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Data Science

Big data is generating a fundamental revolution in many scholarly and intellectual endeavors. Large scale data sets come from digitized analog content created over centuries as well as new data from a large number of sources such as remote sensing, mobile devices, genomic studies, brain images, massive administrative databases, simulation runs, retail transactions, and cameras. These new data sets, coupled with algorithmic and statistical techniques for advanced analysis, make it possible to improve human well-being, accelerate scientific discovery, advance scholarship, and create new social and commercial value. Extracting meaning and value from increasingly complex and voluminous data requires a distinctive set of skills, methods and tools that have been woven together to form an emerging discipline called "Data Science." This new discipline integrates foundational elements from computer science, mathematics and statistics, and combines them meaningfully with deep domain-area knowledge.

The Data Science Initiative at Brown offers a new master's program that will prepare students from a wide range of disciplinary backgrounds for distinctive careers in Data Science. Rooted in a research collaboration among four very strong academic departments, the master's program will offer a rigorous, distinctive, and attractive education for people building careers in Data Science and/or in Big Data Management. The program's main goal is to provide a fundamental understanding of the methods and algorithms of Data Science. Such an understanding will be achieved 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 will also provide experience in important, frontline data-science problems in a variety of fields, and introduce students to ethical and societal considerations surrounding data science and its applications.

The program's course structure, including the capstone experience, will ensure that the students meet the goals of acquiring and integrating foundational knowledge for data science, applying this understanding in relation to specific problems, and appreciating the broader ramifications of data-driven approaches to human activity.

Completion Requirements:

  • 3 credits in mathematical and statistical foundations 
  • 3 credits in data and computational science
  • 1 credit in societal implications and opportunities
  • 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. 

Admission requirements
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).

We also admit exceptional students who lack one or more of the minimum requirements in linear algebra, probability and statistics, and computer science. The four departments (Math, Applied Math, Computer Science, and Biostatistics) will offer a suitable course in each of these three topics during the Brown summer session before the first semester.

The students may also acquire the necessary prerequisites at another institution, with the approval of the program director. See FAQs for additional information.

GRE General: Recommended
GRE Subject: Not required
TOEFL: Required
Writing sample: optional 
Application Deadline: Admitted on a rolling basis until March 15