Peter Bickel, Michael Jordan, Martin Wainwright. People like van der Laan, Bartlett, Brillinger, Aldous, Yu, and Pitman might not be as famous as those Stanford faculty that you listed, but its not like they are some random professors. How about Fernando Perez and his work on creating Jupyter Notebooks? Perhaps its not as much of a research accomplishment, but creating a widely-used software that makes performing statistics and data science easier is definitely worth quite a bit of influence.

I'm not trying to argue that Berkeley is better, in fact I agree that Stanford's program is better. However is it substantially better? I am skeptical. 

There is a valid point though, in that Berkeley's stats department has leaned more heavily towards machine learning recently and if you are a statistics purist you could reasonably make the argument that Stanford is substantially better if that is your criteria for evaluation.

• gouda91 , statsnow and bayessays

@icantdoalgebra  I don't disagree with anything you said.

I think rather than discussing program rankings we should make an advisors ranking, lol, but I guess that's too delicate of a topic even for an anonymous forum. 

On 3/7/2021 at 10:13 PM, Stat Phd said: US news for reference:

Is this the most recent 2020 rankings from US News? I can't seem to find their stats rankings after 2018.

(They only do grad program rankings every several years (4, maybe?), so 2018 should be the most recent)

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Statistics at UC Berkeley

We are a community engaged in research and education in probability and statistics. In addition to developing fundamental theory and methodology, we are actively involved in statistical problems that arise in such diverse fields as molecular biology, geophysics, astronomy, AIDS research, neurophysiology, sociology, political science, education, demography, and the U.S. Census.

Top ranking programs

We are consistently ranked one of the top two Statistics graduate programs in the United States and globally. Our undergraduate and graduate programs are renowned for preparing students for a constantly evolving data-based and data-driven world. We teach the concepts and real skills to successfully and ethically work in fields as diverse as medicine, finance, technology and government.

Our award winning faculty

Our faculty are at the leading edge of statistical research, with 2 National Medal of Science winners, 12 members of the National Academy of Science, multiple members of the American Academy of Arts and Sciences, Guggenheim Fellows, MacArthur Fellowship ("Genius Grant") winners, and Sloan Fellows, to name just some of the awards held by our esteemed faculty.

Teaching innovations

Our instruction is at the cutting edge of statistical science. Our pedagogy includes concepts across data science, probability, applied and theoretical statistics, mathematics and machine learning. We are innovative in adapting new interdisciplinary courses and methods to meet a changing world around us.

Prospective Students

Current students, department members, outside community, news & events, new faculty: joshua grossman, acting assistant teaching professor.

New Faculty: Amanda Coston, Assistant Professor

Former Visiting Professor Benjamini wins Rousseeuw Prize

Mackey, M.A. '11 joins Society for Science National Leadership Council

Research in the department is wide ranging, both in terms of areas of applications and in terms of focus. To see what we are currently working on:

Giving to the Statistics Department

You can help ensure the department continues to be a leading Statistics program in the nation, meeting our mission of educating leaders, creating knowledge, and serving society.

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PhD Program

Advanced undergraduate or masters level work in mathematics and statistics will provide a good background for the doctoral program. Quantitatively oriented students with degrees in other scientific fields are also encouraged to apply for admission. In particular, the department has expanded its research and educational activities towards computational biology, mathematical finance and information science. The doctoral program normally takes four to five years to complete.

Doctoral Program in Statistics

Statistics phd minor.

• Graduate Studies

Ph.D. Program

The PhD program prepares students for research careers in theory and application of probability and statistics in academic and non-academic (e.g., industry, government) settings.  Students might elect to pursue either the general Statistics track of the program (the default), or one of the four specialized tracks that take advantage of UW’s interdisciplinary environment: Statistical Genetics (StatGen), Statistics in the Social Sciences (CSSS), Machine Learning and Big Data (MLBD), and Advanced Data Science (ADS). 

Admission Requirements

For application requirements and procedures, please see the graduate programs applications page .

Recommended Preparation

The Department of Statistics at the University of Washington is committed to providing a world-class education in statistics. As such, having some mathematical background is necessary to complete our core courses. This background includes linear algebra at the level of UW’s MATH 318 or 340, advanced calculus at the level of MATH 327 and 328, and introductory probability at the level of MATH 394 and 395. Real analysis at the level of UW’s MATH 424, 425, and 426 is also helpful, though not required. Descriptions of these courses can be found in the UW Course Catalog . We also recognize that some exceptional candidates will lack the needed mathematical background but succeed in our program. Admission for such applicants will involve a collaborative curriculum design process with the Graduate Program Coordinator to allow them to make up the necessary courses. 

