phd in data science and statistics

DEPARTMENT OF STATISTICS AND DATA SCIENCE

Phd program, phd program overview.

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

Requirements

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

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

In order to receive a doctoral degree, students must:

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

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

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

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

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

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

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

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

Optional MS degree en route to PhD

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

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

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

DiscoverDataScience.org

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

Created by aasif.faizal

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

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

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

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

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

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

So why pursue a data science PhD?

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

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

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

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

Topics include:

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

Preparing for a Doctorate Program

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

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

Data Science PhD Programs, Historically

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

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

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

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

Can You Get a PhD in Data Science Online?

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

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

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

Overview of PhD Coursework

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

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

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

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

What’s the core curriculum like?

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

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

Dissertation

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

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

Join professional associations

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

Leverage your social network

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

Kaggle competitions

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

Internships

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

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

Get certified

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

Conferences

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

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

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

What most schools will require when applying:

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

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

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

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

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

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

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

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

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

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

Complete List of Data Science PhD Programs

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

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

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

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

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

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

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

View Course Offerings

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

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

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

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

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

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

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

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

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

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

Coding well in C++ is preferred.

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

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

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

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

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

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

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

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

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

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

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

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

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

View Course Offerings – Clemson

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

View Course Offering

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

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

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

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

Applicants must have a master’s already.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Students in this program work in research groups across campus.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

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

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

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

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

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

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

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

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

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

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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

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

Choosing a Field of Study

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

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

Residency Requirements

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

Your Advisor and Special Committee

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

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

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

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

Statistics PhD Travel Support

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

Completion of the PhD Degree

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

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

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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

Statistics & Data Science

Dietrich college of humanities and social sciences, ph.d. programs, our ph.d. programs enable students to pursue a wide range of research opportunities, including constructing and implementing advanced methods of data analysis to address crucial cross-disciplinary questions, along with developing the fundamental theory that supports these methods..

Unique opportunities for our Ph.D. students include:

• We host four cross-disciplinary joint Ph.D. programs for students who want to specialize in machine learning , public policy , neuroscience , and the link between engineering and policy .
• Our faculty have deep involvement in a range of important, data-rich scientific collaborations, including in the areas of genetics, neuroscience, astronomy, and the social sciences. This allows students to have easy access to both the crucial questions in these fields, and to the data that can provide the answers.
• Students begin work on their Advanced Data Analysis Project in the second semester. This year-long, faculty/student collaboration, distinct from the thesis, provides an immediate intensive research experience.
• Carnegie Mellon is home to the first Machine Learning Department . Many of our faculty maintain joint appointments with this Department and they (and our students) have strong connections to this exciting and growing area of research.

The programs leading to the degree of   Doctor of Philosophy in Statistics   seek to strike a balance between theoretical and applied statistics. The Ph.D. program prepares students for university teaching and research careers, and for industrial and governmental positions involving research in new statistical methods. Four to five years are usually needed to complete all requirements for the Ph.D. degree.

These pages present the requirements for each of our Ph.D. programs.

The page   "Core Ph.D. Requirements"   lays out the requirements for all Ph.D. students, while each of the four joint programs are described under the Joint Ph.D. Degrees pages. Our Ph.D. students can also earn a   Master of Science in Statistics   as an intermediate step towards their ultimate goal.

Joint Ph.D. Programs

Statistics/machine learning, statistics/public policy, statistics/engineering and public policy, statistics/neural computation  .

PhD in Data Science

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

Research Excellence:

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

Research Topics:

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

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

Ph.D. Program

Advising The vice chair for graduate studies is the chief graduate adviser and heads a committee of faculty advisers who may serve as academic advisers. The research interests of the members of this committee span most of the major areas of statistics. During their first quarter in the program students are required to meet with an academic adviser who assists them in planning a reasonable course of study. In addition, the academic adviser is responsible for monitoring the student’s degree progress and approving the study list each quarter. Students are encouraged to begin thinking about their research interests as early as possible. After the student identifies a dissertation topic, the chair of the dissertation committee becomes the student’s academic adviser.

