masters or phd in data science

Best Master’s in Data Science for 2024

It’s no secret that the need for data experts is growing due to the exponential amount of data being generated every day. One of the best ways to gain the in-demand skills to be able to harness, analyze, and create value from data is pursuing a master’s degree. This ranking was last updated February 2024.

*Please note that not all schools offer this specific type of information.

UC Berkeley’s Master’s in Data Science — Online

Master Key Skills in Data Mining, Machine Learning, Research Design & More

The online Master of Information and Data Science (MIDS) program is preparing the next generation of experts and leaders in the data science field and providing students with a UC Berkeley education without having to relocate. Students graduate with connections to UC Berkeley’s extensive alumni network in the Bay Area and across the world. All international applicants will be required to submit official Test of English as a Foreign Language (TOEFL) scores.

Syracuse University MS in Applied Data Science Online

Our Updated Curriculum at Syracuse University Prepares You to Be a Leader in Data Science

Don't get left behind as the data science industry evolves. In as few as 18 months, earn your MS in Applied Data Science from Syracuse University, ranked #25 in Best Online Graduate Computer Information Technology Programs. Apply today to gain the analytical, technical and managerial expertise required to stand out in a competitive job market. No GRE required.

1. Harvard University

Harvard University’s masters in data science does not require test scores, and applicants are advised not to submit or mention them. Three letters of recommendation are needed to apply, and international students must provide a TOEFL or IELTS score if the primary language of their undergraduate instruction is not English. Tuition costs $61,768 for students’ first year, and then $30,884 for the second year (one term only). The application deadline was December 1, 2023, for the fall term. (*Note on reported undergraduate GPA for 2023-24 enrollees: “only includes students whose undergraduate institutions used a 4-point scale (75% of enrollees)”).

2. University of North Texas

The University of North Texas does not require the submission of any standardized tests to apply, but international students must provide proof of English language proficiency. Two letters of recommendation are needed. Tuition and fees for in-state students is about $8,600 (nine credit hours per semester) and about $16,200 for out-of-state students (nine credit hours per semester). The deadline to apply for the fall 2024 cohort was December 10, 2023.

3. New York University

NYU’s master’s in data science program does not require a GRE score, but international students must turn in either a TOEFL or IELTS scores—or meet certain exceptions. Three letters of recommendation are needed to apply. Annual tuition is $41,184 for domestic and international students, and the deadline to apply is January 22 for the fall 2024 cohort. Four months post-program, graduates earn median pay of $132,522.

Earn Your Master’s in Data Science Online From SMU

Want to pursue a top 3 job in the U.S.? Earn a M.S. online and become a data scientist with SMU

Designed for working professionals looking to advance their careers, DataScience@SMU is an online Master of Science in Data Science from Southern Methodist University. The program’s interdisciplinary curriculum prepares data science professionals to work with large datasets, analyze information and relate findings. The program blends live online classes, self-paced coursework and in-person learning experiences with classmates and faculty.

4. University of Michigan--Ann Arbor

At the University of Michigan—Ann Arbor, standardized tests are not required to apply for its data science master’s program. Two letters of recommendation are needed, and international students must submit a TOEFL score. The program is jointly run by the university’s statistics, biostatistics, electrical engineering and computer science, and information units. The deadline to apply for the fall 2024 cohort is January 15. Annual tuition is about $13,600 for in-state students and $27,400 for those out of state.

5. Carnegie Mellon University

Carnegie Mellon’s master of computational data science requires a GRE score to apply, but it is waived for CMU undergraduates. Three letters of recommendation are needed to apply, and international student must submit a TOEFL score. Annual tuition costs $55,800. Four months post-program, graduates earn $135,000 in median pay. Applications were due in December 2023 for the fall cohort.

6. University of California--Irvine

The University of California—Irvine master of data science program does not use GRE scores in its admissions evaluation. International students are required to submit a TOEFL or IELTS score required. Three letters of recommendation are needed to apply, and the annual program tuition is $57,540. International applicants have until January 15 to apply; the deadline for U.S. residents is April 15.

7. University of Rochester

For the University of Rochester’s master’s in data science program, GRE scores are not required, but they may be submitted. Proof of English proficiency must be turned in via TOEFL, IELTS, or Duolingo scores—unless applicants qualify for a waiver. Three letters of recommendation are needed, and the program tuition is about $63,000 annually. Graduates make about $130,000 in annual median pay four months post-program. Applications are due February 15 for the fall 2024 cohort.

8. Indiana University--Bloomington

Indiana University’s Bloomington residential data science master’s program does not require applicants to submit a GRE score (GMAT not accepted), but language scores are required for non-native English speakers. Applications must submit three letters of recommendation. In-state students pay $9,000 in annual tuition; out-of-state student cost is about $30,000. Four months post-grad, alumni earn median pay of $127,000. The deadline to apply for the fall 2024 cohort was January 1.

Maryville University Master of Science in Data Science | Online

Model the future. Then make it your own with Maryville's Data Science program.

Earn your Master of Science in Data Science online from Maryville University in as few as two years. Prepare to collect and analyze data, manage teams, and create data-driven strategies.

9. University of Arizona

The master’s in data science program at the University of Arizona does not require applicants to submit GRE scores, but international students must turn in a TOEFL score. Two letters of recommendation are needed to apply. The deadline for the fall cohort 2024 is February 1. In-state students can expect to pay about $7,500 in annual tuition; out-of-state students will pay about $17,400 per year.

10. University of Delaware

At the University of Delaware, applicants have until February 1 to apply for its master of science in data science program. No standardized tests are required to be submitted, but international students must turn in a TOEFL score. Three letters of recommendation are needed. The program equips students with skills in probability and statistics, databases and data mining, machine learning, mathematics and computation, and ethics.

11. Appalachian State University

Appalachian State’s master of science in applied data analytics requires a GMAT or GRE score to apply, but waivers are available. Three letters of recommendation are needed, and international students must submit a TOEFL score. Four months post-program, graduates earn median pay of $74,000. The deadline to apply for the fall 2024 cohort is July 1.

12. University of Minnesota

The University of Minnesota’s master’s in data science program does not require applicants to submit standardized tests to meet its March 1 application deadline. Three letters of recommendation are needed, and international students must submit either a TOEFL or IELTS score.

13. Oklahoma State University

Oklahoma State University requires students to submit a GRE or GMAT for admission into its master’s program in business analytics and data science. A TOEFL score is required for most international students. Three letters of recommendation are needed. The application deadline for fall 2024 admission is January 15. Four months post-program, graduates earn base median pay of about $90,000.

14. University of Missouri

At the University of Missouri, standardized test results are not required but may be submitted for those applying to the master’s in data science and analytics program. No letters of recommendation are required, but two are suggested. International students from non-English speaking nations may be required to provide proof English language proficiency. Final admissions deadline is June 1 for the fall 2024 cohort. Tuition cost ranges from $575 to $1,325 per credit hour, depending on residency status.

15. Georgia State University

George State’s data science and analytics master’s program does not require a GMAT or GRE score for admission—or letters of recommendation—but they may boost your application or make you eligible for certain scholarships. International students must provide an English proficiency score or meet the exceptions. Application deadline is July 1 for U.S. residents and March 18 for international students interested in enrolling in fall 2024. Graduates earn a median pay of $95,000 four-months post-graduation.

16. Maryville University

Maryville University’s data science master’s program does not require GRE or GMAT scores or letters of recommendation. But, for international students, a TOEFL score is needed. Annual tuition costs $28,800. For the fall 2024 cohort, the admissions process is rolling for U.S. residents, but the deadline is May 1 for international students.

17. University of Michigan--Dearborn

The University of Michigan—Dearborn’s master of science in data science program does not require standardized testing to apply, but international students must turn in a TOEFL score. Two letters of recommendation are needed. The deadline to apply for the fall 2024 cohort is August 1 for domestic applicants and May 1 for those abroad. Annual tuition ranges from about $11,300 to $20,100, depending on residency.

18. New York Institute of Technology

At the New York Institute of Technology, international students must score and submit at least a 300 on the GRE and submit an English proficiency test result. Two letters of recommendation are needed to apply. Admissions deadlines are July 1 for international students and August 1 for U.S. residents.

19. University of San Francisco

The University of San Francisco does not require students to submit standardized tests to apply, but proof of English proficiency is needed for international students. Two letters of recommendation are required. The deadline to apply for the fall 2024 cohort is March 1, and annual tuition is about $56,500.

20. DePaul University

DePaul University’s data science master’s program is test and recommendation letter optional, but international students must submit a score of English proficiency. Annual tuition costs $47,736. The deadline to apply for the fall 2024 cohort is June 15 for international students and August 1 for U.S. residents.

21. Marquette University

Marquette University recommends domestic students submit GRE score for admission into its master’s in data science program. International students must submit GRE and test of English proficiency score. Annual program tuition is $21,203. The deadline to apply for fall 2024 admission is August 1 for U.S. residents and June 1 for international students.

22. Willamette University

Willamette University does not require a GRE or GMAT score submission to apply for its master’s in data science program, but they can be submitted to supplement an application. Two letters of recommendation are needed, and international students must turn in a TOEFL score. Annual tuition costs $51,300. In-person classes are offered in the evening in both Portland and Salem, Oregon. Coursework includes machine learning with Python, data visualization, data engineering, and ethics and policy. The application deadline is March 15 for those abroad and May 15 for U.S. residents.

23. Rochester Institute of Technology

Rochester Institute of Technology has a rolling admission cycle for its master’s degree in data science program. GRE scores are only required for international students, who are also required to include a TOEFL score. Annual tuition is $49,770, and two letters of recommendation are needed to apply. Four months post-program, graduates earn $131,200 in base annual pay.

24. Texas Tech University

Texas Tech University’s master of science in data science program does not require students to submit GMAT or GRE scores, but they are encouraged. All international applicants must provide proof of English proficiency. Letters of recommendation are not required. Applications for the program’s summer 2024 cohort are due May 1; international students are recommended to apply six months in advance. Graduates earn $71,000 in median base pay four months post-program.

25. Worcester Polytechnic Institute

The Worcester Polytechnic Institute does not require standardized tests to be submitted to apply for its master of science in data science program, but three letters of recommendation are needed. Applicants seeking to attend WPI whose native language is not English must submit official TOEFL, IELTS, or Duolingo scores. Graduates four months post-program earn median pay of $117,500. Deadlines to apply for the fall 2024 cohort are April 1 or July 1, depending on applicants’ region.

26. University of St. Thomas

The University of St. Thomas does not require standardized testing or letters of recommendation to apply for its master’s in data science program. TOEFL is needed for international students. Admission to its fall 2024 cohort is a rolling process, and courses cost $3,825 each.

27. American University

American University’s data science program is test optional and does not require any letters of recommendation to apply. However, for international students, a TOEFL score is needed. Tuition costs about $35,000 annually, and graduates earn a median base salary of $81,600 four months post-program. The deadline to apply for fall 2024 is February 15.

28. University of Maryland

The University of Maryland’s master of professional studies in data science and analytics is standardized test option, but international students must submit a TOEFL score. No letters of recommendation are required for admission into the program, which costs $32,000 annually in tuition. The deadline to apply for the fall 2024 cohort for domestic students is June 14 and March 15 for those abroad.

29. CUNY Graduate Center

CUNY Graduate Center’s master’s program in data science requires a GRE score for fall 2024 admission, and successful applicants are expected to place in and above the 80th percentile on the GRE quantitative portion. Two letters of recommendation are needed as well as a TOEFL score for international students. Applications are due April 15.

Frequently Asked Questions

Data science is one of the fastest growing fields—job openings are expected to grow by 35% by 2023, according to the U.S. Bureau of Labor Statistics . And students graduating with a master’s in data science often land six figure salaries. The reason it's a fast growing field, with high paying jobs, is because companies across all industries want data-savvy professionals in this era of digitization. Data provides companies and organizations with the resources they need to make better decisions—and in turn, they need professionals with data science skills who know how to understand and analyze data. 

The GPA you’ll need to get accepted into a master's program for data science varies by school. For all of the programs ranked by Fortune for 2024, the average undergraduate GPA for enrollees was 3.27. Students at Harvard and New York University had the highest GPA, with 3.87 and 3.75, respectively. Marquette University enrollees had the lowest reported GPA—at 3.01.

Master’s degree programs in data science can be offered in person, online or in a hybrid format—and that might be the difference in what the “best program” for you means. Fortune ranks the top five in-person programs for 2024 as: Harvard University, the University of North Texas, New York University, University of Michigan—Ann Arbor, and Carnegie Mellon University. Additionally, our ranking of the top five online programs in 2023 include: University of Southern California, UC—Berkeley, Bay Path University, New Jersey Institute of Technology, and Clemson University.

On average, it takes about one-and-a-half to two years to complete a master’s degree program in data science—with most programs requiring roughly anywhere from 25 to 60 credits to graduate. So it does depend on each individual program and whether you choose to be a full-time or part-time student. That said, thanks to a boost in salary and expanded career options, many students find it worthwhile to obtain a master’s degree in data science—and Gen Z considers the role of data scientist to be one of the most satisfying occupations .

A master’s degree in data science will teach you how to understand and analyze data. But because it's a recently defined career path, how it's applied can vary significantly. As Maurizio Porfiri, a New York University professor, told Fortune: “It’s a weird thing because it’s very vague. I discovered after a while that I had become a data scientist : people just started to refer to me as such.” But sometimes the first step to finding your place in the world of data science is picking a specialization—what type of problem you want to solve by using data. And a master’s degree can either help you find that specialization, or if you’ve already got the answer, will teach you the skills to pursue it.

Fortune compiled a list of seven universities that offer free online data science courses , which offers prospective students an opportunity to learn more about this field. Each university—Harvard University, the University of Michigan, UC Irvine, John Hopkins University, Columbia University, MIT, and Duke University—offers a different course, from linear regression to data science ethics to data science in real life. However, the common goal of these free courses is to give people an inside look into the field.

In 2022, data scientists earned median salaries of $103,500, according to the U.S. Bureau of Labor Statistics . But a degree from a top program might mean even more money; New York University's (ranked third on Fortune’s best in-person data science programs) 2022-23 graduates with a master’s in data science earned an average salary of $143,000 four months after graduation, according to data provided by the university.