While not a requirement, prior background in computing and data analysis is advantageous for admission to our program. In particular, programming experience at the level of UW’s CSE 142 is expected.  Additionally, our coursework assumes familiarity with a high-level programming language such as R or Python. 

Graduation Requirements 

This is a summary of the department-specific graduation requirements. For additional details on the department-specific requirements, please consult the  Ph.D. Student Handbook .  For previous versions of the Handbook, please contact the Graduate Student Advisor .  In addition, please see also the University-wide requirements at  Instructions, Policies & Procedures for Graduate Students  and  UW Doctoral Degrees .  

General Statistics Track

• Core courses: Advanced statistical theory (STAT 581, STAT 582 and STAT 583), statistical methodology (STAT 570 and STAT 571), statistical computing (STAT 534), and measure theory (either STAT 559 or MATH 574-575-576).  
• Elective courses: A minimum of four approved 500-level classes that form a coherent set, as approved in writing by the Graduate Program Coordinator.  A list of elective courses that have already been pre-approved or pre-denied can be found here .
• M.S. Theory Exam: The syllabus of the exam is available here .
• Research Prelim Exam. Requires enrollment in STAT 572. 
• Consulting.  Requires enrollment in STAT 599. 
• Applied Data Analysis Project.  Requires enrollment in 3 credits of STAT 597. 
• Statistics seminar participation: Students must attend the Statistics Department seminar and enroll in STAT 590 for at least 8 quarters. 
• Teaching requirement: All Ph.D. students must satisfactorily serve as a Teaching Assistant for at least one quarter. 
• General Exam. 
• Dissertation Credits.  A minimum of 27 credits of STAT 800, spread over at least three quarters. 
• Passage of the Dissertation Defense. 

Statistical Genetics (StatGen) Track

Students pursuing the Statistical Genetics (StatGen) Ph.D. track are required to take BIOST/STAT 550 and BIOST/STAT 551, GENOME 562 and GENOME 540 or GENOME 541. These courses may be counted as the four required Ph.D.-level electives. Additionally, students are expected to participate in the Statistical Genetics Seminar (BIOST581) in addition to participating in the statistics seminar (STAT 590). Finally, students in the Statistics Statistical Genetics Ph.D. pathway may take STAT 516-517 instead of STAT 570-571 for their Statistical Methodology core requirement. This is a transcriptable program option, i.e., the fact that the student completed the requirements will be noted in their transcript.

Statistics in the Social Sciences (CSSS) Track

Students in the Statistics in the Social Sciences (CSSS) Ph.D. track  are required to take four numerically graded 500-level courses, including at least two CSSS courses or STAT courses cross-listed with CSSS, and at most two discipline-specific social science courses that together form a coherent program of study. Additionally, students must complete at least three quarters of participation (one credit per quarter) in the CS&SS seminar (CSSS 590). This is not a transcriptable option, i.e., the fact that the student completed the requirements will not be noted in their transcript.

Machine Learning and Big Data Track

Students in the Machine Learning and Big Data (MLBD) Ph.D. track are required to take the following courses: one foundational machine learning course (STAT 535), one advanced machine learning course (either STAT 538 or STAT 548 / CSE 547), one breadth course (either on databases, CSE 544, or data visualization, CSE 512), and one additional elective course (STAT 538, STAT 548, CSE 515, CSE 512, CSE 544 or EE 578). At most two of these four courses may be counted as part of the four required PhD-level electives. Students pursuing this track are not required to take STAT 583 and can use STAT 571 to satisfy the Applied Data Analysis Project requirement. This is not a transcriptable option, i.e., the fact that the student completed the requirements will not be noted in their transcript. 

Advanced Data Science (ADS) Track

Students in the Advanced Data Science (ADS) Ph.D. track are required to take the same coursework as students in the Machine Learning and Big Data track. They are also not required to take STAT 583 and can use STAT 571 to satisfy the Applied Data Analysis Project requirement. The only difference in terms of requirements between the MLBD and the ADS tracks is that students in the ADS track must also register for at least 4 quarters of the weekly eScience Community Seminar (CHEM E 599). Also, unlike the MLBD track, the ADS is a transcriptable program option, i.e., the fact that the student completed the requirements will be noted in their transcript. 

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.

Statistics, PHD

On this page:, at a glance: program details.

• Location: Tempe campus
• Second Language Requirement: No

Program Description

Degree Awarded: PHD Statistics

As a science, statistics focuses on data collection and data analysis by using theoretical, applied and computational tools. The PhD program in statistics reflects this breadth in tools and considerations while allowing students sufficient flexibility to tailor their program of study to reflect individual interests and goals. Research can be of a disciplinary or transdisciplinary nature.

