phd after ms in data science

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.

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DEPARTMENT OF STATISTICS AND DATA SCIENCE

Phd program, phd program overview.

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

Requirements

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

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

In order to receive a doctoral degree, students must:

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

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

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

• Meet the department minimum residency requirement of 2 years
• STAT 344-0 Statistical Computing
• STAT 350-0 Regression Analysis
• STAT 353-0 Advanced Regression
• STAT 415-0 I ntroduction to Machine Learning
• STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3
• STAT 430-1, 2 Probability for Statistical Inference 1, 2
• STAT 440 Applied Stochastic Processes for Statistics
• STAT 457-0 Applied Bayesian Inference

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

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

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

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

Optional MS degree en route to PhD

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

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

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

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.

Master's in Data Science Leadership

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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.

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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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  
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• Geographic Information Systems
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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)
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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

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

Name of Degree: Doctor of Philosophy in Information Studies

Credits: 60

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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

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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

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

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

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University of Washington

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

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

Worcester Polytechnic Institute

Worcester, massachusetts.

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PhD 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.

Master’s in Data Science: Program Overview

On this page: Shaping the Future of Data Science •   36 Credits | 2 Years Full-Time Study •   Scholarships

Shaping the Future of Data Science

The Master of Science in Data Science is a highly selective program designed for students with a strong foundation in mathematics, computer science, and applied statistics. Our curriculum focuses on developing innovative methods and cutting-edge techniques to tackle the most pressing challenges in data science.

In today’s data-driven world, organizations across industries are grappling with an unprecedented volume and velocity of data.

The demand for skilled professionals who can harness the power of this data to drive insights, innovation, and impact has never been greater.

Our M.S. in Data Science program equips you with the advanced knowledge, practical skills, and interdisciplinary perspective needed to thrive in this fast-paced and constantly evolving field. Through a rigorous curriculum, hands-on projects, and close collaborations with world-class faculty and industry partners, you’ll become a leader in shaping the future of data science.

36 Credits | 2 Years Full-Time Study

The curriculum comprises 36 credits and offers several ways to structure the program:

• The Industry Concentration , which allows you to pursue a specialization aligned with your career goals and requires industry-targeted coursework, a Practical Training experience, and an internship.
• The Biomedical Informatics (Medical School) Track, which has a  biomedicine-based capstone project that is completed with a biomedicine mentor. 
• Big Data 
• Mathematics and Data
• Natural Language Processing

Both full-time and part-time students must complete all 36 credits within 5 years of enrolling at NYU as an MS in Data Science candidate.

Scholarships

NYU offers a limited number of competitive tuition scholarships to selected students admitted to the program. These scholarships cover a portion of the tuition costs for up to two years. All applicants for admission are automatically considered for these awards.

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Is a PhD in Data Science Worth It?

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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.

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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

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Benjamin Rodriguez

Tony Johnson

Amanda Harwell

Valeria alfaro, tuition and fees.

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

Why Hopkins?

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

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

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

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

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

Data Science FAQs

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

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

Is a Master’s in Data Science worth it?

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

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

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

Academic Calendar

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

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What You Can Do with a Master’s in Data Science

A master’s degree in data science offers an interesting career with rapidly expanding growth.

Pamela Reynolds

Data science careers are booming as employers seek data analysts, big data engineers, and data architects to help them manage huge data streams.

We live in a world of data. 

Although we associate the term “data” with the tech sector and firms like Microsoft, Google, and Amazon, the truth is that data and the data scientists who manage it all are everywhere . Firms in such diverse sectors as retail, finance, telecommunications, healthcare, and agriculture are looking for people who can help them collect, filter, interpret, and analyze the data that keeps their businesses running.

But what does data science really involve, and what do data scientists actually do ? If you pursue a master’s degree in data science, what kind of career opportunities can you expect? In this blog post, we clear away the mystery of this career path, exploring why getting a master’s degree in data science might be the one best thing you can do to boost your career. 

What is Data Science?

Data science is the study of data to extract meaningful insights for business . 

Data scientists are part mathematicians and part computer scientists. They handle, organize, and interpret massive volumes of information with the goal of discerning patterns. They usually work on behalf of organizations where their data insights are used to develop business strategy. As professionals with a foot both in IT and business, data scientists have a unique perspective on what makes things tick. 

Data scientists tap into a wide range of skills that include math, statistics, computer programming, and machine learning — always looking for unseen patterns in the numbers they collect. They construct complex  machine learning algorithms to build predictive models and  artificial intelligence (AI)  systems that can generate the insights  that analysts and business managers will ultimately use to improve their businesses.  

As they take a project from start to finish, data scientists traverse several stages of what is known as the data science lifecycle. 

First, they define the problem. Then, they gather data, filter it, and merge different datasets. They analyze the data, then ultimately build models using algorithms to create a machine learning model. Once a model is built, data scientists test it to make sure it is solving the problem it was designed to address. 

The final step is to communicate with stakeholders, often through data visualization charts and graphs. Stakeholders can then make informed decisions about how to take their business to the next level. 

Skills Needed to be a Successful Data Scientist 

Great problem-solving abilities and communication skills are the fundamental qualities needed in this job, but there are several other skills that are absolutely critical to success. 

Programming Languages

A firm grasp of programming languages is key to manipulate and organize data. Typical languages data scientists use include Python, R programming, SQL, and Scala. You’ll also need to be familiar with database languages, like MongoDB and MySQL. 

