• Transcripts. All applicants may upload unofficial transcripts for application submission. Official transcripts are only required for students who are admitted to the program and are due before classes start. An international transcript evaluation (WES, ECE, or The Evaluation Company) is required for degrees from non-US institutions before enrollment.
• CV or resume.
• Program-specific questions. In lieu of a personal statement, applicants will be asked to answer a series of short form questions:
  1. Describe a skill, experience, or aptitude that sets you apart from other students interested in studying data science at Brown. (max 250 words)
  2. What are your goals and plans with the degree after graduation? How do your plans align with Brown and the DSI’s mission statement? (max 250 words)
• Video Essay: Applicants will be asked to record a short video to describe an internship, research, or course project they particularly enjoyed working on.
• Writing sample (optional). If you have written a technical article or paper in a relevant area which you feel would help us evaluate your preparation for the program, this is where you would attach it.
• Three (3) letters of recommendation. Letters must give a detailed, factual, and candid‬ evaluation of the applicant's capabilities. Rankings and comparisons with other students are‬ very useful. If you’ve completed internships, worked fulltime or part-time in a relevant field, or‬ worked as an RA or TA, recommendation letters from your supervisors are highly‬ recommended! Letters from instructors are only valuable if you interacted with the faculty‬ regularly and they can give detailed evaluation. Please give your recommenders ample time to submit your letter by the February 1 deadline. We suggest that you ask your letter writers to look at this site for more details about the program.
• TOEFL or IELTS. Applicants whose native language is not English and who have not received a college degree from an institution where the instruction is in English must take the TOEFL or IELTS exam;  see testing and waiver policies here. Additional evidence (e.g., certificate of completion of an English course) may also be submitted. We prefer scores above 620 (pBT) or 105 (iBT), and prefer scores higher than that. The corresponding minimum IELTS score is 7.5. 

We seek to admit students from a wide range of backgrounds for distinctive careers in Data Science. This means that students will have a wide range of experience through work and education, which can prepare them for the program in a variety of ways. There are no fixed prerequisites for the program; however, you will need a certain level of math, statistics, and computing experience in order to succeed. Applicants should be prepared to primarily work in Python and R in our courses.

At a minimum, we recommend that students entering the program have completed:

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

Please find descriptions of these courses on Courses at Brown. If necessary, admitted students will consult with the program director to acquire any additional preparation for the program.

We may also admit exceptional students who lack one or two of the minimum requirements in linear algebra, probability and statistics, and computer science. If necessary, admitted students will consult with the program director to acquire any additional preparation for the program. 

If you have questions about your preparation for this program, please carefully read the course descriptions referred to above on Courses @ Brown. You can also take a look at our online program preparation modules or Mathematics for Machine Learning and Data Science: if you are not familiar with the material taught in those modules, you will need additional preparation. You can use those modules or other online courses to get the needed preparation. 

We consider numerous criteria including academic performance, letters of recommendation, industry or other work experience, and fit with Brown University's mission. We also consider TOEFL/IELTS scores (if relevant), motivation, work experience, awards, honors, prizes, and other accomplishments. Because master's applicants are so diverse, no single set of criteria adequately covers all the cases.

Please note: Brown University requires a minimum 3.0 on a 4-point scale for admission (with exceptions for unusual circumstances).

Factors we consider when evaluating applications include:

• Academic performance. The GPA is not the only criterion. Grades in data-science related areas are also important -- we specifically check grades in calculus, linear algebra, and statistics courses. Also, we take into account the fact that at some very competitive schools it is very difficult to achieve a high GPA.
• Work experience. We regularly accept applicants who have had work experience after their undergraduate degree, both in data science related fields and not. Not all applicants have work experience, but for those who do, a good description helps us better evaluate your application. Applicants should describe what programming languages and tools they have used.
• Research experience. Research experience is not required for master's applicants and many of our applicants do not have any, but you can use experience you've had to demonstrate your ability to handle graduate-level data science material. Applicants should describe what programming languages and tools they have used.

• Program-specific questions. In lieu of a personal statement, applicants will be asked to answer a series of short-form questions. The goal of these questions is to get to know you better, your motivation for applying for the data science master’s program at Brown, and what unique experiences you bring to the table.

  Video Essay: Communication skills are essential in data science and related fields. We are looking for solid communication skills, clear explanations, and enthusiasm for the topic you describe.  

• Letters of recommendation. Letters must give a detailed, factual, and candid‬ evaluation of the applicant's capabilities. Rankings and comparisons with other students are‬ very useful. If you’ve completed internships, worked full-time or part-time in a relevant field, or‬ worked as an RA or TA, recommendation letters from your supervisors are highly‬ recommended! Letters from instructors are only valuable if you interacted with the faculty‬ regularly and they can give a detailed evaluation. Please give your recommenders ample time to submit your letter by the February 1 deadline. We suggest that you ask your letter writers to look at this site for more details about the program.
• TOEFL and IELTS scores. Applicants whose native language is not English and who have not received a college degree from an institution where the instruction is in English must take the TOEFL or IELTS exam;  see testing and waiver policies here. Additional evidence (e.g., certificate of completion of an English course) may also be submitted. We prefer scores above 620 (pBT) or 105 (iBT), and prefer scores higher than that. The corresponding minimum IELTS score is 7.5.
• Awards, honors, and prizes: Unless they are well-known (e.g., NSF fellowship or graduation with honors), please give details about them (how many candidates? how many awards? what were the selection criteria?). This is especially important for international applicants. If these awards are really important, we would expect your recommenders to mention them.

The Data Science Institute (specifically, a group of faculty members) evaluates your application and makes recommendations to the School of Professional Studies (SPS), which typically follows our recommendations. Therefore, you are generating your application to be read primarily by the faculty of the Data Science Institute. You will be notified by the SPS if you are admitted. 

Tuition & Fees

Tuition and fee information is listed on the Bursar's website. The approximate total tuition (excluding fees) for students entering the program in Fall 2026, and completing in 12 months, is $91,764. This includes 9 credits required for graduation.

The total cost of the program is only slightly higher for those taking longer than 12 months to complete. Tuition is charged per credit, and students are charged for each credit in the semester when they are registered for the credits. The cost per credit is slightly higher each academic year (usually around 4-5% higher), and certain fees are charged by the university each semester. See the Bursar's website for more details. 

Total cost of attendance, which includes food, housing, and other expenses, does of course depend on how long you take to complete the program. If you have been issued documents with cost of attendance figures, they may include estimates for some of these non-tuition expenses. They may also be based on the default program structure, which is 24 months (two credits per term, so four credits in the first year). The offices best positioned to answer further questions about those documents are the offices that issued them.

Scholarships

The Data Science Institute offers a limited number of full, half, and quarter tuition scholarships, covering 100%, 50%, and 25% of tuition costs, respectively.‬ These scholarships are available to U.S. citizens and legal permanent residents only.‬ Merit-based scholarship decisions are made based on your submitted application; no‬ additional information is required. If you are interested in a need-based scholarship,‬ please submit a‬‭ FAFSA form at the same time you submit your application, but no later than the November 1st deadline for spring intake, and the February 1st deadline for fall intake. Applicants considered for a scholarship may be invited for a zoom‬ interview during the review period. Please note that scholarships apply only to the‬ nine courses required for graduation from the program.

Outside of these scholarships, Brown's Graduate Funding website has information regarding federal student aid, private educational loans, veteran benefits, and external fellowships, scholarships, and grant opportunities.