Continuing students should meet with either the vice chair for graduate studies or their academic adviser at least once each quarter and a record of this interview is placed in the student’s academic file. Each fall a committee consisting of all regular departmental faculty meet to evaluate the progress of all enrolled doctoral students. This committee decides if students are making satisfactory progress, and if not offers specific recommendations to correct the situation. For students who have begun dissertation work, the determination of satisfactory progress is typically delegated to the academic adviser. Students who are found to be consistently performing unsatisfactorily may be recommended for termination by a vote of this committee. Doctoral students normally are considered to be making satisfactory progress if they take the written qualifying examination in the summer following their first year of study and the University Oral Qualifying Examination by the end of their second year.

Major Fields or Sub-disciplines The strengths of current and prospective faculty dictate the specific fields of emphasis in the department: applied multivariate analysis; bioinformatics ( Center for Statistical Research in Computational Biology ); computational and computer-intensive statistics; computer vision; cognition; artificial intelligence; machine learning ( Center for Vision, Cognition, Learning, and Autonomy ); social statistics ( Center for Social Statistics ); experimental design and environmental statistics.

Foreign Language Requirement None.

Course Requirements Students are required to pass, with a grade of B- or better, 54 units of approved graduate course work (200 series) and to maintain an overall grade-point average of 3.0 or better. At least 40 of these units must be in courses from this department; the remaining units may be from courses in related departments. Students are strongly encouraged to take Statistics 200A-200B-200C, 201A-201B-201C, and 202A-202B-202C. All doctoral students are required to take Statistics 290 for at least six quarters, and strongly encouraged to take Stats 290 during each quarter of enrollment. In addition, all doctoral students can take Statistics 296 and/or 596, or 599 as needed. Please note that up to two units of Statistics 285 and eight units of Statistics 596 can be counted toward the 40 units from our department. Stats 290, 296, and 599 are not counted.

Students with gaps in their previous training are allowed to take, with the approval of their academic adviser, undergraduate courses offered by the department. However, Statistics 100A-100B-100C, 101A-101B-101C and 102A-102B-102C may not be applied toward course requirements for a graduate degree in the department. Students who need a basic refresher course are encouraged to take Statistics 100A-100B-100C.

Teaching Experience Students are required to complete at least one quarter of service as a teaching assistant for a minimum of 25% time appointment. Students who serve as teaching assistants in the department must have taken or be currently enrolled in Statistics 495A-495B-495C. International students for whom English is a second language must pass either the Test of Spoken English (TSE) or the UCLA Test of Oral Proficiency (TOP) in English before they may serve as teaching assistants.

Written and Oral Qualifying Examinations Academic Senate regulations require all doctoral students to complete and pass university written and oral qualifying examinations prior to doctoral advancement to candidacy. Also, under Senate regulations, the University Oral Qualifying Examination is open only to the student and appointed members of the doctoral committee. In addition to university requirements, some graduate programs have other pre-candidacy examination requirements. What follows in this section is how students are required to fulfill all of these requirements for this doctoral program.

All committee nominations and reconstitutions adhere to the Minimum Standards for Doctoral Committee Constitution.

The written qualifying examination consists of a high-quality paper, solely authorized by the student. This paper can be a research paper containing an original contribution, or a focused critical survey paper. The paper should demonstrate that the student understands and can integrate and communicate ideas clearly and concisely. The paper should be approximately 10 pages, single-spaced, and the style should be suitable for submission to a first-rate journal or technical conference. Any contributions that are not the student’s, including those of the student’s adviser, must be explicitly acknowledged in detail.

After passing the written qualifying examination, students select a doctoral committee that administers the University Oral Qualifying Examination, required for advancement to candidacy. Students are encouraged to begin thinking about their research interests as early as possible and to seek out faculty members who might serve on their doctoral committee. Students making satisfactory progress are expected to take the written qualifying examination in the summer following their first year of study and the University Oral Qualifying Examination by the end of their second year.