Data Visualization and Analysis 

A major aspect of this role is knowing how to create charts and graphs that provide an easy-to-grasp picture of the patterns you discover for stakeholders. 

Math and Statistics

You’ll need a strong command of statistics and mathematics, including familiarity with linear algebra, calculus, probability distribution, regression, dimensionality reduction, and vector models. 

Machine Learning, Deep Learning, and Artificial Intelligence

An understanding of machine learning, artificial intelligence, and deep learning — a subset of artificial intelligence — is important, as these are tools you will use to perform your job. 

Problem-Solving Skills  

As mentioned earlier, you’ll need to be able to analyze a problem by breaking it down into multiple parts. You will also need enough creativity, logic, and persistence to tackle a problem until you’ve found a solution.  

Adaptability

Today, most data scientists focus on data collection, data cleaning, building dashboards and reports, data visualization, and statistical inference, as well as communicating their findings to key stakeholders. 

But as the pace of technology continues to ramp up, it is likely that the role will evolve.  Many of the tasks that are today conducted by data scientists may be performed by computers and AI. A key skill in the future will therefore involve the ability to adapt to new technologies and to learn on the fly.

Communication Skills                                                                                                  

While data scientists must have all the technical skills to extract, understand, and analyze data, one of the most important skills is knowing how to communicate well in order to answer business questions and explain complex results to nontechnical stakeholders.

Learn more about our Data Science Master’s Degree Program.

What Are the Advantages to Having a Master’s Degree in Data Science?

A master’s degree in data science to your resume can offer the following benefits: 

A Deeper Understanding of Theory and Practice in Data Science   

You’ll gain in-depth training in mathematics, statistics, and computer science compared to what you received in your undergraduate studies. You’ll also develop critical thinking and quantitative, domain-specific skills likely to remain in demand even as this fast-paced field quickly evolves.

A Path to Leadership Positions 

With more education under your belt, you can advance to higher-level positions such as machine learning engineer, artificial intelligence engineer, data architect, enterprise architect, or applications architect. 

Potential for a Higher Salary 

Those with more education tend to get more substantial salaries. The average base pay for a data scientist in the United States is about $124,000 according to the Bureau of Labor Statistics, but those in senior positions can expect to make as much as $160,000 or more.

What Type of Jobs Can You Do with a Master’s Degree in Data Science?

The world of data science is exploding, with an ever-expanding number of roles in this dynamic field. A few of the most common data science jobs include: 

• Artificial intelligence engineer : uses traditional machine learning techniques to create models that power applications based on AI. 
• Data scientist : through statistics, math, and programming, analyzes data sets and identifies patterns and problems that might benefit organizations. 
• Data engineer : looks for trends in large data sets and builds algorithms to help organizations mine useful information from raw data.
• Machine learning engineer: creates and runs automated software programs capable of building predictive models from large data sets. The programs “learn” from the information collected, helping them develop more accurate predictive models.
• Software engineer : develops code and operates data tools.
• Data modeler : manages data and creates structures for it through software
• Data analyst : analyzes a company’s data to identify trends, find insights and solutions based on that data, and project future outcomes.
• Big data engineer : collects and prepares large quantities of data.

What Type of Advanced Topics do Data Scientists Work On?

While data scientists can work on almost any project in any field, data scientists with a master’s degree may have a greater selection of in-depth roles to choose from. 

You might find yourself working in government or private industry, in healthcare, tech, or transportation. Roles you may never have thought of include: 

• A disease mapper.  At a health department, you could build predictive epidemiological models to forecast the spread of infectious diseases. Chicago, for example, has adopted an  algorithmic approach  to food safety, sending inspectors to locations where they think there could be a high risk of foodborne illness transmission.
• A cyber city analyst . In city government, you could manage numerous urban data flows ensuring that city systems work the way they should. In Kansas City, Missouri, for instance, government officials  rely on “pothole prediction” technology  to prevent potholes from developing. 
• An autonomous transport specialist . On behalf of an autonomous vehicle manufacturer, a data scientist could build scenarios and crunch data yielding  meaningful ways to improve products and safety .
• A roboticist . On behalf of companies in fields ranging from healthcare to manufacturing, you could build algorithms to help robots acquire new behavioral patterns and the ability to operate semi-autonomously. Robots could then be used in more industries where there are labor shortages, including in  big agriculture  where “agrobots” can now even assess the ripeness of a fruit.

Are Data Science Careers in Demand?

The short answer is yes. In fact, the data science field is on fire, with jobs expected to grow by 36 percent between now and 2031 . As computers and data infiltrate every corner of our lives — from healthcare to digital marketing to financial services, technology, retail, media, and telecommunications — there is a growing need for someone to manage and interpret it all. 

How Can I Pursue a Master’s in Data Science? 

If you’re interested in pursuing a master’s degree in data science , you have made a great choice. This cutting-edge field is expanding, and there are many options for advanced training, including graduate degree programs and graduate certificates .

A master’s degree in data science promises a fascinating career with solid job growth for years to come. 

Explore Graduate Degree Programs at Harvard Extension School.

About the Author

Pamela Reynolds is a Boston-area feature writer and editor whose work appears in numerous publications. She is the author of “Revamp: A Memoir of Travel and Obsessive Renovation.”

Harvard Extension Experience

Start — Or Continue — Your Harvard Extension Studies This Summer

Summer can be a great time to gain ground on your Harvard Extension School (HES) academic journey.