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Master's in Data Science

Master’s in data science program overview.

The Data Science master's program, jointly led by the  Computer Science  and  Statistics  faculties, trains students in the rapidly growing field of data science. 

Data Science lies at the intersection of statistical methodology, computational science, and a wide range of application domains.  The program offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition.  The program focuses on topics such as reproducible data analysis, collaborative problem solving, visualization and communication, and security and ethical issues that arise in data science.

To earn the Master of Science in Data Science, students must complete 12 courses. This requires students to be on campus for at least 3 semesters (one and a half academic years). Some students will choose to extend their studies for a fourth semester to take additional courses or complete a master’s thesis research project.

SEAS will be hosting virtual information sessions this Fall for students interested in the Data Science program. Registration for these sessions is available on the  Admissions Events page for prospective graduate students .

Why pursue a master’s degree in Data Science?

With companies and organizations better able to capture data in a multitude of ways, data-driven decision making is changing the way businesses operate. Powerful analytics tools can model and predict how consumers will behave or markets will respond. Consequently, an understanding of data science is a 21st century job skill that can be beneficial in many different careers.

Data Science Degree Career Paths

Data Science career paths are flexible. There are different pathways to use data science skills.

• Data science professional - data analyst, database developer, or data scientist.
• Analytics-enabled jobs - functional business analyst or data-driven manager.

Data science professionals like data analysts can become qualified for a data science or data system developer role depending on where they deepen their expertise. By expanding knowledge in Artificial Intelligence, statistics, data management, and big data analytics, a data analyst can transition into a data scientist role. By building on existing technical skills in Python, relational databases, and machine learning, a data analyst can become a data system developer. 

Requirements

There are no formal prerequisites for applicants to this master’s program. However, successful applicants do need to have sufficient background knowledge of calculus, linear algebra and differential equations; familiarity with probability and statistical inference; fluency in at least one programming language such as python or R, and an understanding of basic computer science concepts. As Data Science is an interdisciplinary field, SEAS welcomes applicants with undergraduate training in a wide range of academic disciplines. 

• How to Apply

Learn more about  how to apply to the Data Science degree program  or  apply now .

What should a graduate of the Data Science program be able to do?

The design of the program is based on eleven learning outcomes developed through discussions between the computer science and statistics faculty:

Build statistical models and understand their power and limitations

Design an experiment

Use machine learning and optimization to make decisions

Acquire, clean, and manage data

Visualize data for exploration, analysis, and communication

Collaborate within teams

Deliver reproducible data analysis

Manage and analyze massive data sets

Assemble computational pipelines to support data science from widely available tools

Conduct data science activities aware of and according to policy, privacy, security and ethical considerations

Apply problem-solving strategies to open-ended questions

Financing Your Degree

Students typically finance their master’s degree program with a combination of loans, savings, family support, grants (from governments, foundations and companies), fellowships and scholarships. We recommend you visit the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences (Harvard Griffin GSAS)  Funding and Financial Aid  website prior to your application to learn more about your options.

Teaching Fellowships

Approximately 15% of our students are paid Teaching Fellows, usually in the second year. TFing in the first semester is highly unusual. Teaching compensation is paid out at Harvard graduate student rates.

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Data Science

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

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

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

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

Standardized Tests

GRE General:  Not Accepted

APPLICATION DEADLINE

Questions about the program.

Doctor of Philosophy in Data Science

Developing future pioneers in data science

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

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

LEARNING OUTCOMES

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

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

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

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

Ph.D. Student Profile: Jade Preston

Ph.D. Student Profile: Beau LeBlond

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Home / Data Science Programs / PhD in Data Science

Data Science PhD Programs

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

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

SPONSORED SCHOOLS

Syracuse university, master of science in applied data science.

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

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

Southern Methodist University

Master of science in data science.

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

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

University of California, Berkeley

Master of information and data science.

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

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

info SPONSORED

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

Why Earn a PhD in Data Science?

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

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

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

PhD Benefits vs. Downsides

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

Benefits of a PhD in Data Science

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

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

Downsides of a PhD in Data Science

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

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

Is a PhD in Data Science Worth It?

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

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

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

Do You Need a PhD to Land a Job?

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

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

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

Careers for Data Science PhD Holders

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

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

Sample job titles include:

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

PhD in Data Science Curriculum

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

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

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

Dissertation

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

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

Data Science vs. Business Analytics vs. Specialties

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

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

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

Considerations When Choosing a PhD Program

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

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

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

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

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

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

How Much Does a PhD Cost?

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

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

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

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

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

How Long Does a PhD in Data Science Take?

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

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

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

Interested in STEM Careers? 

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

Data Science and Analytics Careers:

• Data Scientist
• Data Analyst
• Business Analyst

Computer Science, Computer Engineering and Information Careers:

• Computer and Information Research Scientist

Marketing and User Research Careers:

• UX Designer  

Compare Careers and STEM Fields:

• Cybersecurity vs. Computer Science

Related Graduate STEM Degrees

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

Related Undergraduate STEM Degrees

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

PhD in Data Science School Listings

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

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

PhD in Data Science/Analytics Online

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

Colorado Technical University

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

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

Enrollment Type: Self-paced

Length of Program: 4 years

Credits: 100

Admission Requirements:

Carnegie Mellon University

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

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

Enrollment Type: N/A

Length of Program: 2 years

Credits: N/A

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

Chapman University

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

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

Enrollment Type: Full-Time and Part-Time

Credits: 70

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

Indiana University – Indianapolis

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

Name of Degree: Ph.D. in Data Science

Credits: 90

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

Kennesaw State University

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

Name of Degree: Doctoral Degree in Analytics and Data Science

Enrollment Type: Full-Time

Credits: 78

• Statement of how this degree facilitates your career goals

PhD in Data Science/Analytics On-Campus

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

New York University

Center for data science, new york , new york.

Credits: 72

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

Institute for Computational and Data Sciences

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

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

Computational Data Sciences  

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

University of Maryland

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

Name of Degree: Doctor of Philosophy in Information Studies

Credits: 60

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

University of Massachusetts in Boston

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

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

Credits: 42

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

University of Nevada – Reno

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

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

Length of Program: 4+ years

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

University of Southern California

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

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

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

University of Washington

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

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

Worcester Polytechnic Institute

Worcester, massachusetts.

Data Science

Students enrolled in the Master of Liberal Arts program in Data Science will develop the skills necessary to analyze, discover, and innovate in a data-rich world. Students gain hands-on experience conducting interdisciplinary data science research.

The Data Science master’s program, jointly led by the Computer Science and Statistics faculties, trains students in the rapidly growing field of data science. The program offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition. The program focuses on topics such as reproducible data analysis, collaborative problem solving, visualization and communication, and security and ethical issues that arise in data science.

Khoury College of Computer Sciences

College of engineering, data science.

Our Master’s in Data Science program will provide you a comprehensive foundation in the skills and theory of data science, as well as the tools and experience to translate data into clear, innovative, and strategic insights.

Data has become one of the most critical tools in shaping our economy, society, and culture. In fact, for the fourth year in a row, Glassdoor named data scientist as the best job in America. Now more than ever, the world needs people who can understand this untapped resource and draw conclusions so vital to future success.

We developed our Master’s in Data Science with that in mind. As an interdisciplinary program between the Khoury College of Computer Sciences and the College of Engineering, you’ll develop comprehensive expertise in mathematics, computing, and data engineering so you can take your data career to the next level. That interdisciplinary aspect means you’ll receive robust, comprehensive exposure to the world of data science. Our elective courses will help you create a curriculum that’s unique to your own interests. And with a full spectrum of exciting co-op, research, and internship opportunities, you’ll gain even more real-world experience to help you emerge from your studies prepared for whatever and wherever data leads you next.

More Details

Unique features.

• Our program meets F-1 international student status requirements.
• This is an interdisciplinary program between Khoury College of Computer Sciences and the Department of Electrical and Computer Engineering in the College of Engineering.
• Courses are tailored toward technically or mathematically trained students.
• Elective courses allow you to explore what interests you.

Program Objectives

• Data Management : Ability to effectively utilize integrated databases for managing large-scale datasets.
• Machine Learning : Ability to effectively apply an appropriate machine-learning model to a problem.
• Analysis and Visualization : Ability to effectively communicate insights derived from data.

An MSDS degree enables you to collect data from numerous sources (databases, files, images) and integrate them into a form in which the data is fit for analysis. Students develop strong programming and analysis skills to explore data, produce summary statistics, perform statistical analyses; using standard data mining and machine-learning models for effective analysis. Students learn to work with both structure and unstructured data. The program provides you with the knowledge to manage, process, analyze, and visualize data at scale. You will learn about the limitations of machine learning and data mining methods as applied to real-world problems and communicate the advantages/disadvantages of the methods to non-data experts. Students will carry out the full data analysis workflow, including unsupervised class discovery, supervised class comparison, and supervised class prediction. They will learn how to properly summarize, interpret, and communicate the analysis of results. In this program you will develop methods for modeling, analyzing, and reasoning about data arising in one or more application domains such as social science, health informatics, web and social media, climate informatics, urban informatics, geographical information systems, business analytics, bioinformatics, complex networks, public health, and game design.

Career Outlook

Students who successfully complete their master's in data science are qualified to seek jobs as:

• Data scientist
• Machine learning engineer
• Applications architect
• Enterprise architect
• Data architect
• Infrastructure architect
• Data engineer
• Business intelligence developer
• Data analyst

Employees with data science expertise are sought out in every industry, not just in technology. "Data scientists are highly educated—88 percent have at least a master's degree and 46 percent have PhDs—and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist," reports KDnuggets, a leading site on Big Data.

Testimonials

Professor olga vitek, khoury college of computer sciences, looking for something different.

A graduate degree or certificate from Northeastern—a top-ranked university—can accelerate your career through rigorous academic coursework and hands-on professional experience in the area of your interest. Apply now—and take your career to the next level.

Program Costs

Finance Your Education We offer a variety of resources, including scholarships and assistantships.

How to Apply Learn more about the application process and requirements.

Requirements

• Online application and fee
• Transcripts from all institutions attended 
• Statement of purpose that should include career goals and expected outcomes and benefits from the program
• Recent professional resumé listing detailed position responsibilities
• Three letters of recommendation
• GPA minimums: 3.0 on a 4.0 scale, 8.0 on a 10.0 scale, or 80 on a 100 scale
• Official TOEFL (100 minimum) or IELTS (7.5 minimum) examination scores (international students only)
• GRE Optional

Are You an International Student? Find out what additional documents are required to apply.

Global Engagement Learn how our teaching and research benefit from a worldwide network of students, faculty, and industry partners.

Admissions Dates

Applicants must submit the online application and all required admission materials no later than the stated deadlines to be considered for admission. Admissions decisions are made on a rolling basis.

Industry-aligned courses for in-demand careers.

For 100+ years, we’ve designed our programs with one thing in mind—your success. Explore the current program requirements and course descriptions, all designed to meet today’s industry needs and must-have skills.

View curriculum

Co-op makes the Northeastern graduate education richer and more meaningful. It provides master’s students with up to 12 months of professional experience that helps them develop the knowledge, awareness, perspective, and confidence to develop rich careers. In addition to the esteemed faculty, many students enroll in the master’s programs largely because of the successful co-op program.

Graduate students typically have an experiential work opportunity following their second semester. This could be a 6- to 8-month co-op or a 3- to 4-month internship. Those who initially experience co-op may have the opportunity to seek an internship for the following summer, or vice versa.

Student participation in experiential education provides enhanced

• Maturity, responsibility, and self-knowledge
• Technical expertise
• Occupational information
• Job-seeking and job-success skills
• Networking opportunities with those in desired career paths

Northeastern’s co-op program is based on a unique educational strategy that recognizes that classroom learning only provides some of the skills students will need to succeed in their professional lives. Our administration, faculty, and staff are dedicated to the university’s mission to “educate students for a life of fulfillment and accomplishment.” Co-op is closely integrated with our course curriculum and our advising system. The team of graduate co-op faculty within the Khoury College of Computer Sciences provides support for students in preparing for and succeeding in their co-ops.

These multiple connections make co-op at Northeastern an avenue to intellectual and personal growth—adding depth to classroom studies, providing exposure to career paths and opportunities, and developing in students a deeper understanding that leads to success in today’s world.

Our Faculty

Northeastern University faculty represents a broad cross-section of professional practices and fields, including finance, education, biomedical science, management, and the U.S. military. They serve as mentors and advisors and collaborate alongside you to solve the most pressing global challenges facing established and emerging markets.

Michelle Borkin

Kylie Bemis

John Rachlin

Jeongkyu Lee

David Kaeli

By enrolling in Northeastern, you’ll be connected to students at our 13 campuses, as well as 300,000-plus alumni and more than 3,500 employer partners around the world. Our global university system provides you with unique opportunities to think locally and act globally and serves as a platform for scaling ideas, talent, and solutions.

Below is a look at where our computing and IT alumni work, the positions they hold, and the skills they bring to their organization.

Where They Work

What they do.

• Engineering
• Information Technology
• Business Development
• Entrepreneurship
• Program and Project Management

What They're Skilled At

• Software Development

Learn more about Northeastern Alumni on  Linkedin .

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• Academics /

Data Science Master’s Degree Program

Develop the technical and analytical skills you need to discover, analyze, model, and visualize information in today's data-rich world.

Online Courses

11 out of 12 total courses

On-Campus Experience

One 3-week course

$3,340 per course

Next Start Term: Spring 2025

Registration opens November 4, 2024

Program Overview

Data science is a complex and dynamic field, and one in which individuals with the right mix of skills are likely to remain in high demand. Through the graduate program in data science, you will master the technical, analytical, and practical skills you need to solve real-world, data-driven problems. 

In the master’s degree program in the field of data science, you will:

• Develop an in-depth understanding of data science methods in predictive modeling, data mining, machine learning, artificial intelligence, data visualization, and big data.
• Build the skills to explore, analyze, manage, and visualize large data sets using the latest technologies.
• Apply data science and analytical methods to address data-rich problems from a variety of fields, think critically about data, and drive decision making.
• Develop the skills for quantitative thought leadership, including the ethical and legal dimensions of data analytics, as well as effective communication and collaboration.