Advancement to Candidacy Students are advanced to candidacy and awarded the Candidate in Philosophy (C.Phil.) degree upon successful completion of the written and oral qualifying examinations.

Doctoral Dissertation Every doctoral degree program requires the completion of an approved dissertation that demonstrates the student’s ability to perform original, independent research and constitutes a distinct contribution to knowledge in the principal field of study.

Final Oral Examination (Defense of the Dissertation) Required for all students in the program. Please see the Advice on Taking the Oral Exam for more information.

Time-to-Degree Students are expected to advance to candidacy for the Ph.D. degree within six quarters of full-time work. Completion of all degree requirements (including the dissertation) normally takes 15 quarters. The maximum time to degree is 24 quarters.

Termination of Graduate Study and Appeal of Termination

University Policy

A student who fails to meet the above requirements may be recommended for termination of graduate study. A graduate student may be disqualified from continuing in the graduate program for a variety of reasons. The most common is failure to maintain the minimum cumulative grade point average (3.00) required by the Academic Senate to remain in good standing (some programs require a higher grade point average). Other examples include failure of examinations, lack of timely progress toward the degree and poor performance in core courses. Probationary students (those with cumulative grade point averages below 3.00) are subject to immediate dismissal upon the recommendation of their department. University guidelines governing termination of graduate students, including the appeal procedure, are outlined in Standards and Procedures for Graduate Study at UCLA.

Special Departmental or Program Policy for the Ph.D. Program

A student who does not advance to doctoral candidacy within six quarters of full-time study is subject to a recommendation for termination. The graduate vice chair informs a student of such a recommendation and the student is asked to submit a written appeal and to solicit letters of support from members of the faculty. The appeal is considered by the Graduate Studies Committee, which makes the final departmental decision.

For Students Who Entered Before Fall 2022 Please click this link . Then navigate to “Program Requirements” in the tab that opens and select the academic year when you matriculated.

Timeline to Filing Your Dissertation

• By Fall of your 2nd year, choose your Faculty Adviser and discuss with your faculty adviser who will be on your committee.
• Complete and submit the Nomination of Doctoral Committee Form at least one month before you take your orals.
• Contact Student Affairs to schedule a time and date to take your orals. Confirm the time and date with your committee.
• Your Adviser will let you know when you are ready to take your final orals and submit your dissertation online. When that time comes, arrange time, date and location with the student affairs office.
• If you still need more time and after you’ve advanced choose to do a Filing Fee instead please read this website carefully: https://grad.ucla.edu/academics/graduate-study/filing-fee-application/
• You must also complete the Filing Fee application found here: https://grad.ucla.edu/gasaa/etd/filingfee.pdf
• Important dates and workshops are found here: https://grad.ucla.edu/academics/calendar/thesis-dissertation-filing-deadlines-and-workshops/
• Should you choose the Filing Fee for a specific quarter, you must be registered and enrolled the quarter before AND you must submit a complete first draft of your dissertation to all committee members at the time you submit your filing fee application (in order to apply the filing fee, students must be registered and enrolled in at least 2 units the quarter before).

Faculty Research Interest See the faculty directory listing for current members and their interests at http://directory.stat.ucla.edu/ .

PhD in Data Science

First Year Requirements

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

Required courses:

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

Synthesis project

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

Thesis Advisor and Dissertation Committee

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

Proposal Presentation and Admission to Candidacy

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

Dissertation Defense

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

Doctoral Program

Program summary.

Students are required to

• master the material in the prerequisite courses ;
• pass the first-year core program;
• attempt all three parts of the qualifying examinations and show acceptable performance in at least two of them (end of 1st year);
• satisfy the depth and breadth requirements (2nd/3rd/4th year);
• successfully complete the thesis proposal meeting (winter quarter of the 3rd year);
• present a draft of their dissertation and pass the university oral examination (4th/5th year).

