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Data Science Initiative

Brown’s Data Science Initiative (DSI) is a cross-disciplinary collaboration between four core departments (Applied Mathematics, Biostatistics, Computer Science, and Mathematics) to catalyze data-enabled science and scholarship across the campus. The collaborations between these departments deepens Brown's the data science expertise, and creates new opportunities for innovation in both the methods and the applications of Data Science.

Our academic programs will offer a rigorous, innovative, and reflective approach to learning and collaboration for anyone seeking a distinctive professional profile on which to build a career in data-enabled fields. As our initial step, we offer a one-year Masters Program that prepares students from a wide range of disciplinary backgrounds.

For additional information, please visit the initiative's website: http://dsi.brown.edu/

Course usage information

DATA 0080. Data, Ethics and Society.

A course on the social, political, and philosophical issues raised by the theory and practice of data science. Explores how data science is transforming not only our sense of science and scientific knowledge, but our sense of ourselves and our communities and our commitments concerning human affairs and institutions generally. Students will examine the field of data science in light of perspectives provided by the philosophy of science and technology, the sociology of knowledge, and science studies, and explore the consequences of data science for life in the first half of the 21st century.

Course usage information

DATA 1030. Introduction to Topics in Data and Computation Science.

Mastering big data requires skills spanning a variety of disciplines: distributed systems over statistics, machine learning, and a deep understanding of a complex ecosystem of tools and platforms. Data Science refers to the intersection of these skills and how to transform data into actionable knowledge. This course provides an overview of techniques and tools involved and how they work together: SQL and NoSQL solutions for massive data management, basic algorithms for data mining and machine learning, information retrieval techniques, and visualization methods.

Prerequisites: A course equivalent to CSCI 0050, CSCI 0150 or CSCI 0170 are strongly recommended.

Course usage information

DATA 2080. Data and Society.

A course on the social, political, and philosophical issues raised by the theory and practice of data science. Explores how data science is transforming not only our sense of science and scientific knowledge, but our sense of ourselves and our communities and our commitments concerning human affairs and institutions generally. Students will examine the field of data science in light of perspectives provided by the philosophy of science and technology, the sociology of knowledge, and science studies, and explore the consequences of data science for life in the first half of the 21st century.

Professor

R. Bahar
Professor of Engineering; Professor of Computer Science

Ugur Cetintemel
Professor of Computer Science

Eugene Charniak
University Professor of Computer Science

Maurice P. Herlihy
An Wang Professor of Computer Science

John F. Hughes
Professor of Computer Science

Sorin Istrail
Julie Nguyen Brown Professor of Computational and Mathematical Science

Philip Klein
Professor of Computer Science

Shriram Krishnamurthi
Professor of Computer Science

David H. Laidlaw
Professor of Computer Science

Michael L. Littman
Professor of Computer Science

Anna A. Lysyanskaya
Professor of Computer Science

Steven P. Reiss
Professor of Computer Science

Roberto Tamassia
Plastech Professor of Computer Science

Gabriel Taubin
Professor of Engineering; Professor of Computer Science

Eliezer Upfal
Rush C. Hawkins University Professor of Computer Science

Andries van Dam
Thomas J. Watson Jr. University Professor of Technology and Education

Stanley B. Zdonik
Professor of Computer Science

Professor Research

Kathryn Fisler
Professor of Computer Science (Research)

Acting Professor

Franco Preparata
An Wang Professor Emeritus of Computer Science

Associate Professor

Amy R. Greenwald
Associate Professor of Computer Science

Seny F. Kamara
Associate Professor of Computer Science

Rodrigo Fonseca
Associate Professor of Computer Science

Sohini Ramachandran
Associate Professor of Biology; Associate Professor of Computer Science

Sherief Reda
Associate Professor of Engineering; Associate Professor of Computer Science

Associate Professor Research

Thomas W. Doeppner
Associate Professor of Computer Science (Research)

Assistant Professor

Theophilus A. Benson
Assistant Professor of Computer Science

Jeff Huang
Assistant Professor of Computer Science

Vasileios Kemerlis
Assistant Professor of Computer Science

George D. Konidaris
Assistant Professor of Computer Science

Daniel C. Ritchie
Assistant Professor of Computer Science

Stefanie A. Tellex
Joukowsky Family Assistant Professor of Computer Science

James H. Tompkin
Assistant Professor of Computer Science

Paul A. Valiant
Assistant Professor of Computer Science

Assistant Professor of the Practice

Ian Gonsher
Assistant Professor of the Practice of Engineering and Computer Science

Senior Lecturer

Caroline J. Klivans
Senior Lecturer in Applied Mathematics and Computer Science

Barbara J. Meier
Senior Lecturer in Computer Science

Adjunct Professor

Michael J. Black
Adjunct Professor of Computer Science

Adjunct Professor of the Practice

Alan M. Usas
Adjunct Professor of the Practice of Computer Science

Adjunct Associate Professor

Tim Klas Kraska
Adjunct Associate Professor of Computer Science

Adjunct Associate Professor Research

Carsten Binnig
Adjunct Associate Professor of Computer Science (Research)

Adjunct Assistant Professor

John H. Jannotti
Adjunct Assistant Professor of Computer Science

Visiting Scientist

Mark E. Nadel
Visiting Scientist in Computer Science

Thomas A. Sgouros
Visiting Scientist in Computer Science

Senior Research Associate

Lok Sang Lawson Wong
Senior Research Associate in Computer Science

Data Science

Master of Science in Data Science

The Data Science Initiative at Brown offers a new master's program (ScM) 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 (Applied Mathematics, Biostatistics, Computer Science, and Mathematics), 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. Moreover, our strong industry partnerships will help you better learn about industry's needs and directions, and will expose you to novel and unique opportunities. In addition, several professors from all across the different department's groups work closely with industry (regional and beyond) and the government, so you will be able to sharpen your skills here on problems that bring research ideas and methods to bear on problems of practical value.

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 deeper theoretical exploration, and
1 credit capstone experience.
We also offer an option as a 5-th Year Master's Program if you are an undergraduate at Brown. This allows you to substitute maximally 2 credits with courses you have already taken.

Master of Science in Data Science

Semester I
DATA 1010 An Introduction to Topics in Probability, Statistics, and Machine Learning2
DATA 1030Introduction to Topics in Data and Computation Science2
Semester II
DATA 2020Probability, Statistics and Machine Learning: Advanced Methods1
DATA 2040Data and Computational Science, Advanced Methods1
DATA 2060Data Science and Society1
An appropriate 1000-level or 2000-level course to be determined by the student and approved by the program advisor. Possible courses could range from advanced mathematical methods to very specific applications of data science.1
Summer
DATA 2050Capstone Project 11
Total Credits9

For more information on admission and program requirements, please visit the following website:

https://www.brown.edu/academics/gradschool/programs/data-science