Program Benefits

Customizable course curriculum with stackable certificate options

Team-based capstone project

Personalized academic & career advising

Entrepreneurial opportunities through the Harvard Innovation Labs

Paid research options

Harvard Alumni Association membership upon graduation

Customizable Course Curriculum

Our curriculum is flexible in pace and customizable by design. You can study part time, choosing courses that fit your schedule and align with your professional goals. Learning is hands-on. Classes, including the precapstone and capstone, feature collaborative activities like online discussions and group projects.

Core and elective course topics include:

• Artificial intelligence
• Data modeling
• Data science and engineering
• Machine learning
• Data mining
• Wearable devices

11 Online Courses

• Primarily asynchronous
• Fall, spring, January, and summer options

You’ll complete the 3-week precapstone course on campus during a January or summer session, working with your team to kickstart your capstone project.

Capstone Project

You’ll collaborate with an industry, government, or academic partner to investigate a real-world topic.

Learn more: Capstone Team Collaborates with NASA

The path to your degree begins before you apply to the program.

First, you’ll register for and complete 2 required courses, earning at least a B in each. These foundational courses are investments in your studies and count toward your degree, helping ensure success in the program.

Alternative admissions pathway: you may choose to pursue the MITx MicroMasters® Program Pathway .

Enroll for your first admission course this spring. Course registration is open November 4, 2024–January 23, 2025.

To get started, explore degree requirements, confirm your initial eligibility, and learn more about our unique “earn your way in” admissions process.

Earn a Stackable Certificate

As you work your way toward your master’s degree, you can take courses that also count — or “stack” — toward a graduate certificate. It’s a cost-effective, time-saving opportunity to build specialized skills and earn more professional credentials.

For each certificate, you can choose courses that best fit your goals.

Stackable graduate certificates include:

• Artificial Intelligence
• Data Science

A Faculty of Data Science Experts

Studying at Harvard Extension School means learning from the world’s best. Our instructors are experts in a wide variety of data science topics. They bring a genuine passion for teaching, with students giving our faculty an average rating of 4.3 out of 5.

Bruce Huang

Director, Information Technology Programs

Stephen Elston

Principal Consultant, Quantia Analytics LLC

Our Community at a Glance

Along with a cohort of diverse peers, you’ll gain hands-on experience conducting interdisciplinary data science research. Our students work in a variety of roles —including as management or IT consultants, business analysts, and data engineers. They're pursuing certificates to advance careers in data science, machine learning, or data analytics.

Download: Data Science Master's Degree Fact Sheet

Average Age

Courses Taken Each Semester

Work Full Time

Would Recommend the Program

Professional Experience in the Field

Pursued for Career Advancement

Careers & Alumni Outcomes

The long-term outlook for careers in data science is strong. As organizations develop new ways to collect data, demand for dedicated professionals with the right mix of skills to interpret that data will continue to expand.

Jobs in data science can be found in any industry that collects information. Your career path could range from developing new technologies for data mining, writing software to store and protect data, analyzing data through advanced modeling and visualization techniques, or applying data-driven solutions to today’s business problems.  

Sample alumni job titles:

• Data scientist
• Software engineer
• Analytics manager
• Data engineer
• Director of data science
• Computer systems analyst
• Quantitative developer
• Machine learning developer
• Big data architect

Career Advising and Mentorship

Whatever your career goals, we’re here to support you. Harvard’s Mignone Center for Career Success offers career advising, employment opportunities, Harvard alumni mentor connections, and career fairs like the Harvard Startup Career Fair and the Data Analytics, Science, and Technology Fair held on campus.

Your Harvard University Degree

Upon successful completion of the required curriculum, you will receive your Harvard University degree — a Master of Liberal Arts (ALM) in Extension Studies, Field: Data Science.

Expand Your Connections: the Harvard Alumni Network

As a graduate, you’ll become a member of the worldwide Harvard Alumni Association (400,000+ members) and Harvard Extension Alumni Association (29,000+ members).

Research and take advantage of all that Harvard has to offer: the amount and range of opportunities is mind-blowing.

Tuition & Financial Aid

Affordability is core to our mission. When compared to our continuing education peers, it’s a fraction of the cost.

After admission, you may qualify for financial aid . Typically, eligible students receive grant funds to cover a portion of tuition costs each term, in addition to federal financial aid options.

Learn more about the cost of attendance .

Coffee Chat: All About Technology Programs at HES

Are you interested in learning more about technology graduate degree programs at Harvard Extension School? Hear directly from our program director, academic advisors, and alumni.

What Can You Do With a Master’s Degree in Data Science?

A master’s degree in data science will give you both the theoretical knowledge and the practical skills you need to advance your career in this rapidly growing field.

Advancing your education in data science can help you develop important analytical, business, modeling, and visualization skills, as well as advanced programming and coding skills. You may learn cutting-edge trends in artificial intelligence, machine learning, and other developing technologies. Or you can explore new ways to apply your skills across different industries such as healthcare, advertising, wearable tech, or autonomous vehicles and drones.

Read more in our blog post What You Can Do with a Master’s in Data Science .

Is a Degree in Data Science Useful?

A degree in data science may not be required to get started in the field, especially for entry-level programmers and data analysts. Degrees in software engineering, computer science, math, and statistics can give you the basic skills you need to start a career in data science.

However, a graduate degree in data science offers a combination of skills that can help you stay competitive in the job market. In addition to programming and statistics, for example, a degree in data science offers advanced knowledge of modeling, data visualization, data analysis, and business intelligence.

Moreover, many mid- and senior-level positions such as data scientist or data architect for example, as well as most management positions, may require a master’s degree in data science.

Is a Career in Data Science a Good Career?

As organizations harness new technologies to gather and collect information more effectively, the demand for knowledgeable professionals with the skills to analyze that data correctly is growing—and quickly.

According to the US Bureau of Labor Statistics , jobs for data scientists and related positions are expected to grow 22 percent between 2020 and 2030.

And due to both growing demand and the complexity of the work, data science can be a lucrative career as well. According to Glassdoor , data science was the third-best job in the United States in 2022, based on median salary and availability of job openings.

How Long Does it Take to Complete the Data Science Graduate Program?

Program length is ordinarily anywhere between 2 and 5 years. It depends on your preferred pace and the number of courses you want to take each semester.

For an accelerated journey, we offer year-round study, where you can take courses in fall, January, spring, and summer.

While we don’t require you to register for a certain number of courses each semester, you cannot take longer than 5 years to complete the degree.

What Prerequisites Do You Need Before Applying for the Data Science Master’s Degree Program?

With Harvard Extension School’s unique “earn your way in” admissions process, there are no prerequisites required to begin your course of study in the data science master’s degree program. To be successful, however, the program assumes that you can write functions in Python and are comfortable with calculus. Learn more about what skills you need to be successful in the course curriculum .

Related Offerings

• Data Science Graduate Certificate

Harvard Division of Continuing Education

The Division of Continuing Education (DCE) at Harvard University is dedicated to bringing rigorous academics and innovative teaching capabilities to those seeking to improve their lives through education. We make Harvard education accessible to lifelong learners from high school to retirement.

• Certificate in Applied Data Science
• What is Cybersecurity?
• MICS Class Profile
• What Is Data Science?
• Careers in Data Science
• MIDS Class Profile
• Study Applied Statistics
• International Admissions
• Fellowships
• Student Profiles
• Alumni Profiles
• Video Library
• Apply Now External link: open_in_new

Home / Data Science

UC Berkeley’s Master of Information and Data Science | Online UC Berkeley’s Master of Information and Data Science | Online UC Berkeley’s Master of Information and Data Science | Online

Leverage the latest tools and analytical methods to work with data at scale, derive insights from complex and unstructured data, and solve real-world problems. Leverage the latest tools and analytical methods to work with data at scale, derive insights from complex and unstructured data, and solve real-world problems. Leverage the latest tools and analytical methods to work with data at scale, derive insights from complex and unstructured data, and solve real-world problems.

Get Admission and Tuition Information

Answer a few quick questions to determine if the Master of Information and Data Science program is a good fit for you.

No GMAT or GRE scores required

• Complete in 12–20 Months — Prepare to be a data science leader well-versed in data engineering, machine learning, statistical analysis, and more.
• Graduate from a No. 2-Ranked Program —The online MIDS program from the UC Berkeley School of Information (I School) is nationally recognized by Fortune magazine. 1
• Gain In-Demand Skills — Careers in data science are on the rise 2 . Our graduates are applying their skills at Amazon, Apple, Facebook, Microsoft, Google, and other top companies.

Earn Your Master’s in Data Science Online

The No. 2-ranked 3 Master of Information and Data Science (MIDS) program, delivered online from the UC Berkeley School of Information (I School), prepares data science professionals to be leaders in the field. By blending a multidisciplinary curriculum, experienced faculty from top data-driven companies, an accomplished network of peers, and the flexibility of online learning, the WASC-accredited datascience@berkeley program brings UC Berkeley to students, wherever they are. Learn more about data science.

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Complete a Rigorous, Holistic Curriculum

The multidisciplinary online data science master’s curriculum draws upon computer science, social sciences, statistics, management, and law. Students use the latest tools and analytical methods to work with data at scale, derive insights from complex and unstructured data, and solve real-world problems.

Flexible Program Paths

The 27-unit, online program is designed for the working professional’s schedule and can be completed on one of three paths: accelerated , standard , or decelerated . Students who wish to take the program on an accelerated or decelerated basis must receive approval from UC Berkeley.

The accelerated path  gives students the opportunity to take three courses per semester to complete the program in as few as 12 months.

The standard path  is designed for working professionals and can be completed in 20 months, with two courses per semester.

The decelerated path  allows students to drop down to one course per semester after the first term and complete the program in no more than 32 months.

Essential and Specialized Skills

The core curriculum focuses on the following key skills:

Research Design

Data Cleansing

Data Engineering

Data Mining and Exploring

Data Visualization

Information Ethics and Privacy

Statistical Analysis

Machine Learning

Featured Courses

The MIDS curriculum  features a wide range of courses that provide students with a comprehensive understanding of how data science can be used to inform decision making in their organizations. Students will complete programming-focused courses, like the featured courses below, in concurrence with courses that focus on the ethical impact of data science and how to effectively communicate results.

Applied Machine Learning

Students will learn how to apply crucial machine learning techniques to solve problems, run evaluations and interpret results, and understand scaling up from thousands of data points to billions.

Behind the Data: Humans and Values

This course examines the legal, policy, and ethical issues that arise throughout the full life cycle of data science. Case studies will be used to explore these issues across various domains such as criminal justice, national security, health, marketing, politics, education, and employment.

Natural Language Processing with Deep Learning

This course is a broad introduction to linguistic phenomena and our attempts to analyze them with machine learning. We cover a wide range of concepts, with a focus on practical applications such as information extraction, machine translation, sentiment analysis, and summarization.

MIDS students complete a capstone by executing a culminating project that integrates the core skills and concepts learned throughout the program. The capstone combines the technical, analytical, interpretive, and social dimensions required to design and execute a full data science project. Students learn integral skills that prepare them for long-term professional success in the field.

Explore our full multidisciplinary curriculum.

Request Information about the Online Master of Information and Data Science

Learn from a curriculum that prepares you to be a leader in data science.

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Our data science perspective.

The online master’s in data science takes a comprehensive approach to data analysis. While other information and data degree programs are adapting to the emergence of big data, the MIDS program is designed from the ground up to focus on the latest tools and approaches to working with data.

As a result, the data science master’s program differs from other analytic degree programs through its:

• Focus on working with complex , unstructured, user-generated data sets (i.e., big, messy data)
• Comprehensive, multidisciplinary curriculum  drawn from the social sciences, computer science, statistics, management, and law
• Coherent integration of the full life cycle of data  — from identifying the right questions to retrieving, cleaning, and modeling the data and communicating results
• Emphasis on the legal and ethical implications  of data privacy and security

The Online Learning Experience

The online master’s in data science combines advanced technology and in-person experiences to ensure you benefit from the full I School experience.

All of the online tools you need to succeed are hosted in one place: the virtual campus.

Access your weekly Zoom classes , where you will engage in meaningful discussions.

Complete coding work  through seamless GitHub integration in the virtual campus.

View upcoming course work and deadlines  with one click on your dashboard.

Attend class or complete course work from anywhere  using the mobile app.

In-Person Immersion

Further your connections with your classmates and professors at an in-person immersion experience. Here, you will participate in workshops, network with industry leaders, and learn about emerging trends in the information field.  

Learn more about the full student experience.

Valuable Relationships with MIDS Alumni

MIDS graduates are working around the world to solve social, economic, and health problems and applying their skills at top companies such as Amazon, Apple, Facebook, Microsoft, and Google. Students and alumni can connect with the I School community through the I School Slack workspace, the official I School professional networking platform.

Graduates employed at these companies excel in roles such as:

• Business data analyst
• Data analyst
• Data architect
• Data engineer
• Data scientist
• Solutions architect
• Systems engineer

“Visiting Uber was an amazing experience. After a 40-minute presentation, we spent over an hour asking questions to Kevin Novak (leader of the data science division) on everything from data science at Uber, to data science at small startups, to the evolving place of data science in the world.”

– Christopher Llop, MIDS graduate

“I rely heavily on the knowledge and confidence that I gained from the MIDS curriculum as I approach open-ended data science problems. My professors at UC Berkeley provided me with a strong basis in structured problem solving and critical thinking as a data scientist.”

– Erin Boehmer, data scientist at Fenix International

Learn more about how MIDS graduates have used these career connections to reach their career goals.

Choose a Program That Transforms Students into Data Science Leaders

Earn a master’s in data science online from UC Berkeley.

The master’s in data science program is seeking applicants who can make a positive impact on the I School community and beyond. A complete application must include the following:

• Online application
• Transcripts from all educational institutions attended
• Statement of Purpose and additional admissions statements
• Two professional letters of recommendation
• Current resume
• Application fee
• Optional:  GRE or GMAT scores
• TOEFL scores (if applicable)

Learn more about admissions requirements.

