Data Science

Study mode:On campus Study type:Full-time Languages: English
Foreign:$ 54.7 k / Year(s) Deadline: Mar 15, 2025
61 place StudyQA ranking:2351 Duration:1 year

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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 will be conducted over one academic year plus one summer, with the option for an additional pre-program summer for students who lack one or more of the basic prerequisites. The regular program includes two semesters of coursework and a one-summer (5- 10 week) capstone project focused on data analysis in a particular application area. 

There are nine credits unites required to pass the program: four in each of the academic year semesters, and one (the capstone experience) in the summer. The nine credit-units divide as follows:

  • 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 deepertheoretical exploration, and
  • 1 credit capstone experience.

Semester 1

The first semester will consist of two double-credit courses, each counting as two units (six meeting hours per week per course).

  1. An Introduction to Topics in Probability, Statistics, and Machine Learning: This course will include topics such as maximum likelihood estimation (MLE); entropy; divergence; random numbers and their applications; introduction to high- dimensional data; graphical models and exponential families; regression and density estimation.
  2. An Introduction to Data and Computational Science: The course will cover basic computational models and algorithms; data management and visualization; basic web programming; information retrieval; integration, and cleaning; hardware; distributed systems; security and privacy; multi-media analytics.

These two courses will be closely coordinated and will come together in the final weeks through small-group projects that draw on the methods learned in both. The project groups formed toward the end of the term will work on analyzing data from one of several possible areas of application using the techniques and tools learned in the first–semester courses. The semester will conclude with each group giving an oral presentation or hosting a poster session.

Semester 2

The second semester covers four single-credit courses:

  1. Probability, Statistics and Machine Learning: Advanced Methods: Includes topics such as estimation and approximation in exponential families; nonparametric regression and density estimation; classification; ensemble methods;
  2. Data and Computational Science: Advanced Methods: Includes topics such as data mining; computational statistics; machine learning and predictive modeling; big data analytics algorithms;
  3. Data and Society: A uniquely Brown course involving case studies that will cover topics such as the broader implications in policy and ethics; publication bias and its impacts on society; security vs. privacy; and homeland security, NSA, and the hope for automated triage. This course will leverage faculty and curricular existing resources, including the Watson Institute and departments in the social sciences and humanities;
  4. Elective: The elective course will be proposed by the student and approved by the program director. Please note that a number of both existing and new courses that would be appropriate electives fall outside of the four core departments. Students may choose, in these elective courses and in their capstone projects, to apply the skills acquired in the rest of their courses to topics and areas of particular intellectual interest. 

Summer Capstone

For their capstone experience, students will work on a project with real data, potentially in any one of the areas covered by the elective course. A faculty member from one of the four departments will oversee the capstone course, although each student may collaborate with an additional faculty member, postdoc, or industry partner on his/her project. Each student will prepare a paper and/or oral presentation of his/her work. The summer capstone should entail at least 180 hours of work (to receive one course credit) and as such, may be completed in 5-10 weeks. The project may begin and end at any time during the summer. A letter grade will be awarded for the summer capstone course.
Upon completion of the summer capstone, students will receive a certification of completion of course requirements for the ScM degree, although the actual degree will not be officially awarded until the following May.

Pre-program summer (as applicable)

In order to cover missing pre-requisites we will offer courses during the Brown summer session. Students needing this background preparation will enroll through the usual channels. We note that these summer courses are prerequisites only and would not count towards the master’s degree requirements. Students taking Brown courses in the summer will incur additional tuition costs. Students admitted to the master’s program may also complete their prerequisite coursework at another institution, with appropriate approval of the program director.

Requirements

  • GRE General: Recommended
  • GRE Subject: Not required
  • Record of grades or other academic performance
  • Letters of reference (you might suggest to your letter writers that they look at this site)
  • TOEFL (for applicants whose native language is not English)

Application Procedure

We accept applications until March 15 for fall admission. We admit students on a rolling basis but cannot accept applications after the deadline has passed. Once the application system has closed, it will not reopen until mid September for new applications. You will be notified by the Graduate School if you are admitted.

Please go here to apply. We strongly urge you to 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 are accepted to the program.

Graduate applications are handled by a combination of the Data Science Initiative and the Graduate School. Your application is formally processed by the Graduate School. Its content is then read by members of the Data Science Initiative, who then forward their recommendations to the Graduate School. The Graduate School formally admits you to the program. Therefore, you may receive correspondence from either of these entities.

Admission requirements

The new master’s program at Brown aims to admit students from a wide range of backgrounds for distinctive careers in Data Science. 

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.

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