The PhD requires a minimum of 135 units. Students are required to take a minimum of nine units of advanced topics courses (for depth) offered by the department (not including literature, research, consulting or Year 1 coursework), and a minimum of nine units outside of the Statistics Department (for breadth). Courses for the depth and breadth requirements must equal a combined minimum of 24 units. In addition, students must enroll in STATS 390 Statistical Consulting, taking it at least twice.

All students who have passed the qualifying exams but have not yet passed the Thesis Proposal Meeting must take STATS 319 at least once each year. For example, a student taking the qualifying exams in the summer after Year 1 and having the dissertation proposal meeting in Year 3, would take 319 in Years 2 and 3. Students in their second year are strongly encouraged to take STATS 399 with at least one faculty member. All details of program requirements can be found in our PhD handbook (available to Stanford affiliates only, using Stanford authentication. Requests for access from non-affiliates will not be approved).

Statistics Department PhD Handbook

All students are expected to abide by the Honor Code and the Fundamental Standard .

Doctoral and Research Advisors

During the first two years of the program, students' academic progress is monitored by the department's Graduate Director. Each student should meet at least once a quarter with the Graduate Director to discuss their academic plans and their progress towards choosing a thesis advisor (before the final study list deadline of spring of the second year). From the third year onward students are advised by their selected advisor.

Qualifying Examinations

Qualifying examinations are part of most PhD programs in the United States. At Stanford these exams are intended to test the student's level of knowledge when the first-year program, common to all students, has been completed. There are separate examinations in the three core subjects of statistical theory and methods, applied statistics, and probability theory, which are typically taken during the summer at the end of the student's first year. Students are expected to attempt all three examinations and show acceptable performance in at least two of them. Letter grades are not given. Qualifying exams may be taken only once. After passing the qualifying exams, students must file for Ph.D. Candidacy, a university milestone, by the end of spring quarter of their second year.

While nearly all students pass the qualifying examinations, those who do not can arrange to have their financial support continued for up to three quarters while alternative plans are made. Usually students are able to complete the requirements for the M.S. degree in Statistics in two years or less, whether or not they have passed the PhD qualifying exams.

Thesis Proposal Meeting and Dissertation Reading Committee 

The thesis proposal meeting is intended to demonstrate a student's depth in some areas of statistics, and to examine the general plan for their research. In the meeting the student gives a 60-minute presentation involving ideas developed to date and plans for completing a PhD dissertation, and for another 60 minutes answers questions posed by the committee. which consists of their advisor and two other members. The meeting must be successfully completed by the end of winter quarter of the third year. If a student does not pass, the exam must be repeated. Repeated failure can lead to a loss of financial support.

The Dissertation Reading Committee consists of the student’s advisor plus two faculty readers, all of whom are responsible for reading the full dissertation. Of these three, at least two must be members of the Statistics Department (faculty with a full or joint appointment in Statistics but excluding for this purpose those with only a courtesy or adjunct appointment). Normally, all committee members are members of the Stanford University Academic Council or are emeritus Academic Council members; the principal dissertation advisor must be an Academic Council member. 

The Doctoral Dissertation Reading Committee form should be completed and signed at the Dissertation Proposal Meeting. The form must be submitted before approval of TGR status or before scheduling a University Oral Examination.

 For further information on the Dissertation Reading Committee, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.8.

University Oral Examinations

The oral examination consists of a public, approximately 60-minute, presentation on the thesis topic, followed by a 60 minute question and answer period attended only by members of the examining committee. The questions relate to the student's presentation and also explore the student's familiarity with broader statistical topics related to the thesis research. The oral examination is normally completed during the last few months of the student's PhD period. The examining committee typically consists of four faculty members from the Statistics Department and a fifth faculty member from outside the department serving as the committee chair. Four out of five passing votes are required and no grades are given. Nearly all students can expect to pass this examination, although it is common for specific recommendations to be made regarding completion of the thesis.

The Dissertation Reading Committee must also read and approve the thesis.