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.

M.S. in Data Science

The m.s. in data science is for individuals looking to strengthen their career prospects or make a career change by developing expertise in data science..

Our students have the opportunity to conduct original research, included in a capstone project, and interact with our industry partners and faculty. Students may also choose an elective track focused on entrepreneurship or a subject area covered by one of our eight centers.

Eligibility Requirements

• Undergraduate degree
• Prior quantitative coursework (calculus, linear algebra, etc.)
• Prior introductory computer programming coursework

Application Requirements

• Online application
• Personal Statement
• Uploaded transcripts from every post-secondary institution attended
• Three recommendation letters
• Curriculum vitae / resumé
• Official Graduate Record Examination (GRE) General Test Scores are optional for the 2023 applications (optional)
• $85 non-refundable application fee
• TOEFL, IELTS or PTE Academic test scores, if applicable

Fall Application Deadline

• Priority Deadline: January 15
• Final Deadline: February 15

Upcoming Admissions Sessions

Seas information sessions:.

• https://apply.engineering.columbia.edu/portal/info_sessions

Data Science Sessions:

• Next Steps: Columbia’s MS in Data Science Program Snapshot Friday, October 11th, 10:30 AM EST (US and Canada)
• Next Steps: Columbia’s MS in Data Science Program Snapshot Monday, November 11th, 1:30 PM EST (US and Canada)
• Next Steps: Columbia’s MS in Data Science Program Snapshot Friday, December 6th, 10:30 AM EST (US and Canada)

Biomedical Data Science Graduate Program Overview

The Biomedical Data Science Training Program is an interdisciplinary graduate and postdoctoral training program, part of the Department of Biomedical Data Science at Stanford University’s School of Medicine.

Our Mission

History of our graduate program, employment in biomedical data science, directions to dbds, contact information, our educational mission.

The mission of DBDS is to train the next generation of research leaders in Biomedical Data Science. Our students gain knowledge of the scholarly informatics literature and the application requirements of specific areas within biology and medicine. They learn to design and implement novel methods that are generalizable to a defined class of problems, focusing on the acquisition, representation, retrieval, and analysis of biomedical information. We also require training in understanding ethical, social, and legal issues and consequences of research. We seek to attract diverse candidates from all backgrounds and experiences.

What is Biomedical Data Science?

Biomedical Data Science is a broad term comprising multiple areas.

• Bioinformatics develops novel methods for problems in basic biology.
• Translational Bioinformatics moves developments in our understanding of disease from basic research to clinical care.
• Clinical Informatics develops methods and tools directly applied to patient care.
• Public Health Informatics works on challenging problems from health systems and populations.
• Imaging Informatics addresses intelligent management, interpretation, and annotation of biomedical images.

Take a look at our current courses. 

Our Graduate Degrees

The graduate training program offers the PhD degree, and three MS degrees (an academic research-oriented degree, a professional distance-learning masters for part-time students, and co-terminal for Stanford undergraduates). We also have post-doctoral fellows, and offer a distance learning certificate.

• Prerequisites . For a graduate degree, Stanford University requires the applicant to have a bachelor’s degree. We do not require any particular major, but we do require that students have strong undergraduate preparation in computer science/software engineering, mathematics (especially calculus, probability and statistics, and linear algebra), and college-level biology. Applicants with limited backgrounds in these areas should fill the deficiencies prior to applying to our program.
• Curriculum . MS and PhD candidates take coursework in four areas: (1) core DBDS classes, (2) an individual plan with electives in computer science, statistics, mathematics, engineering, and allied informatics-related disciplines, (3) required coursework in social, legal, and ethical issues, (4) unrestricted electives. In addition, PhD candidates are required to choose electives in some area of biology or medicine. Degree candidates also learn important didactic skills by serving as teaching assistants in our core courses.
• Funding . We have been continuously funded by  a training grant from the National Library of Medicine since 1984, which provides fellowship support for students who are US citizens and permanent residents. International students bring outside funding or compete for Stanford Graduate Fellowships. Senior graduate students typically receive funding support through their research supervisor.

The History of Our Graduate Program

History at Stanford

The Biomedical Data Science Graduate Program has a long history both at Stanford and internationally, as the first program of its kind. The degree program was initiated in October 1982 as Medical Information Sciences (MIS) and continues to emphasize interdisciplinary education between medicine, computer science, and statistics, offering pre- and postdoctoral degrees and training. The DBDS Program has been supported by a training grant from the National Library of Medicine since 1984, which initially funded only postdoctoral trainees but was broadened to include predoctoral trainees in 1987. The NLM training grant has been renewed every five years since and has provided tuition and stipend support for hundreds of trainees.

Today, the Biomedical Data Science Graduate Program sits in the newly formed Department of Biomedical Data Science and emphasizes methods development and application across the entire spectrum of biology, medicine, and human health.

A Foundation in Medicine and Computer Science

The interaction between Computer Science and other disciplines has produced vibrant areas of research and education at Stanford since the late 1960s; computing activities in the School of Medicine were stimulated even earlier, principally by the Chair of Genetics, Nobel Laureate Joshua Lederberg. Professor Lederberg collaborated with Professor Carl Djerassi (Chemistry) and Professor Edward Feigenbaum (Computer Science) to create what is arguably the first research program that applied the nascent field of artificial intelligence to biomedical problems. Their U.S. Dendral system, which studied the expertise of mass spectroscopists who could interpret an organic compound’s mass spectrum to infer the chemical structure of that compound, is considered the first expert system.

