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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 1010. An Introduction to Topics in Probability, Statistics, and Machine Learning.

After a fast-paced review of fundamentals the course will focus principally on four topics: random number generators and their applications (including Monte Carlo integration and importance sampling); high-dimensional data analysis (clustering, projections, principle and independent component analysis, multiple hypothesis testing and false discovery); regression, density estimation, and classification (linear, semi-parametric, and non-parametric, bias and variance, bootstrapping, cross validation); graphical models and exponential families (latent variables, dynamic programming, belief propagation, hidden Markov models, Markov chain Monte Carlo, expectation/maximization). Assignments will include a mix of analytic problems and computational experiments.

Fall DATA1010 S01 17067 MWF 1:00-2:00(06) (S. Geman)
Fall DATA1010 S01 17067 MWF 11:00-12:00(06) (S. Geman)
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.

Fall DATA1030 S01 17190 TTh 8:30-10:20(03) (T. Kraska)
Fall DATA1030 L01 17449 TTh 12:00-2:00 'To Be Arranged'
Course usage information

DATA 2040. Data and Computational Science.

Advanced Methods in Data and Computational Science. Includes topics such as data mining, computational statistics, machine learning and predictive modeling, and big data analytics algorithms.

Spr DATA2040 S01 26321 WF 1:00-2:20 (E. Upfal)

Professor

Mark Ainsworth
Francis Wayland Professor of Applied Mathematics

R. Bahar
Professor of Engineering; Professor of Computer Science

Frederic E. Bisshopp
Professor Emeritus of Applied Mathematics

Ugur Cetintemel
Professor of Computer Science

Eugene Charniak
University Professor of Computer Science

Constantine Michael Dafermos
Alumni-Alumnae University Professor of Applied Mathematics

Philip J. Davis
Professor Emeritus of Applied Mathematics

Hongjie Dong
Professor of Applied Mathematics

Paul G. Dupuis
IBM Professor of Applied Mathematics

Peter L. Falb
Professor Emeritus of Applied Mathematics

Wendell H. Fleming
University Professor Emeritus, Professor Emeritus of Applied Mathematics and Mathematics

Walter F. Freiberger
Professor Emeritus of Applied Mathematics

Stuart Geman
James Manning Professor of Applied Mathematics

Basilis Gidas
Professor of Applied Mathematics

Yan Guo
Professor of Applied Mathematics

Maurice P. Herlihy
An Wang Professor of Computer Science

Din-Yu Hsieh
Professor Emeritus of Applied Mathematics

John F. Hughes
Professor of Computer Science

Sorin Istrail
Julie Nguyen Brown Professor of Computational and Mathematical Science

George E. Karniadakis
Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics

Philip Klein
Professor of Computer Science

Shriram Krishnamurthi
Professor of Computer Science

Harold J. Kushner
Professor Emeritus of Applied Mathematics and Engineering and L. Herbert Ballou University Professor Emeritus

David H. Laidlaw
Professor of Computer Science

Charles Lawrence
Professor of Applied Mathematics

Michael L. Littman
Professor of Computer Science

Anna A. Lysyanskaya
Professor of Computer Science

John Mallet-Paret
George Ide Chase Professor of Physical Science

Martin R. Maxey
Professor of Applied Mathematics; Professor of Engineering

Donald E. McClure
Professor Emeritus of Applied Mathematics

Govind Menon
Professor of Applied Mathematics

George Morgan
Professor Emeritus of Applied Mathematics

David Mumford
Professor Emeritus of Applied Mathematics

Kavita Ramanan
Professor of Applied Mathematics

Steven P. Reiss
Professor of Computer Science

Boris L. Rozovsky
Ford Foundation Professor of Applied Mathematics

Bjorn Sandstede
Royce Family Professor of Teaching Excellence

John E. Savage
An Wang Professor of Computer Science

Chi-Wang Shu
Stowell University Professor of Applied Mathematics

Lawrence Sirovich
Professor Emeritus of Applied Mathematics

Walter A. Strauss
L. Herbert Ballou University Professor of Mathematics and Applied Mathematics

Chau-Hsing Su
Professor Emeritus of Applied Mathematics

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)

Visiting Professor

Nathan A. Baker
Visiting Professor of Applied Mathematics

Jose Antonio Carrillo de la Plata
IBM Visiting Professor of Applied Mathematics

Vladimir Dobrushkin
Visiting Professor of Applied Mathematics

Yvon Jean Maday
Visiting Professor of Applied Mathematics

Homer F. Walker
Visiting Professor of Applied Mathematics

Acting Professor

Franco Preparata
An Wang Professor Emeritus of Computer Science

Associate Professor

Lucien J. E. Bienenstock
Associate Professor of Applied Mathematics; Associate Professor of Neuroscience