For further information on university oral examinations and committees, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.7 .

Dissertation

The dissertation is the capstone of the PhD degree. It is expected to be an original piece of work of publishable quality. The research advisor and two additional faculty members constitute the student's dissertation reading committee.

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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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Computational Science and Statistics (Ph.D.) - Data Science Specialization

Secondary navigation, use the power of data to help business, government, and society make better decisions..

The computational science and statistics Ph.D. program is designed to train students to integrate computational and statistical methodologies to formulate, model, analyze and solve research problems of interest in a wide variety of applied and theoretical contexts. The data science specialization will emphasize curriculum and research programs that prepare graduates to support local, regional and national industry by analyzing large and complex datasets.

Is it for you?

This program will be a good fit if you:.

• Have an undergraduate or master's degree in mathematics, statistics or related field.
• Have strong problem-solving and quantitative skills.
• Are able to grasp the essence of complex situations.
• Have good communication skills.
• Want your quantitative skills to have an impact.

Career Opportunities

• Data scientist
• Predictive analyst
• Statistician
• Financial/business analyst
• Risk analyst

Data Sciences + Operations PhD Program

Operations Management studies how firms organize their resources and recurring activities in order to be competitive in cost, price, responsiveness and quality. The field stresses quantitative techniques ranging from applied probability to optimization and game theory.

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• ADMISSIONS EVENTS

Operations Management

Operations Management studies how firms organize their resources and recurring activities in order to be competitive in cost, price, responsiveness and quality. The field stresses quantitative techniques ranging from applied probability to optimization and game theory. Successful applicants will have strong technical and mathematical training, and we particularly encourage applicants with undergraduate degrees in mathematics, economics and/or engineering to apply at the USC Application Portal. A prior background in business is not required.

Research areas: Our faculty are interested in a wide variety of research questions; check their personal webpages for specific examples. Some indicative topics include:

• Revenue management and dynamic pricing
• Data-driven analytics
• Healthcare operations
• Market and auction design
• Sustainability
• Supply-chain and service management

Operations Management and operations research prize interdisciplinary thinking and approaches. Hence, high-quality research often leverages a range of methodologies and tools, including:

• Optimization (linear, nonlinear, combinatorial, data-driven)
• Game Theory
• Queuing Theory
• Stochastic Processes / Applied Probability
• Econometrics
• Machine Learning

High quality research can involve developing new theoretical tools in these areas, application driven case studies, or both.

Our statistics faculty are interested in both pushing the boundaries of theoretical knowledge in the field as well as applied areas. There are many interesting research projects at the intersection of statistics, computer science, econometrics, finance, and marketing. Selected topics include:

• Statistical machine learning
• High­dimensional data analysis
• Computational and statistical limits of "Big data" problems
• Structural learning (such as clustering, sparse recovery and network analysis)
• Multiple hypotheses testing, uncertainty assessment and inference problems
• Finance and econometrics
• Applications in genetics, neuroscience and health care

Cutting­edge research in these areas leverages and builds upon many mathematical tools including:

• Functional data analysis
• Graphical models and iterative algorithms
• Optimization

Collegial, Supportive and Expansive

The DSO Ph.D. program is research-focused, collegial, supportive and highly interactive. Ph.D. students are viewed as “junior colleagues” and are encouraged to become involved in academic research with faculty early in their doctoral studies through the following programs and initiatives:

• Low student-to-faculty ratio coupled with the faculty's "open-door" policy leading to frequent and meaningful collaborations. Unlike many other PhD programs, successful students often collaborate with several different faculty members, both in operations and statistics, throughout their PhD. Successful students typically co-author several papers with faculty members, which are often published in top peer-reviewed journals in the field.
• Presentations by invited faculty from around the world as part of a regular seminar provide an opportunity for students to experience the breadth of the field and meet renowned scholars. Click here for more information.
• Internal brown-bag seminars and colloquia with other research-oriented universities in Southern California

Operations Management Research

Research Environment

The structure of the Ph.D. program permits great flexibility, allowing custom-tailored courses of study emphasizing all areas of specialization within the department. In addition to our own faculty, students typically draw on the distinguished faculty and outstanding resources available throughout the University, most notably in economics, mathematics, industrial and systems engineering, computer science, and the Information Sciences Institute. The Center for Global Supply Chain Management(GSCM) and the Institute for Communication Technology Management (CTM) are also key resources to this program.