Application Deadlines

There are three program start dates throughout the year, and applications are reviewed on a rolling basis. The final deadline for the January 2025 cohort is  September 25, 2024 .

Review Application Deadlines

Upcoming Events

Learn more about the MIDS program and meet I School faculty and students during our online and in-person events.

Attend an Upcoming Event

Develop the Skills Needed to Become a Data Science Leader at Top Organizations

Earn a Master of Information and Data Science online from UC Berkeley.

• Best Online Master’s in Data Science Programs, Fortune , Ranked in 2022. ↩︎
• Computer and Information Research Scientists,  Bureau of Labor Statistics. Accessed February 2022. ↩︎
• Best Online Master’s in Data Science Programs , Fortune , Ranked in 2022. ↩︎

Return to footnote reference

Graduate Programs

Data science, request more info.

Brown University’s one or two-year, on-campus master's in data science stands at the forefront of innovation and impact in our data-driven world, equipping you with a blend of theoretical knowledge and practical experience.

The master’s in data science program prepares you for a unique career in the field, regardless of your disciplinary background. You'll integrate foundational elements from computer science, mathematics and statistics with deep domain-specific knowledge.

This program offers the following degree: 

• Master of science (Sc.M.): Coursework and capstone project

Mastering the extraction and value of complex data demands specialized skills, methods and tools. The program’s curriculum covers essential areas such as machine learning, data mining, visualization and data management.

Throughout your coursework, you’ll address key data science challenges and explore ethical and societal implications. You’ll develop foundational knowledge, apply it to real-world problems and understand the broader impacts of data-driven approaches. Experiential learning opportunities enable you to tackle authentic data science challenges firsthand.

Additional Information

*Brown undergraduates can apply to this program as a fifth-year master's degree.

Check back in October for information about our new online Data Science program.

Attend an Info Session

Application Information

Students entering the program will be required to have completed at least a year of calculus (at the level of MATH 0090 & 0100 ), a semester of linear algebra (at the level of MATH 0520 ), a semester of calculus–based probability and statistics (at the level of APMA 1650 ), and an introduction to programming (at the level of CSCI 0150 or 0170 ). You can find more information on these prerequisite courses on cab.brown.edu .

Admitted students will consult with the program director to acquire any additional preparation for the program.

Prerequisites

• Two semesters of calculus (at the level of MATH 0090 and 0100)
• Two semesters of linear algebra (at the level of MATH 0520 and 0540)
• 1 semester of calculus-based probability and statistics (at the level of APMA 1650)
• Introduction to Programming course (at the level of CSCI 0150 or 0170)
• Familiarity with Python or R and ability to write code

“Please find descriptions of these courses on Courses at Brown ”

Please see the  Data Science Institute website  for more information.

If you have any questions regarding the application process for this program, please email  [email protected] .

Application Requirements

Gre subject:.

Not required

GRE General:

Toefl/ielts:.

Required for any non-native English speaker who does not have a degree from an institution where English is the sole language of instruction or from a University in the following countries: Australia, Bahamas, Botswana, Cameroon, Canada (except Quebec), Ethiopia, Ghana, Ireland, Kenya, Lesotho, Liberia, Malawi, New Zealand, Nigeria, Zimbabwe, South Africa, Sierra Leone, Swaziland, Tanzania, Gambia, Uganda, United Kingdom (England, Scotland, Northern Ireland, Wales), West Indies, Zambia. The TOEFL iBT Special Home Edition and the IELTS Indicator exam are accepted. The data science program prefers scores above 620 (pBT) or 105 (iBT). The corresponding minimum IELTS score is 7.5. Students from mainland China may submit the TOEFL ITP Plus exam.

Official Transcripts:

Required. All applicants may upload unofficial transcripts for application submission. Official transcripts are ONLY required for enrolling students before class start. An international transcript evaluation (WES, ECE, or The Evaluation Company) is required for degrees from non-U.S. institutions before enrollment. For applicants with an undergraduate degree from India, WES and ECE are the preferred evaluation services.

Letters of Recommendations:

Three (3) recommendations required

Writing Sample:

Optional (a published or class paper in a relevant area)

Personal Statement:

Please tell us in 1000 words or less why you are specifically interested in the Data Science program at Brown University. Tell us something about yourself and your interests that we can’t figure out from your resume. What are your goals and plans with the degree after graduation? How do your plans align with Brown and the DSI’s mission statement?

Dates/Deadlines

Application deadline, 5 th year deadline, tuition and funding.

• Graduate Tuition & Fees : Please visit the  O ffice of Student and Financial Services  for up-to-date tuition rates.
• Scholarships:  Not available

Completion Requirements

• 3 credits in mathematical and statistical foundations
• 3 credits in data and computational science
• 1 credit in societal implications and opportunities
• 1 elective credit to be drawn from a wide range of focused applications or deeper theoretical exploration
• 1 credit capstone experience, which includes a paper and/or oral presentation
• Thesis: Not required.

Contact and Location

Data science institute, location address, mailing address.

• Program Faculty
• Program Handbook
• Graduate School Handbook

DiscoverDataScience.org

Is a PhD in Data Science Worth It?

Created by aasif.faizal

The most advanced option you can find is a Data Science PhD, which is an intensive and long-term commitment from which you will graduate at the very top of your field.

The truth is, many who establish thriving careers in data science don’t hold PhDs, and no one would argue that they are necessary to have on the table as one considers their educational options. Estimates for the number of data science PhDs is around one third of all who attend graduate school for data science. For a certain type of person – one who is highly studious, with an aptitude for and interest in research – PhD programs can be excellent experience that will situate you for a highly specialized career.

If you’re asking yourself, “Do I need a PhD in Data Science?,” the answer is no. (For a more expansive answer to this question, you can take a look at our article here: “Do I need a PhD in Data Science?”)

But is a PhD in Data Science worth it for those who do decide to take it on? The answer, in short, is yes – at least, it can be. This article will explain the greatest rewards of taking on a doctorate program, with information about job options, Data Science PhD salary ranges, and job growth projections. To learn about all of those as well as survey the other degree options for data scientists, read on.

Advantages of a Data Science PhD

So if a PhD in Data Science isn’t necessary to building a high-earning career in big data, what are the advantages of taking on so many years of schooling? To put it simply, the answer is peerless expertise.

It’s true: one can hold just a master’s degree and still find excellent job opportunities in the data sciences, which is why master’s programs are the most popular path for those in the profession. However, it is unquestionable that a doctorate asserts a higher level of mastery and capability than even master’s degree holders have. If you apply for a job with a PhD on your resume, you’ll be instantly asserting that you are as knowledgeable as they come, which in the case of top-ranking (and top-earning) data science positions is exactly what companies are looking for.

Data Science PhD Programs: How They Work

If you think a doctoral degree in Data Science sounds like the right path for you, it’s worth learning about the specifics of a PhD program. Below is an overview of coursework, anticipated duration, and more.

Coursework and Duration

One of the primary differences between a data science PhD and a master’s program is that a doctorate program culminates in testing and a dissertation, while a master’s program does not. Courses in both programs typically include the following:

• Artificial intelligence
• Data management
• Data mining
• Data visualization
• Machine learning
• Software design

Data science PhDs are known for having an especially intensive orientation toward research, especially in the dissertation component of the work. This can extend the duration of a PhD program by several years. While master’s programs typically take two years if students attend them full-time, a PhD program typically adds two or three years of studying to that timeline.

While many who pursue data science PhDs argue that the insight gained from their extensive dissertation work has paid off in the long run, it’s important to ask yourself if you are going to enjoy making such a deep dive into your studies. If the answer is yes, that’s an excellent reason to proceed with your PhD degree. If not, a master’s program may be the more optimal path for you.

The testing process for data science PhDs is also rigorous, with multiple exams along the way to prove competencies in a variety of subjects. These include oral, written, and practical exams. Earning a PhD asserts by default that you have achieved the mastery needed to pass these tests, which is a powerful assertion of your skill and ability from the get-go.

Finding Your Area of Focus

Like with master’s programs, those pursuing data science degrees typically choose a particular area of focus while in school that will lead directly to their professional specialization. This means it’s crucial to get the lay of the land early so that you’re sure you’re picking a path you’re willing to commit to for a long time. (It’s always possible to acquire deeper insight or even pursue new specialties through certification programs, but it’s recommended to start with one focus that tracks with a degree concentration offered by your school.)

Data Science Salaries

The vast field of data science is proving to be an exceptionally fertile ground to grow a career, no matter what focus area you choose. Indeed, according to the Bureau of Labor Statistics , the median annual pay for data scientists overall is an impressive $100,910 per year, well ahead of most other industries. This is an excellent reason to join this burgeoning field, and it’s been enough to motivate droves of people to pursue data science careers of their own.

If you’re impressed by these numbers, consider this: those statistics describe the overall field of data science, not just the jobs of those who hold PhDs. For these highly advanced professionals, the numbers get much higher. Take a look at the job titles in the next section to see the specific wages of high-ranking data science positions.

While the sudden rush of new candidates seeking data science positions may sound daunting, the job growth statistics for data scientists all but guarantee that high-quality jobs will be available in your area of focus. This is because of the exceptional projected growth rate of data science jobs, which the Bureau of Labor Statistics estimates to be an incredible 36% by 2031.

There are few other industries that offer as significant salaries across the board with so many new positions available.

So why are data scientists so in-demand, and why is the field growing so rapidly? The answer has everything to do with the rise of technology in all aspects of our lives, in particular the way it has transformed how we do business. The rate at which new data technology is evolving means constant adaptations within the world of big data to keep up with it. For example, recent leaps in the field of machine learning (ML) has greatly increased data capturing capacities, leading to a greater need for specialized data analysts who can help process the information quickly.

Careers for Data Science PhDs

One of the biggest questions for prospective data science PhD candidates is this: what will it lead to? Indeed, given the rigor of a data science PhD program, it’s important to think through the investment you’re making.

Below are some of the most common positions data scientist PhDs pursue, along with data scientist PhD salary ranges and more.

High Level Data Scientist

Data scientists often pursue more focused concentrations in the field, but their overall functions include collecting and categorizing data so that it can best be leveraged by organizations. Those who hold doctorate degrees in data science are often available for the highest levels of these jobs, which are roles responsible for important decision making functions, oftentimes communicating with executives and other heads of staff on the key insights they’ve acquired in their field.

As you might expect, these high-ranking data science roles earn significant amounts of money. According to the Bureau of Labor Statistics, data scientists earning in the 90th percentile of the field make an annual mean wage of $167,040.

Business Analyst

Business analysts, also often known as management analysts or management consultants, use advanced algorithms to analyze and interpret data that will later be used to guide business strategy. These can be in-house roles at large organizations or consultant positions who are contracted independently on a project basis. Those who excel at business are especially good candidates to pursue this career path.

According to the Bureau of Labor Statistics, management analysts working at the top of their field (in the 90th percentile) earn an annual mean wage of $163,760.

Database Architects

Database architects play a huge role in a business’ data practices, serving as exactly what their name implies – architects who create the virtual structure in which data is stored and organized. It’s imperative that those who hold these roles be highly advanced in their field, as the strength of a business’ database is a crucial factor in the success of its overall operations.

Database architects are highly valued employees and are compensated accordingly. The Bureau of Labor Statistics reports that the top earning database architects in the US make a mean annual wage of $169,500.

Information Security Analysts

The field of cybersecurity is rapidly expanding as new technologies also introduce new types of cyberattacks to databases. Those with rigorous specialization in information security – such as what is conferred by a data science PhD – are ideal candidates to fill these roles. Indeed, companies are unlikely to hire anyone who is not seriously qualified to do this role, as this person will take responsibility for protecting the business’ most vital documents.

The highest earning (90th percentile) information security analysts are reported by the Bureau of Labor Statistics to make a mean annual salary of $165,920.

Other Data Science Degree Options

Now that you understand the benefits of a data science PhD program, it’s worth taking stock of the other data science degree and certification options that are available. Good news: all of these degree types have online options, many of which are part-time. This means you can attend school from anywhere, with any schedule.

Data Science Bachelor’s Degree

If you would like to pursue a data science PhD but don’t yet hold a bachelor’s degree in any subject, you will first need to complete a bachelor’s program. If you are in this position, it’s recommended to concentrate on data science during undergraduate school so that you can get a rich introduction to the field, even perhaps finding the area of focus where you’d like to plan your career.

It is possible to start a career in data science with just a bachelor’s degree, though most elect to pursue some level of graduate program, as you will enter the field at a higher level of responsibility, with pay to match. To learn more about bachelor’s in data science degree programs, take a look at our guide here .

Data Science Master’s Degree

A master’s in data science is the most popular path for those entering the field of big data. This degree will give you the expertise needed to find competitive jobs with significant responsibilities and the excellent salaries that draw so many to the data science profession. The coursework for a master’s degree is quite similar to a PhD, minus the intensive testing and the dissertation.

There are numerous fantastic Master’s in Data Science programs that can give you the experience and education needed to find a great position in the field. When choosing a master’s program, be sure it offers a concentration in your intended area of specialty. To take a look at the top online master’s programs available near you, visit our guide here .

Data Science Associates Degree

If you do not have a bachelor’s degree and would like to get your professional life started quickly, an associates degree program can give you the training you need to pursue some entry-level jobs in the world of data science. It’s important to note that these programs on their own are unlikely to give you the expertise needed for a high-earning data science career, but they can offer excellent exposure to the field and provide you with your first work experience.

To learn more about associates in data science degree programs, enjoy our guide here, which will give you all the information you need.

Data Science Certificate

An alternative to a long-term degree program, data science certificates can build a particular area of skill or expertise that can help situate you on a particular career path in data science. Some data science professionals who hold advanced degrees also decide to take on certificate programs to expand on their areas of knowledge or add to their list of specializations.

To learn more about data science certificate programs, visit our comprehensive guide here .

Data Science Bootcamps

Data science bootcamps are likely the fastest possible way to enter the data science profession. These courses – which usually have remote and in-person options – give you a literal crash course in a particular arena of data science, typically over a period of about twelve weeks. You will leave with a developed skill set that usually tracks with a particular type of entry-level job.

Like with most data science opportunities outside of graduate programs, these bootcamps are unlikely to set you up with a high-ranking data science careers, but they can be an excellent way to build your fluency in programming languages or other data science skills.

