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/

**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) |

**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' |

**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 1010 | An Introduction to Topics in Probability, Statistics, and Machine Learning | 2 |

DATA 1030 | Introduction to Topics in Data and Computation Science | 2 |

Semester II | ||

DATA 2020 | Probability, Statistics and Machine Learning: Advanced Methods | 1 |

DATA 2040 | Data and Computational Science, Advanced Methods | 1 |

DATA 2060 | Data Science and Society | 1 |

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 2050 | Capstone Project ^{1} | 1 |

Total Credits | 9 |

^{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