Recognized Research Authorities

The Operations Management faculty have served as senior editors and associate editors of premier management journals, including:

• Management Science
• Manufacturing and Service Operations Management
• Naval Research and Logistics
• Operations Research
• Production and Operations Management

Faculty Honors

• Fellows, Production and Operations Management Society
• National Science Foundation Grants

Learn More:   Explore the Data Sciences and Operations department to learn more about current research and faculty activities.

Statistic Research

Research by the statistics faculty emphasizes developing new methodology that grows out of business and public policy applications. In methodology, the statistics faculty's research covers a wide segment of the field of statistics including Bayes and empirical Bayes methods, data mining, categorical data, hidden state Markov models, classification rules, clustering methods, econometric and psychometric models, and general linear models. Applications include finance, marketing, real estate, health policy, defense system testing, computer science and discrimination in employment and in sentencing.

The Statistics faculty members are among the leading authorities of Business Statistics worldwide. One senior Statistics faculty member plays a leading role in the profession's advice to the federal government on statistical methodology. In addition, Statistics faculty members have served as senior editors and associate editors of premier management journals, including:

• Annals of Statistics
• Journal of the American Statistical Association
• The Econometrics Journal
• Journal of Multivariate Analysis
• Statistica Sinica
• Fellow of the American Statistical Association; Noether Young Scholar Award; National Science Foundation Grants; National Science Foundation CAREER Award

Learn More:   Explore the Statistics group to learn more about current research and faculty activities.

Faculty Coordinator:   Vishal Gupta , Associate Professor of DSO

OPERATIONS MANAGEMENT REQUIREMENTS

Sample Course Schedule

• DSO 671: Inventory Models and Supply Chain Management
• GSBA 602: Selected Issues in Economic Theory
• ISE 631: Linear Programming
• Elective (1)
• ISE 538: Elements of Stochastic Processes (or MATH 505B: Applied Probability)
• DSO 677: Dynamic Programming and Markov Decision Processes
• ISE 630: Foundations of Optimization
• GSBA 604: Regression and Generalized Linear Models for Business
• BUCO 634: Communication for Doctoral Students: Conference Papers and Presentations
• Summer Research Paper

Research Development Beginning in the first semester, students will apprentice with a faculty mentor to learn research fundamentals. Typically students are assigned to a new mentor each semester in the first year. During the summer following the first year in the program, students are expected to research and complete a Summer Research Paper.

Screening Examination In addition to the papers and examinations assigned in their first-year courses, a screening process will occur in May at the end of the first year. This process will include a review of each student's grades, an analysis of competence in written communications and a written exam based on the material covered during the first year. The results of the examination help the faculty determine students’ developmental needs and fitness to continue in the program.

• DSO 670: Current Research in Operations management
• 3 electives
• DSO 674: Queuing and Stochastic Networks
• ISE 632: Metwork Flows and Combinatorial Optimization
• 2 electives
• BUCO 633: Communication for Doctoral Students: Foundations of Academic Writing
• BUCO 637: Communication for Doctoral Students: Succeeding as a Teacher
• Independent Dissertation Preparation (2)

Research Development At this stage in the program, the relationships Ph.D. candidates have developed through the research apprentices often lead to collaborative research projects. Ph.D. candidates continue to apprentice faculty with increasing responsibility. They also begin work on their dissertations and are expected to have an approved dissertation proposal before the start of the fourth year.

Teaching Development Ph.D. candidates begin developing teaching skills by serving as a teaching assistant for one or two classes and engaging in teaching skills workshops.