Data science bootcamps are booming, with plenty of options all over the country. Take a look at our guide here to find the program that is right for you.

Finding the Path That’s Right for You

If you’re feeling overwhelmed by the different opportunities available in the data sciences, don’t worry. While there are indeed many options that are suited to candidates with different skill sets, interests, and backgrounds, the good news is that most of these options are good, and are likely to significantly help you start your career.

For a more elaborate overview of the different program options in data science, take a look at our program guide here for a complete comparison.

• Related Programs

PhD in Data Science

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

First-Year Requirements

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

Required Courses:

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

Synthesis project

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

Thesis Advisor and Dissertation Committee

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

Proposal Presentation and Admission to Candidacy

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

Dissertation Defense

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

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Master of Science in Data Science and Statistics

Program description.

Developed by top-tier faculty at The University of Texas at Dallas, the Master of Science in Data Science and Statistics degree program balances applied and theoretical coursework — along with numerous elective courses in specialized subjects and subfields — so that students acquire advanced data analysis training and the kinds of sophisticated skillsets that prepare the way for high-level careers in corporate, nonprofit or governmental organizations.

Students in the Data Science and Statistics program can tailor the curriculum to their interests and career aspirations by choosing one of three tracks:

• Statistics Track : Students who pursue this track receive a solid foundation and deep background in theoretical and applied statistics, making it possible for them to pursue doctoral education or enter the job market.
• Applied Statistics Track : The Applied Statistics Track is typically for students who wish to enter the job market after completing the master’s degree program. This track is particularly popular among students who already have a background in another discipline but would like to build expertise in statistics to enhance their employment opportunities.
• Data Science Track : Students who choose this interdisciplinary track take a balanced mix of courses in Statistics, Computer Science and Mathematics, equipping them with the knowledge and experiences they’ll need to pursue careers related to Big Data.

The Data Science and Statistics master’s program ensures that students gain a broad understanding of the field, apply their knowledge and analytical skills to create effective and novel solutions to practical problems and communicate and work effectively in collaborative environments.

Other benefits include:

• World-Class Faculty : The program is led by faculty of the School of Natural Sciences and Mathematics who are widely cited experts in their respective fields.
• Comprehensive Curriculum : Courses in the Data Science and Statistics master’s program will introduce students to new ideas, technologies, and competencies while preparing them to succeed in competitive, ever-changing industries.
• Facilities : A cluster of buildings and research labs on the northwest side of campus comprise the over 300,000-square-foot space where students can explore the sciences including the famous Natural Sciences and Research Lab – the “mermaid building” and the Sciences Building. Opened in 2020, the 186,000-square-foot Sciences Building is home to state-of-the-art labs for advanced research in mathematical, biological and physical sciences.
• Location : Situated in the greater Dallas region—recently rated by Forbes magazine as the #1 “Best City for Jobs”—UT Dallas provides students with easy access to employers and internship opportunities, not to mention a large and supportive alumni population.

Career Opportunities

With the opportunity to tailor their education to fit their career aspirations, graduates of the Statistics master’s program go on to pursue a wide variety of professional careers in both public and private sectors in roles such as:

• Statisticians
• Biostatisticians
• Data scientists
• Quantitative analysts
• Researchers

The  NSM Career Success Center  is an important resource for students pursuing STEM and healthcare careers. Career professionals are available to provide strategies for mastering job interviews, writing professional cover letters and resumes and connecting with campus recruiters, among other services.

Marketable Skills

Review the marketable skills for this academic program.

Application Deadlines and Requirements

The university  application deadlines apply with the exception that, for the upcoming Fall term, all application materials must be received by December 15 for first-round consideration of scholarships and fellowships. See the  Department of Mathematical Sciences graduate programs website  for additional information. 

Visit the  Apply Now  webpage to begin the application process. 

Contact Information

For more information, contact [email protected]

School of Natural Sciences and Mathematics The University of Texas at Dallas 800 W. Campbell Rd. Richardson, TX 75080-3021

nsm.utdallas.edu

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Data Science Master's Program Online

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

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Online Data Science Graduate Program Overview

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

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

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

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

Data Science Degree Options

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

Data Science Courses

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

Proficiency Exams

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

Program Contacts

James spall.

Benjamin Rodriguez

Tony Johnson

Amanda Harwell

Valeria alfaro, tuition and fees.

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

Why Hopkins?

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

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

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

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

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

Data Science FAQs

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

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

Is a Master’s in Data Science worth it?

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

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

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

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

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Interested in pursuing an advanced career in applied mathematics? Learn which industries and occupations are available to you with JHU EP.

EP Partners with the Panama Canal to Offer a Custom Graduate Education Program

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

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PhD Program

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

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

Course Requirements

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

Research Rotation Program

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

Preliminary Assessment Examination

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

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

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

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

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

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

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

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

Research Qualifying Examination and Advancing to Candidacy

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

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

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

Dissertation Defense Examination and Thesis Requirements

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

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

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

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

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

Special Requirements: Professional Training and Communications

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

Obtaining an MS in Data Science

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

Course Exceptions: Students with MS in Data Science (or similar field)

If a student has already been granted a Master’s degree in Data Science (or a related field, as determined by the Graduate Program Committee) before entering the HDSI PhD program, the student can submit a “Requirement Substitution” petition for up to 2 courses to be substituted by DSC 299 (up to 8 units).

Further leniency may be granted in exceptional cases in which both the student and their faculty advisor must separately appeal to the Graduate Program Committee. It is up to the Graduate Program Committee to decide whether the appeal is rejected or granted in part or in its entirety.

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Ph.D. in Data Science

The ph.d. in data science at smu is distinctive because of its highly interdisciplinary nature..

Most existing Data Science Ph.D. programs are either housed in a single department, such as Statistics, Computer Science, Operations Management or Business Analytics; or they focus on a single disciplinary area of research, such as Business or Medicine.

The program’s core curriculum consists of courses in Computer Science, Operations Management, Statistics, and Data Science, and elective courses go beyond those disciplines to include Mathematics, Finance, Marketing, Education, Psychology, Chemistry, Game Design, Economics, and more. Student and faculty interest will continue to set directions for how the program evolves in the future.

Another distinctive feature are the research rotations that students engage in after having completed 4 semesters of coursework.

The goal of this program is to recognize that data science research can inform nearly every discipline at the university and beyond; and that the future of research and work in data science will not be limited to specific and restricted areas.

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Graduate programs in data science

The University of Minnesota's graduate programs in data science provide a strong foundation in big data and its analysis.

Data science students are able to tap into experts and resources from thirteen areas across the University, including the College of Science and Engineering , the School of Statistics , the Institute of Health Informatics , and the School of Public Health Division of Biostatistics .

Program options

There are currently three graduate program options available:

M.S. in Data Science

Post-Baccalaureate Certificate

Data science graduate minor

Program staff

Yao-Yi Chiang

Director of Graduate Studies 5-191 Keller Hall [email protected]

Allison Small

Senior Graduate Program Coordinator 330F Lind Hall Current students:  [email protected] Prospective students:  [email protected]

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Data Science, Analytics and Engineering, PhD

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Analytics, Big Data, Data Engineering, Data Science, approved for STEM-OPT extension, computing, statistics

Learn to meet the need for data-driven discovery of new knowledge and decision-making, which enhances enterprise performance as well as scientific investigation.

The PhD program in data science, analytics and engineering engages students in fundamental and applied research.

The program's educational objective is to develop each student's ability to perform original research in the development and execution of data-driven methods for solving major societal problems. This includes the ability to identify research needs, adapt existing methods and create new methods as needed. This is accomplished through a rigorous education with research and educational experiences.

Students complete a foundational core covering database management, information assurance, statistical learning and statistical theory before choosing to focus on data analytics or data engineering. The program culminates in the production of a dissertation.

This program may be eligible for an Optional Practical Training extension for up to 24 months. This OPT work authorization period may help international students gain skills and experience in the U.S. Those interested in an OPT extension should review ASU degrees that qualify for the STEM-OPT extension at ASU's International Students and Scholars Center website.

The OPT extension only applies to students on an F-1 visa and does not apply to students completing a degree through ASU Online.

• College/school: Ira A. Fulton Schools of Engineering
• Location: Tempe
• STEM-OPT extension eligible: Yes

84 credit hours, a written comprehensive exam, an oral comprehensive exam, a prospectus and a dissertation

Required Core (12 credit hours) CSE 511 Data Processing at Scale (3) CSE 543 Information Assurance and Security (3) CSE 572 Data Mining (3) or IEE 520 Statistical Learning for Data Mining (3) or EEE 549 Statistical Machine Learning: From Theory to Practice (3) IEE 670 Mathematical Statistics (3) or STP 502 Theory of Statistics II: Inference (3) or EEE 554 Probability and Random Processes (3)

Electives and Additional Research (39 credit hours)

Research (12 credit hours) DSE 792 Research (12)

Other Requirements (9 credit hours) data engineering coursework or data analytics coursework

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

Additional Curriculum Information All students must take qualifying exams covering the required core courses within one year of matriculation into the program.

The dissertation prospectus should be submitted and its oral defense completed no later than one year following completion of the 60th credit hour and also no later than the fourth year in the program.

Students must select coursework from either the data engineering or the data analytics requirements. Students should see the academic unit for the approved course list.

Students cannot take a data engineering or data analytics course and have it meet an elective requirement at the same time. Students need to take a different elective course to reach the number of credit hours required for the program. Other coursework may be used with the approval of the academic unit to fulfill these requirements.

Twelve credit hours of DSE 792 Research are required, and up to 24 credit hours are allowed on the plan of study. Students with research hours in excess of 12 will add these credit hours to their electives and additional research.

Electives include:

• additional DSE 792 Research credit hours (up to 12 credit hours allowed beyond the required 12)
• approved elective courses, of which up to three credit hours of DSE 790: Reading and Conference are permitted, with approval.

When approved by the student's supervisory committee and the Graduate College, this program allows 30 credit hours from a previously awarded master's degree to be used for this degree. If students do not have a previously awarded master's degree, the 30 hours of coursework are to be made up of electives to reach the required 84 credit hours.

Applicants must fulfill the requirements of both the Graduate College and the Ira A. Fulton Schools of Engineering.

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

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

Applicants are required to submit:

• graduate admission application and application fee
• official transcripts
• two letters of recommendation
• letter of intent or written statement
• proof of English proficiency

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

ASU does not accept the GRE® General Test at home edition.

If the student is assigned any deficiency coursework upon admission, those classes must be completed with a grade of "B" (scale is 4.00 = "A") or higher within two semesters of admission to the program. Deficiency courses do not apply to the total credit hours required to complete the degree program.

Deficiency courses are: CSE 205 Object-oriented Programming and Data Structures IEE 380 Probability and Statistics for Engineering Problem Solving MAT 242 Elementary Linear Algebra or MAT 342 Linear Algebra or MAT 343 Applied Linear Algebra MAT 267 Calculus for Engineers III

Program learning outcomes identify what a student will learn or be able to do upon completion of their program. This program has the following program outcomes:

• Apply the tools and methods from industrial statistics, operations research, machine learning, computer science and computer engineering on solving data analytic problems.
• Manage large, heterogeneous data sets for knowledge discovery.
• Conduct research resulting in an original contribution to knowledge in data sciences.

Graduates demonstrate proficiency with existing methodology and significant accomplishment at advancing the state of the art in their chosen area, enabling them to pursue careers in the following fields:

• advanced research

Computer Science and Engineering Program | CTRPT 105 [email protected] 480-965-3199

Engineering

Data science, master of science in data science.

Accelerate your upward career mobility with a master’s degree in data science from the Virginia Commonwealth University (VCU). Jointly operated by the VCU College of Engineering and the Department of Statistical Science and Operations Research in the College of Humanities and Sciences, coursework will encompass theory and application of data science techniques.

Technological advancements and the proliferation of collected data have enabled businesses to use information for more precise strategic decisions. Because of these trends, data science continues to evolve as a distinct discipline.

Our program teaches the advanced knowledge necessary to employ tools and strategies for the analysis and interpretation of complex data to solve real-world problems. Become part of this rapidly growing field with graduate-level education focused on interdisciplinary coursework in statistics, computer science and industry-specific knowledge tailored to your interests. Students seeking to work in a healthcare-related field, for example, would develop industry-specific medical knowledge in order to interpret data effectively.

We are among the best graduate programs in the nation as ranked by U.S. News and World Report . Combined with our industry connections and access to Richmond-area businesses, VCU Engineering is a solid choice for your continuing education.

What you’ll learn

Our 30-credit program can be completed in about two years by full time students. It emphasizes ethical considerations, real-world projects and integration of industry-specific information, focusing on effective data communication and culminating in a two-semester practicum experience serving a client. Your knowledge of business, manufacturing and research will also grow to help advance your career and provide a mechanism for lifelong learning and professional development. Graduates will be prepared to process data with computational methods and advanced algorithms for any business that generates data for decision-making.

Through hands-on opportunities, the advanced computing skills you master will be complemented by an ability to communicate effectively with stakeholders and apply advanced problem solving to computing challenges in cross-disciplinary teams.

The master’s in data science at the VCU College of Engineering will teach you about:

• Data manipulation
• Machine learning algorithms
• Data visualization
• Big data technologies
• Database management 

You will also learn programming languages like: 

Virginia has a significant need for qualified data scientists. According to The Virginia Economic Development Partnership, the state has a high concentration of employers seeking qualified data scientists. In 2020, the recruitment platform Zippia ranked Virginia as a top location for companies actively looking for data scientists.

Etched into the landscape of Richmond, the commonwealth's capitol, The VCU College of Engineering gives students access to a culturally vibrant and diverse city full of potential. We focus on developing close partnerships with public institutions and private businesses in order to give you unique learning and job opportunities.

Our proximity to Virginia policymakers and the importance of supplying industry with capable data scientists will position you to make the most of VCU’s partnerships.