Professor Lederberg’s second key effort was to attract NIH funding for a large medically focused shared computer for the medical school. This computer, known as ACME, was heavily used by Stanford medical researchers, educators, and students until 1973. It brought a computing culture into the environment, which in turn began to attract medical students who had an interest in the intersection of the two fields.  Later ACME gave way to the SUMEX-AIM Computer, also funded by NIH with Lederberg as PI. This resource was the first biomedically focused machine on the ARPANet, which evolved to become today’s Internet.  The SUMEX Computer was a key resource at Stanford for almost 20 years.

Working closely with Stanley Cohen (a Professor of Medicine who later succeeded Lederberg as Chair of Genetics) and Bruce Buchanan (a research scientist in computer science who was a member of the Dendral Project), Edward Shortliffe undertook a combined MD/PhD with the doctoral degree in a self-designed interdisciplinary program. Further discussion with faculty, students, and researchers emphasized the interest and need to formalize this kind of interdisciplinary education, directly leading to the formation of the MIS graduate program.

The Human Genome Project and a Turn at the Turn of the Century

The launch of the Human Genome Project in 1990 and its completion in 2003 seeded substantial interest and need for computing in the biological community. In 2000 Dr. Russ B. Altman succeeded Dr. Shortliffe as Director of the MIS Program and in recognition of a new mission beyond clinical informatics, to fundamental issues of biomedical knowledge, its representation and its application, the program was renamed Biomedical Data Science  Training Program  (DBDS). The term Biomedical Data Science   represents not only the continued development of medical information systems but also the use of sophisticated computation to study medicine at the molecular, cellular, organismal, and population levels.

Biomedical Data Science Today

On September 1, 2023, the Biomedical Informatics (BMI) training program finalized its last step in merging with the Department of Biomedical Data Science (DBDS) and formally changed its name to the Biomedical Data Science Training Program.

Our trainees admitted after September 1, 2023 will earn their Master’s and PhD degrees in Biomedical Data Science.

The mission of our department and the training program remain fully aligned to “advance precision health by leveraging large, complex, multi-scale real-world data through the development and implementation of novel analytical tools and methods.” Aligning the name of the degree program with department name was widely regarded as both logical and appropriate. More importantly, it reflects a shared vision in our research and education missions that serves to pull our integrated work in biomedical informatics, biostatistics and AI/ML under a unified interdisciplinary umbrella.

The DBDS Training Program at Stanford continues to evolve to meet the needs of biomedical computation and application. Under the guidance of the current Director since 2018 and Chair of the Department of Biomedical Data Science, Professor Sylvia Plevritis, and with support from NLM, the DBDS Program continues to innovate in the areas of Healthcare and Clinical Informatics, Translational Bioinformatics, and Clinical Research Informatics. In addition to historical research thrusts in biomedical knowledge representation and the genetic basis of disease, current research explores algorithms for real world biomedical data, multi-modal data and meta-analysis, medical image analysis, responsible clinical decision making, reproducibility, methods for efficient querying and access to big biomedical data, and more.

Prospective students with interest in career directions in Biomedical Data Science should review a list of our Alumni and their current jobs under the People Directory .

If you have a job posting that you would like to send to the DBDS students and post-docs, please email it to dbds-job-openings at lists.stanford.edu for distribution as we deem appropriate for our audience.

DBDS Current Students and Alumni

The  School of Medicine Career Center  offers resources for professional and leadership development, resources for the job hunt ranging from presentation skills, resume preparation, interview skills to job hunt strategy. There is a seminar series from both industry and academia, and a number of industry events: demos, job fairs, industry mixers.

The University’s  Career Development Center  supports undergraduate and graduate career development. They have  Career Fairs .

To add your name to the DBDS jobs email list, send your request to the DBDS student services team .

External Job Listings in Biomedical Data Science

AMIA Job Exchange BayBio’s Job Sites list BioCareer’s Job site Bioinformatics.org’s Jobs site BioinformaticsDirectory listings Genomeweb’s Job listings ISCB Jobs Database Nature’s Jobs list New Scientist Jobs NIH’s job listings Science Career’s Ziprecruiter

Postdoctoral Positions at Stanford

Please see the descriptions for various opportunities in Biomedical Data Science under Postdoctoral Training

Directions to DBDS Program Offices

The DBDS Program Offices are in the Stanford’s Medical School Office Building (MSOB). The street address is: 1265 Welch Road, Stanford, CA 94305.

MSOB is located on the corner of Campus Drive West and Welch Road, between Panama Street and Welch Road. MSOB is a three story white building with redwood window framing. The exact latitude/longitude is 37.431734, -122.179476. See the map, below.

There are two options for parking:

• The parking lot in front of our building, which has an entrance on Welch Road. This lot has a few parking spots with coin metered parking.
• The large parking lot across the street on Welch Road. Entrance to the lot is from Stock Farm Road or Oak Road, but you have to drive within the lot towards the corner of Welch Road and Campus Drive. Payment is through cash, coins, or credit card using an automated permit dispenser. Information:  https://transportation.stanford.edu/parking

For all questions about the program, email: 

[email protected]

Mailing Address: Office Location 

Department of Biomedical Data Science Graduate Training Program

Stanford University School of Medicine

1265 Welch Road, MSOB X-343

Stanford, CA 94305-5464

MS in Data Science

Request information, start the application process, fall 2025 application deadlines:.