Qualifying Exam The examination qualifying a student for candidacy for the Ph.D. degree is partly written and partly oral, designed, at least in part, to test the student’s fitness to undertake independent research. After successful completion of the qualifying exams, the student is admitted to candidacy.

Research Development Research is the primary activity in the fourth year and beyond. Faculty view Ph.D. candidates as “junior colleagues” and partner to write and present papers.

Teaching Development Ph.D. candidates co-teach one course with a faculty member.

Dissertation Defense & Submission The dissertation is the culminating work of a student’s independent research. In the fourth or fifth year of the program, students complete, defend and submit the dissertation. This is the last step to earn the Ph.D. degree.

STATISTICS REQUIREMENTS

• MATH 541B Introduction to Mathematical Statistics
• ISE 630 Foundations of Optimization
• MATH 507A Theory of Probability or MATH 505a Applied Probability
• MATH 505b Applied Probability or Math 506 Stochastic Processes
• DSO 607 High-Dimensional Statistics and Big Data Problems
• GSBA 604 Regression and Generalized Linear Models for Business
• MATH 541A Introduction to Mathematical Statistics 1 elective

Research Development Beginning in the first semester, students will apprentice with a faculty mentor to learn research fundamentals. Typically students are assigned to a new mentor each semester in the first year. In the summer following the first year in the program, students research and complete a Summer Research Paper.

• MATH 541B Introduction to Mathematical Statistics or MATH 547 Methods of Statistical Inference (if already taken 541B)
• 3 electives (including a graduate level class in a selected business area)
• DSO 599 Special Topics Class

Research Development At this stage in the program, the relationships Ph.D. candidates have developed through the research apprentices often lead to collaborative research projects. Ph.D. candidates continue to apprentice faculty with increasing responsibility. They also begin work on their dissertations.

Qualifying Exam Students are required to complete the qualifying exam by November 1 of their third year. The examination qualifying a student for candidacy for the Ph.D. degree is partly written and partly oral, designed, at least in part, to test the student’s fitness to undertake independent research. After successful completion of the qualifying exams, the student is admitted to candidacy.

The department maintains a highly supportive and collegial atmosphere that fosters close cooperation and coordination among the faculty to ensure the integration of the several specific disciplines represented by individual members of the group. This collaborative imperative guarantees the continued relevance and timeliness of the teaching and research programs.

Student Background: Our students come from all of over the world. They have strong academic backgrounds and bring with them a variety of experiences prior to joining the program.

Rashmi Ranjan Bhuyan

• PhD Candidate in Data Sciences and Operations

Rashmi is a fourth year Ph.D. student in the statistics group at USC Marshall School of Business. He is broadly interested in machine learning and it's application in social science and business.

Sebnem Manolya Demir

• PhD Student in Data Sciences and Operations

Katerina Giannoutsou

Katerina Giannoutsou is a PhD student in the Data Sciences and Operations department in the Marshall School of Business. She previously obtained her bachelor’s degree in Mathematics and Economics at New York University and worked as a Research Assistant at the Operations Research Center of MIT where she focused on healthcare operations. Her current research focuses in mechanism design and the dynamics of two-sided platforms.

Sang-Wook Lee

Second year Ph.D. student in Data Sciences and Operations at USC Marshall School of Business

APPLYING TO THE PhD PROGRAM

Dates + deadlines.

December 15, 2023: Application Deadline - Accounting, Data Sciences & Operations, and Management & Organization* 

January 15, 2024: Application Deadline - Finance & Business Economics and Marketing 

The link to the PhD Program application is available on the Admissions page and the next opportunity to apply is for Fall 2024 admission. Late applications may or may not be considered at the discretion of the admissions committee. 

Admissions decisions are made from mid-February to mid-April. You will be notified by email when a decision has been made.

ADMISSIONS CONTACT

Ph.D. Program USC Marshall School of Business 3670 Trousdale Parkway, BRI 306 Los Angeles, California 90089-0809 EMAIL

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