Master’s program students also have access to benefits like:

• Applying classroom knowledge to real-world problems through a two-semester practicum experience serving a client. Working professionals can choose to design their project around existing duties with their employer.
• Cooperation on A.I. data science projects through access to VCU programs employing the technology.
• In-depth learning of statistical analysis theory and application through joint operation with the Department of Statistical Science and Operations Research in the College of Humanities and Sciences.
• Dedicated Career Services department that provides internship and employment opportunities.
• Industry connections through college partnerships with public and private industry.
• Interdisciplinary education to teach collaboration with engineering practitioners outside your field of study.

Study of foundational data science topics and their application is the focus of the master’s program curriculum. Reference the VCU Bulletin for a full list of data science classes. Master’s program courses are 500 level and above (for example, CMSC 502). Below are a few signature courses from the program:

• Advanced Natural Language Processing (CMSC 516) : Learn about recent advances in natural language processing and apply this knowledge to the processing of unstructured text using natural language processing algorithms. You will study word-level, syntactic and semantic processing in addition to topicslike rule-based and statistical methods for creating computer programs that analyze, generate and understand human language; regular expressions and automata; context-free grammars; probabilistic classifiers; and machine learning. Apply your knowledge to real-world problems like spell-checking, Web search, automatic question answering, authorship identification and developing conversational interfaces.
• Introduction to Machine Learning (CMSC 606) : You will gain a foundational understanding of machine learning and recent advances in modern machine learning approaches, like deep learning. Topics covered include: automated differentiation for machine learning, linear models based on maximum likelihood, feedforward deep models and techniques for improving effectiveness and efficiency of training models. The course also covers specialized deep architectures like convolutional networks, generative models and large language models.
• Statistical Data Analysis (STAT 534) : You will become familiar with processing different data types from multiple sources; presentation of complex data; programming, statistical and machine learning algorithms (like maximum likelihood and least squares); design, implementation and analysis of simulation studies; and other topics that reflect the current needs of data scientists.

Data science can be applied to many fields, infusing your career with near limitless opportunity. The VCU College of Engineering master’s in  data science can facilitate career advancement in fields like:

• Computer science
• Manufacturing
• Mathematics
• Pharmaceuticals

With a master’s in data science from the VCU College of Engineering, you will be ready for roles like:

• Business Intelligence Analyst: Help stakeholders create actionable strategies from collected data to increase a company’s efficiency and maximize profits. Parse large amounts of data using effective database querying to produce reports and identify trends that generate actionable business insights.
• Data Scientist: Gather and interpret data to solve a specific problem or model and present data to convey key information to stakeholders.
• Machine Learning Engineer: Build large-scale software systems for processing massive data sets, using these systems to train algorithms capable of learning cognitive tasks and generating useful insights and predictions. Machine learning engineers manage the entire data science pipeline, including sourcing and preparing data, building and training models, and deploying models to production.

With the help of our Career Services team, VCU College of Engineering graduates have many opportunities to network with alumni and industry professionals. Our students work at companies like:

• Black Knight Technology Inc.
• Blue River Technology
• Capital One
• CoStar Group
• Federal Reserve Bank of Richmond
• Micron Technology Inc.
• MITRE Corporation
• NT Concepts

How to apply

VCU offers an online, self-managed application process. See what’s needed to apply for an engineering graduate program and reference our list of Frequently Asked Questions (FAQ) .

Start your application

Statistics & Data Science MS Overview

Program overview.

The M.S. in Statistics and Data Science are terminal degree programs that are designed to prepare individuals for career placement following degree completion. The M.S. does not directly lead to admission to the Statistics Ph.D. program however, those with a strong academic record in statistics and probability theory, and demonstrate promising ability to conduct in-depth research should consider applying to the doctoral program in Statistics.

• Advanced graduate study pathways

Students are expected to live within commuting distance of Stanford campus to ensure significant engagement with the department and faculty. Students are not required to live on-campus (graduate housing), but many find it more conducive due to competitive rental market in neighboring cities and transportation logistics.

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Department orientation for new Stats and DS students

Our mandatory New Student Orientation typically takes place on the Thursday before Autumn Quarter classes begin. I will offer an online meeting in August to explain enrollment and best practices.

University orientation events will be announced in September. These are hosted by the Graduate Life Office (GLO) and known by their acronym, NGSO. Students should plan to arrive on campus one to two weeks before the start of classes for the quarter.

Familiarize yourself with the Academic Calendar to anticipate pending deadlines throughout your time in the program.

2024-25 First Days of Classes and End of Terms

( These dates are subject to change at the discretion of the University.)

• Winter break: December 16 – January 3
• Spring break: March 24 – March 28
• Spring 2024-25: March 31 and June 11
• Summer 2024-25: June 23 and August 16

(updated Feb. 2024)

Length of the program

Students typically finish the degree program in 5 or 6 quarters (excluding summer). With a vast schedule of awesome courses offered during the year, the idea of staying longer is quite appealing to many, but one must weigh the cost of tuition and living expenses of enrolling beyond the degree's required 45 units. 

For those who can manage more than three courses each quarter, enrolling in 11+ units of required courses would allow a student to complete the degree in a shorter period of time (less cost of living/housing expenses).

We advise students to take 1-2 required courses each quarter and an elective course of interest in order to make satisfactory degree progress.

First quarter enrollment example for the Statistics MS:

Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem.

Prerequisites: Integral Calculus of Several Variables (Math 52) and familiarity with infinite series, or equivalent (4 units)

After taking Stats 118, the students should be able to:

• Understand the principles of probability in discrete and continuous cases without measure theoretic detail. Apply counting techniques to solve probability problems in spaces with regularity or symmetry.
• Recognize important distributions in the exponential families and their connections.
• Apply probability models to real-world situations, and recognize famous problems in disguise, like the Birthday problem, the Ballot problem, and the Matching problem.
• Derive expectations and variances of random variables in structured probability spaces.
• Exploit probabilistic symmetries to solve simple problems.
• Understand results such as the Central Limit Theorem and Poisson approximation, and recognize their importance in statistical applications.
• Gain familiarity with more advance topics in probability.

February 2024

Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case-based methods, and data visualization.

Prerequisites: Introductory courses in statistics or probability (e.g., STATS 60 ), linear algebra (e.g.,  MATH 51 ), and computer programming (e.g.,  CS 105 ) (3 units)

After taking STATS 202 the students should be able to:

• Understand the distinction between supervised and unsupervised learning and be able to identify appropriate tools to answer different research questions.
• Become familiar with basic unsupervised procedures including clustering and principal components analysis.
• Become familiar with the following regression and classification algorithms: linear regression, ridge regression, the lasso, logistic regression, linear discriminant analysis, K-nearest neighbors, splines, generalized additive models, tree-based methods, and support vector machines.
• Gain a practical appreciation of the bias-variance tradeoff and apply model selection methods based on cross-validation and bootstrapping to a prediction challenge.
• Analyze a real dataset of moderate size using either R or Python.
• Develop the computational skills for data wrangling, collaboration, and reproducible research.
• Be exposed to other topics in machine learning, such as missing data, prediction using time series and relational data, non-linear dimensionality reduction techniques, web-based data visualizations, anomaly detection, and representation learning.

Linear algebra for applications in science and engineering: orthogonality, projections, spectral theory for symmetric matrices, the singular value decomposition, the QR decomposition, least-squares, the condition number of a matrix, algorithms for solving linear systems. MATH 113 offers a more theoretical treatment of linear algebra. MATH 104 and ENGR 108 cover complementary topics in applied linear algebra. The focus of MATH 104 is on algorithms and concepts; the focus of ENGR 108 is on a few linear algebra concepts, and many applications.

Prerequisites: Intro linear algebra, multivariate calculus ( MATH 51 ) and programming experience on par with CS 106 . (3 units)

Learning objectives: Learn concepts and theorems well enough to formulate real world problems in the language of linear algebra and apply linear algebraic techniques to solve the problems.

First-quarter enrollment example for Stats-Data Science:

• Using the STATS200 course description  to determine if the course content would be redundant material for you, STATS305A (autumn) is recommended instead.
• Consider taking a course under the suggested electives section .

Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: STATS 116 . Please note that students must enroll in one section in addition to the main lecture.

Terms: Aut, Win | Units: 4

This course introduces the fundamental ideas and methods in causal inference, with examples drawn from education, economics, medicine, and digital marketing. Topics include potential outcomes, randomization, observational studies, matching, covariate adjustment, AIPW, heterogeneous treatment effects, instrumental variables, regression discontinuity, and synthetic controls. Prerequisites: basic probability and statistics, familiarity with R.

Terms: Aut | Units: 3

• Analyse a real dataset of moderate size using either R or Python.

M.S. Program advisor assignments

M.S. program advisors assignments will be announced in September. MS advisor assignments are determined over the summer and will be announced in September. To ensure equity and easy distribution rules, students are assigned by their last name (alpha order).

If needed, you'll be able to discuss with your program advisor at the start of the quarter to help you determine the appropriate enrollment before the final study list deadline . Please see the information concerning course placement in the FAQ section below.

• Guidelines and expectations to help establish a professional and respectful academic advising culture

Independent Study (for Elective credit)

While research is not a required component of the degree, the desire to participate in research has been an increasing trend through recent years.

A common request n that has come up in the past few years is regarding the ability to conduct research (for credit), with faculty as independent study/directed reading/independent research.

[More on networking opportunities: Please also browse the information on relevant seminars, student groups and organizations near the bottom of this page.]

While there exists a way to earn credit for independent study/research ( STATS299 ) under the supervision of their program adviser or other Statistics faculty. One must obtain approval from the advisor and provide clearly defined objectives and expected outcome(s) before enrolling in their section.

• Develop a goal statement for what the student hopes to accomplish and the purpose of the independent study. (List your goals by explaining what you hope to gain in terms of knowledge, skills, etc.)
• Select and/or develop learning objectives related to the goal statement. (Using broad statements, list each objective and/or learning activity in the plan.)
• Develop a timetable for implementation of activities and completion of course requirements. (Include what it is that you expect to do and produce and dates for completion and submission. List the types of activities/assignments that the you will be completing by the end of the quarter.)

Other (teaching/research) opportunities

Assistantships.

Campus assistantships are not a guarantee and should not be relied upon to fund your tuition.

TA/RA opportunities within the Statistics dept are designated for the doctoral students as it is a predominant training component of their 5-year program . There is very little chance that either of these opportunities would be available to students outside of the Statistics doctoral program. If an opportunity becomes available, it will be announced to the Statistics graduate student population.

Statistics faculty do not manage the hiring of RA/TA, nor do they have funding to support Masters students.

An assistantship may sometimes be obtained from related departments and schools. It is the student's responsibility to find these opportunities and there are no guarantees. Begin an online search for Course Assistant applications at least three months before the start of the next quarter as departments need to start the hiring process well ahead of time.

!Do not commit to a TA/CA position if you do not have sufficient time to devote to the job.

Some departments or schools hire our M.S. students for hourly research assistant positions. This type of work is not to be confused with full or partial tuition allowance (GAP 7.3) . Before accepting any work, confirm with the hiring department or school whether it is an hourly position, or if it is a type of tuition allowance.

Career prospects

At this time, the department does not publish job placement data of its graduates. Instead, we can provide a general trend of job placements in recent years:

Many students find employment in data science, research analytics, software engineering, program management within the technology sector (operations research), or the finance industry (asset management, acquisitions/mergers, business analytics) as well as various governmental services. The majority of our graduates have found employment in the Bay Area and other major cities around the world.

Stanford Career Education hosts career fairs throughout the year, and there is a tremendous benefit to our campus being situated in Silicon Valley . To participate, students upload their resumes in advance via Handshake, indicate which field/industry and companies they are interested in and industry partners reach out to schedule interviews.

Stanford Career Education also explains Where to Find Jobs & Internships  !

We don't collect data on salaries. This information can be gathered in an online search of job recruitment and financial education sites.

• Data about mathematicians and statisticians from the U.S. Bureau of Labor Statistics

Advanced Graduate Study

The number of students who pursue graduate programs is steadily increasing.

Statistics MS students that feel strongly about entering a 5-year program of research in statistical theory and applications should meet with their program advisor to discuss which programs and schools are an appropriate place and time to apply. With careful planning, students will be able to build a strong program that will make them highly competitive applicants wherever they apply.

Previous years' graduates had been accepted to doctoral programs in Statistics at Columbia, University of Washington, Wharton School, UC Berkeley and UCLA.

Common questions from incoming Statistics Masters students

Stanford does not require a deposit to confirm your acceptance or initiate matriculation.

The student bill for autumn quarter is due in October.

• Student Services: Understand Your Student Bill and Payment System

Student Visa Application in Axess: " Initiate I-20 or DS-2019; Request. " You may do so immediately following accepting in Axess. The I-20 process will begin after submission of required documentation. Bechtel International Center will contact you if they require any further information.

• Review the steps to request/transfer the I-20

Courses that you've taken at your previous institution (or applicable work experience) should be taken into account for the following scenarios:

Statistics students: Autumn Quarter

Probability Theory

• Students returning to school may wish to brush-up on their skills in statistics and probability and should also enroll in STATS 118 - previously STATS116 ; Summary notes courtesy of Professor Dembo.
• Students should be comfortable with probability at the level of STATS116/MATH151 (summary of material) and with real analysis at the level of Math115. Past exposure to stochastic processes is highly recommended.
• A new course STATS221 focuses on topics in discrete probability that are well beyond undergraduate probability, with particular emphasis on random graphs and networks. While at a level and style similar to STATS217, the material of STATS221 is more modern, and do not overlap any of STATS 217/218/219 (nor with the STATS310 sequence or with MATH236).

Theoretical Statistics

• For those familiar with the material in this problem set then STATS200 is recommended (autumn). If the problem set poses a struggle, then we suggest starting with STATS118
• Using the STATS200 course description to determine if the course content would be redundant material for you, STATS305A (autumn) is recommended instead.

Linear Algebra

• MATH104 Applied Matrix Theory
• Choosing between MATH104 & 113 (outline courtesy of the Math Department)
• CME364A Convex Optimization I
• CME302 Numerical Linear Algebra

Programming

• For those with some programming experience (introduction to programming/intermediate programming), consider one of the following:
• CS106B Programming Abstractions (A, W, S, Su)
• CS106AX Programming Methodologies in JavaScript and Python (Accelerated)
• CS 107 Computer Organization & Systems

ExploreCourses , the university's academic database, can be searched using the program code (e.g., STATS116, CS106, MATH104, etc.) or by subject. Please pay special attention to the quarter(s) that courses will be offered, as not all courses are offered at all times, and some are not offered more than once per year. The course schedule is updated in August each year; ExploreCourses will redirect to the new database when it goes live.