Priority deadline: February 1st, 2025

Final deadline: April 1st, 2025

The MS in Data Science (MSDS) in Computing & Data Sciences at Boston University prepares you to make significant contributions to all aspects of computational and data-driven processes that are woven into all aspects of society, economy, and public discourse. It is our goal that this program leads to solution of problems and synthesis of knowledge related to the methodical, generalizable, and scalable extraction of insights from data as well as the design of new information systems and products that enable actionable use of those insights to advance scholarly as well as practical pursuits in a wide range of application domains.

The MSDS is a flexible program designed to meet the goals of students looking to pursue either academic or professional careers in Data Science. Upon completion of the program, students will be prepared to pursue careers in which they will become leaders in their chosen areas, whether in academia through advanced graduate work in a PhD, or in industry (by collaborating, directing, and effectively managing diverse teams of practitioners working at the forefront of industrial R&D).

The MSDS in Computing and Data Sciences is currently designated by US Department of Homeland Security (DHS) as a STEM-eligible degree program . International students in F-1 student status may be able to apply for a 24-month extension of their 12-month Optional Practical Training (OPT) employment authorization. More information about STEM OPT eligibility is available from the BU International Students and Scholars Office (ISSO).

Read about the MS in Data Science program in Fortune Education .

Curriculum 

The MSDS is a 32-credit flexible program designed to meet the goals of students looking to pursue either academic or professional careers in data science, and can be completed in as little as 9 months.  Students will declare either a Core Methods Focused Concentration or Applied Methods Focused Concentration.  In addition to the core curriculum and concentration courses, the MSDS program offers students a unique opportunity to enhance their learning through an optional summer internship or master’s thesis course .  As a result, the program can be extended and completed over 12 or 16 months.  All students begin the program once every year in September; Spring entry term is not offered.

Degree Requirements

Eight semester courses (32 credits) approved for graduate study are required. 

Course requirements include 5 competency courses. Students are expected to take ONE course in each of the following areas (5 courses total):

A1 Modeling and Predictive Analytics

A2 Data-Centric Computing 

A3 Machine Learning and AI

A4 Social Impact

A5 Security and Privacy

Plus 3 additional courses:

• CDS DS 701 Tools for Data Science (The only required course for the MSDS program)
• Concentration Elective 1
• Concentration Elective 2

Concentrations

All MSDS students must declare either a core methods concentration or an applied methods concentration, both of which consist of 3 courses (12 credits)

Core Methods Option

Methods focused students are expected to be interested primarily in the development of general (application agnostic) data science methods and will most likely come from STEM undergraduate majors.  

Students will take DS 701 and 2 additional courses from any of the A1, A2, or A3 competencies. 

Applied Methods Option

Applied Methods focused students are expected to be especially interested in the development and application of special-purpose data science in applied areas such as Management, Public Health, Cybersecurity, etc.  Such students may also be transitioning into data science from one of those fields. 

Students will take DS 701 and 2 additional courses from any of the approved applied methods courses .

Additional Degree Components

Please note that both additional degree components come with an additional tuition cost. The program tuition number shown on the tuition website does not include those additional degree components. Adding the additional degree components will also extend the total length of the program.

Master Thesis Course (4-credits)

The Master thesis course will take place during the 3rd semester. If an MS student wants to add this additional degree component, they must have a faculty member to serve as their Thesis Advisor and complete a thesis application. The Thesis Advisor must be a faculty member from CDS, either core or affiliated. Students who are approved for a thesis will be registered for a Master's Thesis course (DS799). If the MS thesis spans more than one semester, the student must receive approval from their advisor. Students must declare their intention to pursue a thesis by the end of the add/drop period in their 2nd semester. At this point students should identify an advisor and the advisor should agree to supervise the thesis.

Summer Internship Course (1-Credit)

This is NOT a Co-op course and does not provide internship placements. However, career resources are provided for MSDS students during the job searching process.

The summer internship should be in the industry area of focus and completed during both summer terms for a total of 12 weeks. International students will have the opportunity to apply for CPT for their internships as well. The summer internship will happen after you finish the program in both Fall and Spring semesters. During the spring semester, you will need to submit an application, which will include information about your summer internship, to the graduate affairs office. Once your application is approved, you will be registered for a one-credit summer internship course for CPT purposes.

Learning Outcomes

Mastery of the principal tools of data decision making, including defining models, learning model parameters, management, and analysis of massive datasets, and making predictions.

Demonstrated competence in application of data science tools to address substantive questions in one or more applied areas, and will address those questions through sophisticated use of data science tools, including tools specifically appropriate for each applied area.

Ability to extend tools of data decision making, including building specialized computational pipelines, automating data workflows, and developing human-computer interfaces.

Ability to interpret and explain results, including assessing uncertainty and developing data visualizations.

Gain awareness of the social impacts of data centered methods, including ethical considerations, fairness, and bias.

Ability to understand and adhere to policy, privacy, security, and ethical norms.

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.

• Residency Policy for Graduate Students
• Campus housing (section on this page)

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

• Trending Now
• Foundational Courses
• Data Science
• Practice Problem
• Machine Learning
• System Design
• DevOps Tutorial

List of Top US universities for MS in Data Science

The Master of Science in Data Science (MSDS) has become one of the most popular graduate programs globally, as it equips students with in-demand skills in data analysis , machine learning , and data management . The United States, known for its exceptional educational infrastructure and top-tier technology companies, offers some of the most renowned programs in this field. Pursuing an MS in Data Science in the USA provides students with access to world-class education, hands-on experience, and a broad network of professionals in the tech industry.