Amy R. Greenwald
Associate Professor of Computer Science

Johnny Guzman
Associate Professor of Applied Mathematics

Matthew T. Harrison
Associate Professor of Applied Mathematics

Seny F. Kamara
Associate Professor of Computer Science

Rodrigo Fonseca
Associate Professor of Computer Science

Sohini Ramachandran
Associate Professor of Ecology and Evolutionary Biology and Computer Science

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

Hui Wang
Associate Professor of Applied Mathematics

Associate Professor Research

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

Xuejin Li
Associate Professor of Applied Mathematics (Research); Assistant Professor of Applied Mathematics (Research)

Visiting Associate Professor

Benjamin J. Raphael
Visiting Associate Professor of Computer Science

Assistant Professor

Sona Akopian
Prager Assistant Professor of Applied Mathematics

Jerome B. Darbon
Assistant Professor of Applied Mathematics

Guosheng Fu
Prager Assistant Professor of Applied Mathematics

Nicolas Garcia Trillos
Prager Assistant Professor of Applied Mathematics

Jeff Huang
Assistant Professor of Computer Science

Vasileios Kemerlis
Assistant Professor of Computer Science

George D. Konidaris
Assistant Professor of Computer Science

Tim Klas Kraska
Assistant Professor of Computer Science

Benjamin S. Kunsberg
Prager Assistant Professor of Applied Mathematics

Anastasios Matzavinos
Assistant Professor of Applied Mathematics

Daniel C. Ritchie
Assistant Professor of Computer Science

Stefanie A. Tellex
Assistant Professor of Computer Science

James H. Tompkin
Assistant Professor of Computer Science

Paul A. Valiant
Assistant Professor of Computer Science

Assistant Professor Research

Zhen Li
Assistant Professor of Applied Mathematics (Research)

Alireza Zarif Khalili Yazdani
Assistant Professor of Applied Mathematics (Research)

Visiting Assistant Professor

Joseph G. Politz
Visiting Assistant Professor of Computer Science (Research)

William Thompson
Visiting Assistant Professor of Applied Mathematics

Visiting Assistant Professor Research

Benjamin S. Lerner
Visiting Assistant Professor of Computer Science (Research)

Daniele Venturi
Visiting Assistant Professor of Applied Mathematics (Research)

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

Thomas L. Dean
Adjunct Professor of Computer Science

Donald L. Stanford
Adjunct Professor of Computer Science

Alan M. Usas
Adjunct Professor of Computer Science

Adjunct Professor of the Practice

Linn F. Freedman
Adjunct Professor of the Practice of Computer Science

Adjunct Associate Professor

Joseph J. Laviola
Adjunct Associate Professor of Computer Science

Daniel F. Potter
Adjunct Associate Professor of Computer Science

Adjunct Associate Professor Research

Erik B. Sudderth
Adjunct Associate Professor of Computer Science (Research)

Adjunct Assistant Professor

Gelonia L. Dent
Adjunct Assistant Professor of Applied Mathematics

John H. Jannotti
Adjunct Assistant Professor of Computer Science

Adjunct Assistant Professor Research

Bruce D. Campbell
Adjunct Assistant Professor of Computer Science (Research)

Adjunct Lecturer

Roger B. Blumberg
Adjunct Lecturer in Computer Science

Visiting Scholar

Seok Hyun Hong
Visiting Scholar in Applied Mathematics

Chunyan Huang
Visiting Scholar in Applied Mathematics

Seick Kim
Visiting Scholar in Applied Mathematics

Xueke Pu
Visiting Scholar in Applied Mathematics

Matteo Riondato
Visiting Scholar in Computer Science

Lian Yang
Visiting Scholar in Applied Mathematics

Visiting Scientist

Elizabeth J. Makrides
Visiting Scientist in Applied Mathematics

Mark E. Nadel
Visiting Scientist in Computer Science

Paris G. Perdikaris
Visiting Scientist in Applied Mathematics

Thomas A. Sgouros
Visiting Scientist in Computer Science

Kyongmin Yeo
Visiting Scientist in Applied Mathematics

Senior Research Associate

Tim Nelson
Senior Research Associate in Computer Science

Research Associate

Daniel J. Milstein
Research Associate in Computer Science

Tarik Moataz
Research Associate in Computer Science

Ellie Pavlick
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 1010An 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
1

 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.

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

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