The data science master's program provides a comprehensive education in the methods and algorithms of data science, through a study of relevant topics in mathematics, statistics and computer science.
The Data Science Initiative at Brown offers a master's program that prepares students from a wide range of disciplinary backgrounds for distinctive careers in data science.
Extracting meaning and value from increasingly complex and voluminous data requires a distinctive set of skills, methods and tools that together form the emerging discipline of data science. Data science integrates foundational elements from computer science, mathematics and statistics, and combines them meaningfully with deep domain–area knowledge.
Brown’s data science master's program educates students in the methods and algorithms of data science, through a study of relevant topics in mathematics, statistics and computer science, including machine learning, data mining, security and privacy, visualization, and data management. The program also provides experience in important, frontline data science problems in a variety of fields and introduces students to ethical and societal considerations surrounding data science and its applications.
The program's course structure ensures that the students meet the goals of acquiring and integrating foundational knowledge of data science, applying this understanding to specific problems, and appreciating the broader ramifications of data–driven approaches to human activity. An experiential learning component offers students the opportunity to apply their skills to real-world data science problems. Students can fulfill this requirement through an industry internship or work with faculty at Brown or elsewhere.
Students entering the program will be required to have completed at least a year of calculus (at the level of MATH 0090 & 0100), a semester of linear algebra (at the level of MATH 0520), a semester of calculus–based probability and statistics (at the level of APMA 1650), and an introduction to programming (at the level of CSCI 0150 or 0170).
Admitted students will consult with the program director to acquire any additional preparation for the program.
Please see the Data Science Initiative website for more information.
If you have any questions regarding the application process for this program, please email firstname.lastname@example.org.
Required; the GRE General Test at home version is accepted.
Required for any non-native English speaker who does not have a degree from an institution where English is the sole language of instruction or from a University in the following countries: Australia, Bahamas, Botswana, Cameroon, Canada (except Quebec), Ethiopia, Ghana, Ireland, Kenya, Lesotho, Liberia, Malawi, New Zealand, Nigeria, Zimbabwe, South Africa, Sierra Leone, Swaziland, Tanzania, Gambia, Uganda, United Kingdom (England, Scotland, Northern Ireland, Wales), West Indies, Zambia.
The TOEFL iBT Special Home Edition and the IELTS Indicator exam are accepted.
Students from mainland China may submit the TOEFL ITP Plus exam.
Required. All applicants may upload unofficial transcripts for application submission. Official transcripts are ONLY required for enrolling students before class start. An international transcript evaluation (WES, ECE, or SpanTran) is required for degrees from non-U.S. institutions before enrollment.
Letters of Recommendations:
Three (3) recommendations required
1000 word personal statement that gives your reasons to pursue graduate work in the field of your study. The statement should include examples of your past work in your chosen field, your plans for study at Brown, issues and problems you'd like to address in your field and your professional goals.
5th Year Deadline
- 3 credits in mathematical and statistical foundations
- 3 credits in data and computational science
- 1 credit in societal implications and opportunities
- 1 elective credit to be drawn from a wide range of focused applications or deeper theoretical exploration
- 1 credit capstone experience, which includes a paper and/or oral presentation
- Thesis: Not required.