For Autumn 2024-25, students whose matriculation status is CLEAR will be able to enroll in courses early September(9:00 PM Pacific time ).

• U pdate your address .

Axess enrollment allows students to plan their quarter starting:

• August 28 ( Mon ) Planning opens for undergraduate, graduate, and Graduate School of Business (GSB) students.

Stanford's course registration system allows students to enroll in courses with conflicting meeting patterns. While this is allowed at the start of the quarter (first three weeks), it is generally discouraged due to time constraints and expectations; the course should be dropped by the end of Week 3 ( Final Study List deadline ).

Instructors will not accommodate a student whose classes have conflicting end-quarter exams.

Resources from Bechtel International Center

New International Students:

• Release of Enrollment Holds: All F and J students are required to bring their passport, I-20 or DS-2019 , and a recent print out/screen shot on digital device of your admissions I-94 electronic record to one of the Maintaining Your Legal Status workshops in order to have your enrollment hold removed. The hold will be removed within 24 hours.
• Prior to attending this workshop, you must update your SEVIS (U.S.) address and U.S. phone number on Axess. Instructions on how to update your address can be found on the Bechtel website: How to update your address

F-1 Students Who Attended Other U.S. Schools:

• All F-1 transfer students must complete the check-in process within 15 days of the program start date. This can only be done after you have updated your SEVIS (U.S.) address field and U.S. phone number in Axess and have attended one of the Maintaining Your Legal Status workshops at Bechtel .
• After these two requirements have been met you will receive an e-mail instructing you to come to Bechtel to pick up your Transfer Completed I-20.

Most students report that they were almost always able to enroll in the courses they needed each quarter. It is recommended that students make themselves available at the time that enrollment opens (9 pm Pacific).

If enrollment is closed and the course does not have a waiting list, students should contact the instructor to communicate their desire or need to take the course. Explain that the course is needed for your degree and confirm that you will not be enrolled in a course with a conflicting meeting pattern or final exam. Where possible the instructor will try to accommodate your request.

In some instances, be sure to carefully read the course description for enrollment steps. Some courses require the student to submit an application.

Minimum units allowed during the regular academic year each quarter is 8 units which is considered full-time enrollment. Most students enroll in 8 units each quarter and many are able to enroll 10 units.

A few students are able to manage 11-15 units each quarter to finish their degree in less time.

If you need to enroll part-time (minimum 3 units), check your eligibility for Part-Time Enrollment in the Graduate Academic Policy and Procedures guide.

Most students take 5-6 quarters to finish their degrees, not including summer quarter. Some students can finish it in as few as 4 quarters, many choose to stay for 6 quarters (A,W,S) over two academic years.

Some students choose to take fewer required courses each quarter due to a more taxing course-load or due to outside commitments. They may also want to take other courses outside of the degree's requirements.

A thesis is not required for the Master's degree. Those who are interested in pursuing a thesis project, finding the right faculty is vital to starting any level of research. It takes considerable time and planning before permission is granted. Those who are successful then enroll in the Statistics STATS299 Independent Study course (up to 3 units) under the section number of their M.S. program advisor (or other faculty advisor).

As is stated in the admission offer letter, completing the M.S. degree in Statistics at Stanford is not a bridge to the Statistics Ph.D. at Stanford.

In addition to their faculty advisor, many students feel comfortable approaching and speaking with faculty and instructors. Bear in mind, Stanford faculty are often committed to various ongoing research projects; it can be difficult to connect or network with Stanford faculty and researchers without learning about what they do. We suggest attending any of the myriad seminars across campus that are of interest to you; which will open up an unparalleled domain of networking possibilities where you can learn about the diverse world of Stanford research.

Most first-year students choose to live on campus in graduate housing . However, there are also many students who prefer to live off-campus in the surrounding Bay Area .

Graduate students are guaranteed campus housing their first year.

Graduate Housing Lottery The Graduate Housing Lottery is the process by which new and continuing graduate students, as well as non-matriculated students such as post-docs, apply for 2022-23 and summer 2022 housing. Students will have the opportunity to rank their desired housing options and form groups. Housing is available for single students, couples, and families.

• Graduate Housing Lottery Website includes information about housing options and the Lottery
• Housing Lottery explained
• Lottery FAQ
• 2023-24 Graduate Housing Brochure
• Other campus housing options via RDE Community Housing

The campus housing application is available via Axess in April:

• Go to the Student drop-down menu and select Housing and Dining
• Select Apply for Housing
• Follow the instructions to submit your application.

R&DE Student Housing Assignments will be hosting a series of webinars covering the Graduate Housing Lottery:

• April 28 from 4-5 pm
• April 5: Application portal opens.
• May 3: Applications due for summer 2022 and 2022-23
• May 27: Assignments announced for summer 2022 and 2022-23

On-campus housing:

• Schedule your move-in date (campus residents)
• What to bring (and what not to bring)
• Useful resource links for international students

New to California?

• Emergency readiness in campus housing
• Earthquake information from Stanford CardinalReady
• Be Quake Safe at Stanford

If these items aren't already in your suitcase, be sure to purchase them before the end of autumn quarter!

• Reusable water bottles (at least 2)
• Reusable thermos (for Statistics coffee and espresso to-go!)
• An umbrella (or a big rain poncho to drape over yourself and your backpack)
• A waterproof jacket
• Comfortable walking shoes

If you plan to bring/purchase a bike (scooter/skateboard)

• 2 sturdy locks
• Bicycle repair kit
• A rechargeable light for your handlebar
• Wear reflective clothing at dusk and night
• Sign up for a bike safety class

Bike Information and Resources for New Students

Bike Safety repair stations throughout Stanford's campus

As on most college campuses, Stanford students predominantly rely on a bike to get around. For those without access to a car, Caltrain, VTA or SamTrans provide more than adequately fulfill transportation needs up and down the peninsula (including airport shuttles). In addition, Stanford's free Marguerite shuttle service provides access to the campus to/from surrounding cities (Menlo Park, Palo Alto, parts of Redwood City) and to and from the Caltrain stations in Menlo Park and Palo Alto. Bay Area commute-traffic congestion rivals that of other major cities, which means driving on the peninsula to Stanford is impacted during peak hours.

• Marguerite was the name of the horse-drawn bus run by Jasper Paulsen in the earliest days of the university.
• Free transit options and incentive programs
• Parking permits
• Bay Area traffic information via 511.org

Finding things to do after you relocate to Stanford

• Campus events calendar
• Science and Engineering events calendar
• Graduate Students campus community center
• Math & Science Library
• Tresidder Memorial Union
• Stanford Arts Map
• Cantor Center for the Arts
• Virtual Tours   
• Stanford Magazine
• The Six Fifty.com
• Visit Stanford's Office of Student Engagement .
• Learn about student organizations in the School of Engineering .
• Civic opportunities are listed with the Haas Center for Public Service .
• Interested in art, design, music or the performing arts? Find your niche within Stanford Arts Groups .

Academic Resources and Support

There are many resources available across Stanford. Masters students most often take advantage of the workshops and career fairs sponsored by BEAM and similar events offered by the School of Engineering's Xtend Career Forum for the Data Science program.

• The Graduate Life Office (GLO) hosts New Graduate Student Orientation (NGSO) Week.
• Professional Development
• Interdisciplinary Learning
• Academic resources abound at the Office of Accessible Education .
• The Stanford Daily offers a curated list of various campus resources.

The statistics courses taught by the Department typically require some knowledge of the programming language R . Many courses rely on Python coding.

• List of Software available on Farmshare (a shared computing environment)

Recommended resources:

• Software for Data Analysis by John Chambers
• Hands-On Programming with R by G. Grolemund
• R Packages by H. Wickham

Yes: the Statistics Seminar is offered by the department, and the Probability Seminar is offered jointly with Stanford Math . Additionally, many other departments hold seminars that are open to students of all disciplines:

• Biomedical Data Science
• CS Computer Forum
• GSB Organizational Behavior

Stanford student groups that may be of interest are:

• Stats for Social Good
• CS for Social Good

International students who are employed off-campus are subject to the policies outlined by Bechtel International Center concerning Curricular Practical Training .

In order to be eligible to be hired, international students (F-1) MUST file for CPT via BechtelConnect and enroll in the course STATS298 Industrial Research for Statisticians .

Please follow the Statistics department protocol for CPT before starting the application .

Getting to know Stanford

Stanford Celebrates 125 Years

• Stanford Stories No. 25: Early Stanford Women
• Historical Timelines
• Images of Main Quad Then and Now

Notification/Obligation to Read Email For many University communications, email to a student's Stanford email account is the official form of notification to the student, and emails sent by University officials to such email addresses will be presumed to have been received and read by the student. Emails and forms delivered through a SUNet account by a student to the University may likewise constitute formal communication, with the use of this password-protected account constituting the student's electronic signature. Read the entire policy pertaining to University Communication with Students.

Summer quarter distance-learning enrollment option (NDO student)

Master’s degree students who will matriculate autumn quarter have the option to take statistics courses online via the Stanford Center for Professional Development (SCPD) before arriving on campus. Registration and enrollment is administered through SCPD (NDO student status).

Matriculation will proceed as usual with autumn quarter start.

If you have any questions about course placement for summer quarter, please email Caroline Gates ( cgates [at] stanford.edu (cgates[at]stanford[dot]edu) ), your Student Services Officer in Statistics.

• International student visas will be processed over summer with a start date in September.
• CS dept policy: Students are obligated to enroll in the maximum unit for the CS course as a NDO student.

Summer tuition: 1/10th the full-time tuition cost + SCPD fees

Prior to Graduate Admissions matriculating your student record for autumn, Statistics and Data Science students may enroll in one or two courses online:

• STATS 117 and STATS 118 Theory of Probability I & II (3 & 4 units respectively)
• Essentials of Stochastic Processes by Rick Durrett
• An Introduction to Statistical Learning with Applications in R by G. James, D, Witten, T. Hastie and R. Tibshirani
• The Art and Science of Java by Eric Roberts
• Programming Abstractions in C by Eric Roberts

Ph.D. Specialization in Data Science

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

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

Applied Mathematics Doctoral Program

Computer Science Doctoral Program

Decision, Risk, and Operations (DRO) Program

Electrical Engineering Doctoral Program

Industrial Engineering and Operations Research Doctoral Program

Statistics Doctoral Program

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

Specialization Requirements

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

Information Request Form

Ph.d. specialization committee.

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

Richard A. Davis

• Faculty of Arts and Sciences Howard Levene Professor of Statistics

Vineet Goyal

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

Garud N. Iyengar

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

Gail Kaiser

Rocco a. servedio, clifford stein.

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

John Wright

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

• People Directory
• Safety at UD

Statistics Data Science: Ph.D.

• Chair's Welcome Statement
• Food and Agribusiness Marketing and Management
• Environmental and Resource Economics
• Statistics 4+1 (B.S. + M.S.)
• Agricultural and Resource Economics MS
• Applied Statistics Online MS
• Statistics MS
• Our Faculty & Staff
• Center for Experimental and Applied Economics
• Hemp Economic Marketing and Policy
• Additional Resources
• Publications

How to apply >

Our Ph.D. in Statistics Data Science program offers you the opportunity to hone your skills in mathematical reasoning, statistical modeling, computation, and methodology development.

Through this new doctoral program, you will gain a thorough understanding of probability and statistics as well as machine learning methods. You’ll apply statistical methods and theory to real-world data challenges in an interdisciplinary manner. This program will expose you to cutting-edge research and developments in statistics, machine learning, artificial intelligence and data sense, preparing you for statistics and data science careers in academia, the public sector and industry.

Jump to:   Admission & Degree Requirements   |  Application Deadlines   |  Research Areas   |  Faculty  

Admission & Degree Requirements

Admission to this program is highly competitive and selective. We require you to submit the following with your application. 

All transcripts from undergraduate and graduate (if applicable) institutions. 

Three letters of recommendation.

Personal statement: Include research interests;do not exceed three pages.

GRE General test score (required but can be waived for students currently enrolled in or have already earned the MS in Statistics degree in UD)

GRE subject test in Mathematics or other STEM fields (optional).

Language scores (for international students whose native language is not English, and who have not received a degree at a U.S. college or university). A score of 100 or higher on the Test of English as a Foreign Language (TOEFL), or equivalently 7.5 or higher on the International English Language Testing System (IELTS). 

A department graduate committee will decide who is admitted to the program in compliance with University policies and procedures. The committee reserves the right to interview the applicants.

Students with an MS degree in Statistics or related fields are eligible for a 4-year accelerated track with a reduced course load. Eligibility is determined by the admission committee.

Degree Requirements

You must have, or expect to have a bachelor’s degree or higher in statistics, mathematics or a related field from an accredited college of university, by the date of admission.

Ready to Apply?

Apply now >, view course and exam requirements >, email the program director >.

* Disclaimer: The customized GPT is an experimental tool designed to provide real-time answers based on the official curriculum and commonly asked questions. GPT-generated answers may not always be accurate. Please verify all information through the official University of Delaware website.

Application deadlines

Regular admission is for each fall semester. Applicants must submit their application via the online link no later than February 1 .

Research Areas

The Statistics faculty within the department engage in a broad range of research topics. Our expertise spans classical statistical problems, such as hypothesis testing, high-dimensional data analysis, dimension reduction, time-series analysis, and nonparametric statistics, as well as contemporary topics, including network modeling, graph learning, neural networks, computational statistics, and optimization. Additionally, our faculty are actively involved in data-driven research applications across diverse fields, such as large language models, image data analysis, financial forecasting, health sciences, biology, and animal science.

The program also offers students the flexibility to pursue research in collaboration with our affiliated faculty or any other University of Delaware faculty whose work is closely aligned with statistics and data science. This interdisciplinary approach provides a unique opportunity for students to tailor their research experience to their academic and professional interests.