Why Study Data Science in the USA?

Top-Ranked Universities: The USA is home to many of the world’s best universities, recognized for their cutting-edge research, distinguished faculty, and innovative curricula in data science.

Industry Connections: Universities in the USA often maintain strong relationships with industry leaders, including major tech companies, which provide students with internships and job placement opportunities.

Comprehensive Curriculum: Programs in the USA offer a well-rounded curriculum that combines both theoretical knowledge and practical skills, preparing students for diverse roles in data science.

Research Opportunities: Students have the chance to engage in groundbreaking research and contribute to advancements in data science, collaborating with renowned faculty members.

Diverse Student Body: The multicultural environment in U.S. universities enhances the learning experience, offering a global perspective on solving data-related challenges and opportunities.

Course Curriculum For Data Science in the USA

semester-wise Data Science Curriculum in a 4-year program format:

List of Colleges

Here is the updated table with fees in both USD and INR:

Admission Requirements and Eligibility Criteria for Data Science in the USA

Bachelor’s Degree A bachelor’s degree in a relevant field is essential for pursuing a Master’s in Data Science in the USA. Accepted fields typically include Computer Science, Mathematics, Statistics, Engineering, or related disciplines. Some universities may also consider applicants from non-traditional backgrounds, provided they have completed relevant coursework in programming, statistics, and data analysis.

GPA Requirements Most U.S. universities require a minimum Grade Point Average (GPA) of 3.0 on a 4.0 scale. However, for top-tier institutions, a competitive GPA above 3.5 is often expected. Applicants with lower GPAs might compensate through strong test scores, work experience, or exceptional projects.

GRE Scores Graduate Record Examination (GRE) scores are traditionally required for admission to most U.S. graduate programs. However, an increasing number of institutions have become test-optional, allowing students to choose whether or not to submit GRE scores. For universities that still require the GRE, a strong score in the quantitative section is crucial, especially for data science programs.

English Proficiency Test (TOEFL/IELTS) International students must demonstrate their proficiency in English through standardized tests such as TOEFL or IELTS. Each university sets its own minimum score requirements, typically around 90-100 for TOEFL or 6.5-7.0 for IELTS. Some programs may waive this requirement for students from English-speaking countries.

Letters of Recommendation (LORs) Most universities require 2-3 letters of recommendation. These letters should come from academic professors or professional supervisors who can attest to your technical abilities, work ethic, and potential for success in the program. Strong LORs that provide specific examples of your skills and accomplishments can significantly enhance your application.

Statement of Purpose (SOP) The SOP is a critical part of the application process. In this essay, you are expected to outline your career goals, research interests, and why you want to pursue a degree in data science. This is your opportunity to demonstrate your passion for the field, your readiness for graduate study, and how the program aligns with your career aspirations.

Documents Required for Data Science in the USA

• Transcripts : Official academic transcripts from all post-secondary institutions must be provided. International students may be required to submit their transcripts for course-by-course evaluation through credentialing agencies like WES (World Education Services).
• Test Scores : Ensure GRE and TOEFL/IELTS scores are sent directly from the testing agency to the universities you are applying to.
• Resume/CV : A detailed resume or CV showcasing academic achievements, work experience, internships, research projects, certifications, and relevant technical skills.
• Letters of Recommendation : These should be uploaded through the application portal or sent directly by the recommenders, providing insights into your academic and professional capabilities.
• Statement of Purpose (SOP) : A personal statement outlining your academic and career goals, your motivation for studying data science, and why you’re a good fit for the program.

Admission Process for Data Science in the USA

• Research Programs Begin by researching data science programs that align with your career goals. Consider factors like university rankings, curriculum, faculty, and available specializations. Pay attention to the application deadlines and specific admission requirements for each institution.
• Prepare Documents Gather all required documents: academic transcripts, test scores (GRE, TOEFL/IELTS), resume/CV, letters of recommendation, and the statement of purpose. Ensure all documents are accurate and meet the format required by the university.
• Submit Applications Complete the online application form for each university. Carefully follow all instructions and ensure that all necessary documents are uploaded or submitted as directed.
• Application Fee Pay the application fees as required by each university. The fees can vary but typically range from $50 to $150 per application.
• Interviews Some universities may require an interview, either in person or online. Prepare by reviewing common interview questions, reflecting on your experiences, and articulating your goals clearly.
• Admission Decision Once the application is submitted, it may take several weeks to months to receive an admission decision. During this time, universities may ask for additional documents or clarifications.
• Acceptance and Enrollment If you are accepted, you will need to confirm your enrollment by submitting a deposit. You will also need to register for classes and attend orientation sessions as per the university’s instructions.

Pursuing a Master’s in Data Science from a top U.S. university offers students access to world-class education, cutting-edge research, and unparalleled career opportunities. Understanding the curriculum, eligibility criteria, and admission process is essential to making informed decisions and securing a place in a program that best suits your academic and professional goals. By preparing a strong application, you can open doors to a successful career in the rapidly growing field of data science.

List of Top US universities for MS in Data Science – FAQs

What is the average duration of an ms in data science program.

Typically 1.5 to 2 years depending on the program and whether we study full-time or part-time.

Is it necessary to have a background in Computer Science to apply?

