• Record of grades or other academic performance; we do not require WES or other external evaluation of international transcripts. Please submit any transcripts available to give as complete a picture as possible of your educational record. Please provide unofficial (scanned) copies of your transcripts as part of the electronic application. You will be prompted to mail an official copy of your transcript if you accept an offer of admission.
• CV or resume
• Personal statement: Please tell us in 1000 words or less why you are specifically interested in the Data Science program at Brown University. Tell us something about yourself and your interests that we can’t figure out from your resume. What are your goals and plans with the degree after graduation? How do your plans align with Brown and the DSI’s mission statement? 
• Writing sample (optional): If you have written a technical article or paper which you feel would help us evaluate your preparation for the program, this is where you would attach it.
• Three (3) letters of recommendation (suggest to your letter writers that they look at this site)
• TOEFL or IELTS for applicants whose native language is not English; see testing and waiver policies here. We prefer scores above 620 (pBT) or 105 (iBT). The corresponding minimum IELTS score is 7.5.
• Reporting code for tests: 3094 (no departmental code)

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. 

At a minimum, we recommend that students entering the program have completed at least a year of calculus (at the level of MATH 0090 & 0100), two semesters of linear algebra (at the level of MATH 0520 and 0540), a semester of calculus-based probability and statistics (at the level of APMA 1650), and an introduction to programming (at the level of CSCI 0150 or 0170). Please find descriptions of these courses on Courses @ Brown (choose Any Term in the keyword search). 

We also admit exceptional students who lack one of the minimum requirements in linear algebra, probability and statistics, and computer science. These students will be expected to take a course at Brown or elsewhere to achieve the necessary preparation before the program begins, and should consult with the program director to determine what is most appropriate. 

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. 

You do not have to complete your preparation for the program before you apply! 

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: There are no eligibility criteria for application, such as minimum GPA (but see recommended minimums for English-language test scores below). 

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. 
• Personal Statement: Please tell us in 1000 words or less why you are specifically interested in the Data Science program at Brown University. Tell us something about yourself and your interests that we can’t figure out from your resume. What are your goals and plans with the degree after graduation? How do your plans align with Brown and the DSI’s mission statement?
• 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. Ask your recommender to follow these guidelines. Remind your recommenders of deadlines to ensure they are met. 
• 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. Additional evidence (e.g., certificate of completion of an English course) may also be submitted. We generally do not consider applicants who have scored below 620 (pBT) or 105 (iBT), and prefer scores higher than that. The corresponding minimum IELTS score is 7.5.  More information. 
• 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 foreign applicants. If these awards are really important, we would expect your recommenders to mention them.

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

Tuition and fee information is listed on the Bursar's website. The approximate total tuition (excluding fees) for students entering the program in fall 2023, and completing in 12 months, is $85,626. This includes 8 credits (4 per semester) at the 2023-2024 tuition rate, and the 9th credit at the 2024-2025 tuition rate (estimated with an increase of 4%).

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 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 who issued them.

Unfortunately, we do not currently have scholarships available for this program. Brown's Office of Financial Aid can help with loan funding; you can find information and a financial worksheet.