Core Faculty  

Dr. Shanshan Ding

Dr. Wei Qian

Dr. Jing Qiu

Dr. Cencheng Shen

Dr. Peng Zhao

Affiliated faculty

Dr. Austin Brockmeier

Dr. Rahmat Beheshiti

Dr. Yin Bao

Dr. Jeff Buler

Dr. Kyle Davis

Dr. Vu Dinh

Dr. David Hong

Dr. Mokshay Madiman

Dr. Xi Peng

Dr. Guangmo (Amo) Tong

Dr. Xu Yuan

College of Agriculture & Natural Resources

Academic Departments

• Animal & Food Sciences
• Applied Economics & Statistics
• Entomology & Wildlife Ecology
• Plant & Soil Sciences

Additional Links

• Faculty & Staff Resources

531 South College Avenue Newark, DE 19716 (302) 831-2501

• Personalise your journey
• Contact QUT Contact QUT

Graduate Certificate in Data Science

Develop the skills to analyse data, create visualisations, and apply statistical and machine learning models. Prepare for a career in data science and analytics, or specialise further in the Master of Data Science program.

• Gardens Point
• 1 year full-time
• 0.5 years part-time

In this course you will

• Gain expertise in a highly sought-after capability applicable across various industries, including translating data into insights and intelligence to drive change and make crucial decisions. 
• Learn how to design problem analyses, generate data visualisations, develop and validate appropriate statistical and machine learning models, and incorporate analyses into solution pipelines. 
• Access expert academics and leading researchers who apply data science and data analytics to real-world challenges and have strong industry connections worldwide. 

Applications are open for 2025

Secure your spot now.

Mark your calendar, as the on-time application closing date for the upcoming semester is fast approaching.

Why choose this course?

When you choose our Graduate Certificate in Data Science course, you'll be setting yourself up for success in the dynamic and rapidly growing field of data science. Our curriculum is designed to equip you with the essential knowledge and skills to excel in this competitive industry. By choosing this course, you are investing in a solid foundation that will prepare you for a fulfilling career or further postgraduate studies in data science.

Here are just a few reasons why this course is the perfect choice for you:

Comprehensive learning outcomes

Our course covers a wide range of learning outcomes, including demonstrating general knowledge of data science principles, employing appropriate data science methods, applying problem-solving approaches, working independently and collaboratively in teams, communicating professionally, appraising personal values and performance, and reflecting on social and ethical data science issues.

Practical application

You'll have the opportunity to apply what you learn to real-world scenarios, enabling you to derive insights from data to support decision-making and to design and execute data science solutions.

Professional development

Our course will help you develop technical skills and appraise your personal values, attitudes, and performance in your continuing professional development.

Emphasis on collaboration and communication

You'll gain experience in working both independently and collaboratively in teams, as well as communicating professionally in oral and written form for diverse purposes and audiences.

Ethical considerations

You will have the chance to reflect on social and ethical data science issues, including how these relate to First Nations Australians, giving you a well-rounded understanding of the ethical implications of data science.

Real-world learning

This course is specifically designed to meet industry needs. It combines expertise in statistics, computer science, and business process management to provide real-world learning opportunities. 

Gain experience applying your analytical skills to complex problem domains and applying high-order thinking strategies within data-rich contexts through synthesising multiple sources of information. 

Explore this course

What to expect.

This course will prepare you for a future-focused career in the fast-paced, ever-changing world of data analytics. Its collaborative curriculum across disciplines will teach theories and methods and allow you to apply that knowledge to predict, forecast, visualise, and make decisions in various applied areas. 

You'll be introduced to the foundations of data science and analytics and have the opportunity to further your knowledge in additional fields such as statistics for data science, machine learning, programming, rapid web development, and data analytics for strategic decision makers.

Careers and outcomes

After completing the program, you will have the professional skills to apply data science in a wide range of industries. This certificate can serve as a stepping stone to the  Master of Data Science , where you can focus on data analytics, data systems development, or data-driven decision-making.

Possible careers

• Data analyst
• Data scientist
• Data systems developer
• Data-driven decision maker

Details and units

Course structure, requirements, entry requirements.

You must have one of:

• A recognised bachelor degree (or higher qualification) in any discipline; or
• A recognised diploma (or higher qualification) in any discipline followed by at least two years full-time (or equivalent) professional work experience in the fields of biomedicine, information technology, or mathematics; or
• At least five years full-time (or equivalent) professional work experience in the fields of biomedicine, information technology, or mathematics.

Minimum academic requirements

Entry requirement.

You must have a recognised bachelor degree (or higher qualification) in any discipline.

Minimum English language requirements

Select the country where you completed your studies to see a guide on meeting QUT’s English language requirements.

Your scores and prior qualifications in English-speaking countries are considered. Approved English-speaking countries are Australia, Canada, England, Ireland, New Zealand, Scotland, United States of America and Wales.

If your country or qualification is not listed, you can still apply for this course and we will assess your eligibility.

UTS (University of Technology Sydney) Insearch - Academic English

English program.

Academic English 5 (AE5) program with a final overall grade of PASS or higher completed within one year of starting at QUT.

Bachelor studies

Higher education.

A completed bachelor degree (or higher) with a minimum of 1 year full-time studies with a passing grade point average from RMIT Vietnam, completed within five years of starting at QUT.

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average from RMIT Vietnam, completed within two years of starting at QUT.

Bachelor or Higher

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average from a recognised English institution, with all prior schooling/studies in an approved English speaking country.<br> <br>Bachelor or higher degree (minimum of 1 year full-time on-campus studies) with a passing grade point average from a recognised English institution. These studies must have been completed within five years of starting at QUT, if prior schooling/studies were studied in a non-English Speaking country.

Bachelor degree or higher with an overall passing grade point average from a recognised Australian institution (the duration of studies must be 1 year or more full-time, studied on-campus), with all prior schooling/studies in an approved English speaking country. <br> <br>Bachelor degree or higher with an overall passing grade point average from a recognised Australian institution (the duration of studies must be 1 year or more full-time, studied on-campus). These studies must have been completed within five years of starting at QUT where prior schooling/studies were studied in a non- English Speaking country.

QUT University Certificate in Tertiary Preparation (UCTP) (QC06)

Achieve passing grades in QCD111 Communication 1, QCD211 Communication 2 and QCS300 Introduction to the Language of Research; and obtain an overall grade average of 4 out of 7 or higher across these units.

QUT English for Academic Purposes (EAP)

QC36 English for Academic Purposes (EAP) 2 Standard or QC37 English for Academic Purposes (EAP) 2 Extended with 65% completed within one year of starting this course at QUT.

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average from a recognised South African institution, with all prior schooling/studies in an approved English speaking country.<br> <br>Bachelor or higher degree (minimum of 1 year full-time oncampus studies) with a passing grade point average from a recognised South African institution. These studies must have been completed within five years of starting at QUT, if prior schooling/studies were studied in a non-English Speaking country.

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average from a recognised German institution, with all prior schooling/studies in Germany.

DAAD English Language Certificate

B2 (4 star in all bands) within five years of starting at QUT.

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average from a recognised Swedish institution, with all prior schooling/studies in Sweden.

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average from a recognised Norwegian institution, with all prior schooling/studies in Norway.

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average from a recognised Danish institution, with all prior schooling/studies in Denmark. Diploma Supplement or an official letter from home institution stating English as the language of instruction.

Bachelor or higher degree from a recognised Indian institution completed within five years of starting at QUT, and 65% in the High School English Core subject awarded by CISCE or CBSE.

Bachelor or higher degree (minimum of 1 year full-time on-campus studies) at a recognised Hong Kong institution with: <br>a passing grade point average and these studies must have been completed within five years of starting at QUT; and<br>an official language of instruction letter is required if the academic transcripts doesn't clearly state English is the Language of Instruction; and<br>evidence of minimum HKDSE Level 2 overall in English Language.

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average from a recognised institution in Netherlands completed within five years of starting at QUT, with all prior schooling/studies in Netherlands. Diploma Supplement or an official letter from home institution stating English as the language of instruction.

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average from a recognised Finnish institution completed within five years of starting at QUT, and a pass in English subject from Finnish High School. Diploma Supplement or an official letter from home institution stating English as the language of instruction.

Bachelor or higher

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average at a recognised USA institution, with all prior schooling/studies in an approved English speaking country.<br> <br>Bachelor or higher degree (minimum of 1 year full-time oncampus studies) with a passing grade point average at a recognised USA institution. These studies must have been completed within five years of starting at QUT, if prior schooling/studies were studied in a non-English Speaking country.

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average at a recognised Singapore institution, with all prior schooling/studies in Singapore.<br> <br>Bachelor or higher degree (minimum of 1 year full-time on-campus studies) with a passing grade point average at a recognised Singapore institution. These studies must have been completed within five years of starting at QUT, if prior schooling/studies were studied in a non-English Speaking country.

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average from a recognised Canadian institution, with all prior schooling/studies in an approved English speaking country.<br> <br>Bachelor or higher degree (minimum of 1 year full-time on-campus studies) with a passing grade point average from a recognised Canadian institution. These studies must have been completed within five years of starting at QUT, if all prior schooling/studies were studied in a non- English Speaking country.

Bachelor or higher degree (minimum volume of 2 year full time oncampus studies) at a recognised Malaysian institution with<br>a passing grade point average and these studies must have been completed within five years of commencement at QUT; and<br>an official language of instruction letter is required if the academic transcripts doesn't clearly state English is the Language of Instruction; and<br>evidence of a pass in the English subject in a recongised high school qualification: SPM, STPM, UEC, A levels and O levels or equivalent.

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average from a recognised Irish institution, with all prior schooling/studies in an approved English speaking country.<br> <br>Bachelor or higher degree (minimum of 1 year full-time oncampus studies) with a passing grade point average from a recognised Irish institution. These studies must have been completed within five years of starting at QUT, if prior schooling/studies were studied in non-English Speaking country.

Bachelor degree with a minimum course GPA of 3.0 on a 4 point scale from one of the following universities completed within five years of starting at QUT:<br>Assumption University<br>Thammasat University<br>Chulalongkorn University<br>Mahidol University<br> <br>You must provide a letter from the institution confirming that English was the language of instruction.

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average from a recognised Iceland institution completed within five years of starting at QUT, and a pass in the English subject from Studentsprof. Diploma Supplement or an official letter from home institution stating English as the language of instruction.

Bachelor degree or higher with an overall passing grade point average from a recognised Papua New Guinean institution (the duration of studies must be 1 year or more full-time, studied on-campus) within the last five years.

Bachelor or higher degree (minimum of 1 year full-time studies) with a passing grade point average from a recognised New Zealand institution, with all prior schooling/studies in an approved English speaking country.<br> <br>Bachelor or higher degree (minimum of 1 year full-time on-campus studies) with a passing grade point average from a recognised New Zealand institution. These studies must have been completed within five years of starting at QUT, if prior schooling/studies were studied in non-English Speaking country.

We accept English language proficiency scores from the following tests undertaken in a secure test centre. Tests must be taken no more than 2 years prior to the QUT course commencement.

Don't have the English language score you need? We can help!

We offer English language programs to improve your English and help you gain entry to this course.

When you apply for this course, we will recommend which English course you should enrol in.

Your actual fees may vary depending on which units you choose. We review fees annually, and they may be subject to increases.

2024: CSP $4,200 per year full-time (48 credit points)

2024: $19,100 per year full-time (48 credit points)

Student services and amenities fees

You may need to pay student services and amenities (SA) fees as part of your course costs.

Find out more about postgraduate course fees

HECS-HELP: loans to help you pay for your course fees

You may not have to pay anything upfront if you're eligible for a HECS-HELP loan.

Find out more about government loans

Scholarships

You can apply for scholarships to help you with study and living costs.

Browse all scholarships

International Merit Scholarship

Qut real world international scholarship.

A scholarship to cover tuition fees, with eligibility based on your prior academic achievements.

You may also be eligible for

Centrelink payments

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• 1 year part-time

Graduate Certificate in Information Technology

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Graduate Diploma in Information Technology

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Master of Information Technology

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Master of Philosophy

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• 80.00 is the ATAR/selection rank threshold for Bachelor of Biomedical Science/Master of Data Analytics

Bachelor of Business/Bachelor of Data Science

• 8 years part-time
• 84.00 is the ATAR/selection rank threshold for Bachelor of Business/Bachelor of Data Science

Bachelor of Business/Master of Data Analytics

• 80.00 is the ATAR/selection rank threshold for Bachelor of Business/Master of Data Analytics

Bachelor of Creative Industries/Bachelor of Human Services

• 70.00 is the ATAR/selection rank threshold for Bachelor of Creative Industries/Bachelor of Human Services

Bachelor of Data Science

• 3 years full-time
• 6 years part-time
• 84.00 is the ATAR/selection rank threshold for Bachelor of Data Science

Bachelor of Data Science/Bachelor of Laws (Honours)

• 5.5 years full-time
• 84.00 is the ATAR/selection rank threshold for Bachelor of Data Science/Bachelor of Laws (Honours)

Bachelor of Data Science/Bachelor of Property Economics

• 84.00 is the ATAR/selection rank threshold for Bachelor of Data Science/Bachelor of Property Economics

Bachelor of Engineering (Honours)/Master of Data Analytics

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• 90.00 is the ATAR/selection rank threshold for Bachelor of Engineering (Honours)/Master of Data Analytics

Bachelor of Information Technology/Master of Data Analytics

• 80.00 is the ATAR/selection rank threshold for Bachelor of Information Technology/Master of Data Analytics

Bachelor of Mathematics (Statistics)

• 84.00 is the ATAR/selection rank threshold for Bachelor of Mathematics (Statistics)

Bachelor of Mathematics/Master of Data Analytics

• 84.00 is the ATAR/selection rank threshold for Bachelor of Mathematics/Master of Data Analytics

Bachelor of Science/Bachelor of Data Science

• 84.00 is the ATAR/selection rank threshold for Bachelor of Science/Bachelor of Data Science

How to apply

Follow our step-by-step applying guide to make sure your application is complete, giving you the best chance of getting in.

Ready to apply?

If you're ready for the next step, apply online today.

If you're ready for the next step, apply online today or contact our MBA Program Manager +61 468 575 146 or [email protected]

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