While a background in Computer Science or a related field is preferred the some programs accept students from the other disciplines with relevant coursework or work experience.

Can international students apply for MS in Data Science programs?

Yes, most programs are open to international students though they may require proof of the English proficiency and additional documentation.

What kind of financial aid is available for MS in Data Science students?

The Financial aid options include the scholarships, assistantships and loans. Check with the individual universities for the specific opportunities.

Are GRE scores required for all Data Science programs?

Many programs have become test-optional but some may still require GRE scores. Check each program’s requirements.

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Master of Science in Business Analytics

The MSBA at the UIC Business Liautaud Graduate School prepares you for today’s hottest careers.

The MS in Business Analytics is a STEM designated program* providing you with the skill set necessary to analyze large data sets and generate insights through techniques in data visualization, statistical modeling and data mining. The degree will provide you with a holistic approach to the field and develop expertise in data management, machine learning and predictive analytics, along with a strong business foundation *(CIP Code 52.1301).

The curriculum emphasizes industry experience, exposure and collaboration across a variety of analytics projects. Every industry is using technology to generate new data and they need analysts to interpret that data to understand customers, develop new products and enhance revenue.

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• Edit icon Request Information
• Calendar icon Attend An Event
• Check icon Apply Now

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# 51 MSBA in U.S. — QS World University Rankings

STEM designated program

UIC Business Master of Science in Business Analytics Heading link Copy link

GMAT/GRE Waivers Heading link Copy link

For fall 2024 applicants, the MS in Business Analytics program offers GMAT/GRE waivers for the following:

• Graduates of U.S. colleges and universities with 3+ years of post-bachelor’s professional work experience by the start of the program
• Current students and graduates of U.S. colleges and universities with a 3.0+ cumulative GPA (all majors)
• Current students and recent alums of the U of I system who qualify through Preferred Admissions

Please complete the GMAT/GRE waiver request form to find out if you qualify. Please note that competitive GMAT/GRE scores may help your chances of admission and being awarded merit aid.

• GMAT/GRE Waiver Request Form

Curriculum Heading link Copy link

The MSBA curriculum includes courses covering current topics on business intelligence, applied statistics, data mining, machine learning, text analytics, data visualization, optimization and ‘big’ data analytics. The curriculum integrates knowledge and training on technical topics with business applications and functions, such as finance, marketing and operations to understand analytics strategy and practice in organizations. Various analytics-related business electives allow students to focus on specific business areas.

Cutting-Edge Curriculum

Fast-paced program The 32-credit hour program can be completed in one year of full-time study, or more than one year of part-time study. Students may be assigned additional prerequisite coursework depending upon their academic and professional background. *

Exposure to technology Through intense coursework, you will gain experience in using current tools such as R, Python, Hadoop, Spark, Tableau, IBM/SPSS and others. With hands-on projects, you can learn to analyze live data from digital marketing and social media, finance, accounting, supply chain management, healthcare and e-commerce.

Experiential learning The MSBA includes a capstone experience providing you with the opportunity to work with a client organization to analyze a business issue and develop analytics solutions.

Who should enroll? We encourage applicants from all majors and backgrounds who want to combine skills in data management, technology and analytics to create business solutions to apply. Our program is also flexible for working professionals and entrepreneurs seeking to develop new skillsets in business intelligence with the goal of becoming data-driven decision makers.

More Information

• * For a full list of requirements including prerequisites, visit the UIC Catalog .
• Full a full list of courses offered, visit the Course Catalog .
• For tuition and fees information, visit the Tuition page .

Program Outcomes

The UIC Business Liautaud Graduate School Master of Science in Business Analytics program helps you develop skills and training to work in data-rich environments, and enables you to develop capabilities in business intelligence, machine learning and analytics. After completing the program, you will be able to bring together information technology, AI, data science and business so that you will be able to analyze and communicate the value of data.

Career Outcomes : Opportunities in business analytics abound and promise to grow exponentially for the next decade. Our program prepares students for analyst, data scientist roles in the areas of accounting, finance, digital marketing, supply chains, revenue management, risk management, among others. If you are a current working professional, the program can open up managerial opportunities such as product manager for information and corporate level positions including chief data officer, analytics officer or information officer.

More than  91% of recent graduates (Class of 2022) had been employed within six months of graduation.The average starting salary for the these MSBA graduates was $89,000.

Explore Degree Requirements

Joint degrees heading link copy link.

• Master of Science in Business Analytics and Master of Science in Management Information Systems
• Master of Science in Business Analytics and Master of Business Administration
• Master of Science in Business Analytics and Master of Science in Finance

Program Faculty Heading link Copy link

Sid bhattacharyya, ranganathan chandrasekaran, boxiao beryl chen, michael y. choi, vijay kamble, moontae lee, selva nadarajah, aris ouksel, stanley sclove, negar soheili, john sparks, theja tulabandhula, james christopher westland, featured courses heading link copy link, ids 521 advanced database management.

Advanced topics in Structured Query Language (SQL), dimensional modeling for Data Warehousing (DW) systems, and some next generation (NoSQL) and cloud-based database architectures that address current Big Data challenges.

• IDS 558 Revenue Management

Mathematical models and analytics to solve for profit-maximizing business strategies for companies. Topics covered include price optimization, price differentiation, market segmentation, capacity allocation, and network management.