Degree Requirements

Curriculum plan options.

• 84 credit hours, a written comprehensive exam, a prospectus and a dissertation

Required Core (3 credit hours) STP 526 Theory of Statistical Linear Models (3)

Other Requirements (15 credit hours) IEE 572 Design Engineering Experiments (3) or STP 531 Applied Analysis of Variance (3) IEE 578 Regression Analysis (3) or STP 530 Applied Regression Analysis (3) STP 501 Theory of Statistics I: Distribution Theory 3 (3) STP 502 Theory of Statistics II: Inference (3) STP 527 Statistical Large Sample Theory (3)

Electives (42 credit hours)

Research (12 credit hours) STP 792 Research (12)

Culminating Experience (12 credit hours) STP 799 Dissertation (12)

Additional Curriculum Information Electives are chosen from statistics or related area courses approved by the student's supervisory committee.

Other requirements courses may be substituted with department approval.

Students must pass:

• one qualifying examination and coursework in analysis
• a written comprehensive examination
• a dissertation prospectus defense

Students should see the department website for examination information.

Each student must write a dissertation and defend it orally in front of five dissertation committee members.

Admission Requirements

Applicants must fulfill the requirements of both the Graduate College and The College of Liberal Arts and Sciences.

Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree in mathematics, statistics or a closely related area from a regionally accredited institution.

Applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program or a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in an applicable master's degree program.

All applicants must submit:

• graduate admission application and application fee
• official transcripts
• statement of education and career goals
• three letters of recommendation
• proof of English proficiency

Additional Application Information An applicant whose native language is not English must provide proof of English proficiency regardless of their current residency.

Completion of the following courses (equivalents at ASU are given in parentheses) is required. Applicants who lack any of these prerequisite courses must complete them before being considered for admission.

• calculus (MAT 270, 271 and 272)
• advanced calculus (MAT 371)
• linear algebra (MAT 342)
• computer programming (CSE 100)
• introductory applied statistics (STP 420)

Next Steps to attend ASU

Learn about our programs, apply to a program, visit our campus, application deadlines, learning outcomes.

• Able to complete original research in statistics.
• Proficient in applying advanced statistical methods in coursework and research.
• Address an original research question in statistics.

Career Opportunities

Statistical analysis and data mining have been identified as two of the most desirable skills in today's job market. Data, and the analysis of data, is big business, and the Department of Labor projects that overall employment of mathematicians and statisticians will grow 33% between 2020 and 2030, much faster than the average for all occupations.

For graduates of the doctoral program in statistics, that means a broad variety of career opportunities in fields as diverse as business, finance, engineering, technology, education, marketing, government and other areas of the economy.

These are just a few of the top career opportunities available for a graduate with a doctoral degree in statistics:

• business consultant or analyst
• data science professor, instructor or researcher
• data scientist
• faculty-track academic
• financial analyst
• market research analyst
• software engineer
• statistician

Program Contact Information

If you have questions related to admission, please click here to request information and an admission specialist will reach out to you directly. For questions regarding faculty or courses, please use the contact information below.

• [email protected]
• 480/965-3951

• Youth Program
• Wharton Online

Statistics and Data Science

Wharton’s phd program in statistics and data science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. these include theoretical research in mathematical statistics as well as interdisciplinary research in the social sciences, biology and computer science..

Wharton’s PhD program in Statistics and Data Science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include:

• analysis of observational studies;
• Bayesian inference, bioinformatics;
• decision theory;
• game theory;
• high dimensional inference;
• information theory;
• machine learning;
• model selection;
• nonparametric function estimation; and
• time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

For information on courses and sample plan of study, please visit the University Graduate Catalog .

Get the Details.

Visit the Statistics and Data Science website for details on program requirements and courses. Read faculty and student research and bios to see what you can do with a Statistics PhD.

Statistics and Data Science Doctoral Coordinator 

Dr. Bhaswar Bhattacharya Associate Professor of Statistics and Data Science Associate Professor of Mathematics (secondary appointment) Email: [email protected] Phone: 215-573-0535

• Youth Program
• Wharton Online

PhD Program

Wharton’s PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include: analysis of observational studies; Bayesian inference, bioinformatics; decision theory; game theory; high dimensional inference; information theory; machine learning; model selection; nonparametric function estimation; and time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

Apply online here .

Department of Statistics and Data Science

The Wharton School, University of Pennsylvania Academic Research Building 265 South 37th Street, 3rd & 4th Floors Philadelphia, PA 19104-1686

Phone: (215) 898-8222

• Contact Information
• Course Descriptions
• Course Schedule
• Doctoral Inside: Resources for Current PhD Students
• Penn Career Services
• Apply to Wharton
• Financial Aid