IDS 560 Analytics Strategy and Practice

Client-based projects to learn how to apply the analytic skills developed in the MS Business Analytics curriculum to practical problems. Case studies and analytics related issues in the context of organizational strategy.

IDS 561 Analytics for Big Data

Fundamental concepts of distributed algorithms to analyze large-scale data in various domains, data mining on large data and applications (MapReduce), data storage, query and business intelligence with distributed databases. Use of Hadoop, Hive, Spark, Mahout.

• IDS 564 Social Media and Network Analysis

Analytic approaches to help organizations utilize large volumes of social media data for making informed business decisions, social network analysis, customer behavior analysis, social advertising using machine learning methods, use of Gephi and R.

IDS 572 Data Mining for Business

Methods and tools for discerning meaningful and useful patterns in data. Overview of data mining and its application to business problems, core data mining techniques, and best practices. Software used include use of Rapid Miner and R.

IDS 575 Statistical Models & Methods for Business

Foundations of modern statistics and machine learning methods for business analytics. Multivariate analysis, generalized linear models, supervised and unsupervised learning, maximum likelihood and expectation maximization, structured prediction, tree methods, sampling, support vector machines, time series analysis. Software used include R.

• IDS 576 Advanced Predictive Models

Advanced machine learning techniques and applications. Neural networks and deep learning, hierarchical models, Bayesian networks. Use of Python and various packages.

In Their Own Words Heading link Copy link

“My passion for data and insights began during high school when I was working on a project on the milk supply pattern of my locality which encompassed thousands of households. My curiosity got the best of me, fast forward a few years and I ended up pursuing a master’s in economics, wherein my understanding of data-driven business grew and I also learned its immense importance. My transition to analytics as a whole has been a gradual and smooth ride, influenced by my work at Capgemini where I was a consultant with Data Science and Analytics team. UIC provided me the best possible MSBA program which matched my background and catered to my requirements. It helped me develop sound technical skills along with knowledge of business concepts. UIC has definitely helped me kick start my journey in the world of analytics.” Varshini Varanasi  |  Data Science & Analytics Consultant at EY, MSBA '18

So Much More at UIC Business Heading link Copy link

Special topics.

Analytics special topics (IDS 594), updated regularly:

• Cognitive Computing and Analytics with IBM Watson
• Health Information Management & Analytics
• Advanced Analytics Using SAS
• Machine Learning Applications Using R
• Machine Learning Applications with Python
• Data science for Online Customer Analytics

Analytics Electives

Other analytics electives:

• IDS 476 Business Forecasting Using Time-Series Methods
• IDS 566 Advanced Text Analytics
• IDS 567 Data Visualization

Business Electives

Various courses based on individual interest, such as:

• IDS 552 Supply Chain Management
• IDS 540 Marketing Analytics
• ACTG 516 Financial Statement Analyses
• FIN 510  Investments
• FIN 516 Options & Futures

Additional Organizations and Programs Heading link Copy link

Business analytics organization student chapter.

The BAO is a student organization that fosters interaction and networking with industry experts and leaders in the field of analytics and data science. Workshops and speaker series expose students to different technologies and application areas.

Preferred admission and STEM-designated program

Outstanding UIC and UIUC undergraduates with a GPA of 3.00 in their undergraduate degree in IDS can be admitted into the MSBA program without a GMAT or GRE score.

The MSBA is a STEM designated program. International students are allowed work experience on the OPT STEM extension.

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Some of the world’s largest businesses recruit at UIC Business, including:

Fortune 500

• Assurant Solutions
• Blue Cross & Blue Shield
• CAN Insurance
• Capital One
• Cardinal Health
• Caterpillar
• Critical Mass
• Discover Financial Services
• Dyson Technical Solutions
• Enova International
• Flexon Technologies
• Foresight ROI
• IBM (Poland)
• McKinsey & Company
• Motorola Solutions

...and More

• Remedy Analytics
• Rise Interactive
• SAP America
• Shelby Group
• Thompson-Reuters
• United Health Group
• Wavicle Data Solutions
• Webb Mason Analytics

Frequently Asked Questions Heading link Copy link

The Master of Science in Management Information Systems Program focuses on information technology, software and systems, and prepares students for jobs as developers, business analysts, project managers and technology consultants.

The Master of Science in Business Analytics Program trains students to work in data-rich environments. It enables students to solve real business problems through organization, analysis, and interpretation of data. This course prepares students for jobs as data analysts, data scientists, and domain analysts (e.g., financial analyst, healthcare analyst, or risk analyst).

The program offers prerequisites to equip students with the necessary business and technical background. Prerequisites do not count toward the 32-credit program requirement. Technical prerequisites cover essential knowledge of databases, programming and statistics. Students without prior business education or experience are required to take two courses in core business areas like finance, marketing, accounting or operations. Prerequisite courses may be waived based on equivalent prior coursework or work experience in a functional area. For a full list of requirements including prerequisites, see “View Degree Requirements” above or visit the UIC Catalog.

The MS Business Analytics program offered by the Liautaud School of Business is a STEM (Science, Technology, Engineering and Math) course.

The MS Business Analytics Program admits students in both the fall and spring semesters. Students may choose to enter in the summer semester to take prerequisite courses, including programming, database and business prerequisites. Students seeking summer admission are encouraged to apply early.

Students from a variety of backgrounds (including tech and business) enroll for the MSBA Program. Through coursework, students develop the technical and business skills required to successfully complete the program. We assess candidates to provide each individual with prerequisite courses based on their background and work experience.