Data Science Institute


The curriculum for the Data Science Master's Program consists of nine credits: eight required courses, one of which is the experiential project course, and one elective.

The program can be completed in 12 months (September to August). All students begin the program in September; there is no option for starting in the spring semester. Students may elect to complete the program over 16, 21, or 24 months, and most do so. In some cases, exceptionally well-prepared students might be able complete their work in nine months.

Fifth-year masters students must complete the program in one year (September to August). 

For students taking longer than 12 months, full-time status for visa purposes is two credits per semester (and only one credit in the final semester). For fifth-year master's students, full-time status is three credits per semester. 

Required Courses

Develops all aspects of the machine learning pipeline: data acquisition and cleaning, handling missing data, exploratory data analysis, visualization, feature engineering, modeling, interpretation, presentation in the context of real-world datasets. Fundamental considerations for data analysis are emphasized (the bias-variance tradeoff, training, validation, testing). Classical models and techniques for classification and regression are included (linear and logistic regression with regularization, support vector machines, decision trees, random forests, XGBoost). Uses the Python data science ecosystem (e.g., sklearn, pandas, matplotlib).

This course covers the storage, retrieval, and management of various types of data and the computing infrastructure (such as various types of databases and data structures) and algorithmic techniques (such as searching and sorting algorithms) and query languages (such as SQL) for interacting with data, both in the context of transaction processing (OLTP) and analytical processing (OLAP). Students will be introduced to measures for evaluating the efficacy of different techniques for interacting with data (such as ‘Big-Oh’ measure of complexity and the number of I/O operations) and various types of indexes for the efficient retrieval of data. The course will also cover several components of the Hadoop ecosystem for the processing of "big data." Additional topics include cloud computing, NoSQL databases, and modern data architectures. Introduction to some of the concepts and techniques of computer science essential for data science will also be covered.

APMA 1690. Computational Probability and Statistics

Examination of probability theory and mathematical statistics from the perspective of computing. Topics selected from random number generation, Monte Carlo methods, limit theorems, stochastic dependence, Bayesian networks, dimensionality reduction. Prerequisites: APMA 1650 or equivalent; programming experience is recommended.

CSCI 1450. Advanced Introduction to Probability for Computing and Data Science

Probability and statistics have become indispensable tools in computer science. Probabilistic methods and statistical reasoning play major roles in machine learning, cryptography, network security, communication protocols, web search engines, robotics, program verification, and more. This course introduces the basic concepts of probability and statistics, focusing on topics that are most useful in computer science applications. Topics include: modeling and solution in sample space, random variables, simple random processes and their probability distributions, Markov processes, limit theorems, and basic elements of Bayesian and frequentist statistical inference. Basic programming experience required for homework assignments.

A modern introduction to inferential methods for regression analysis and statistical learning, with an emphasis on application in practical settings in the context of learning relationships from observed data. Topics will include basics of linear regression, variable selection and dimension reduction, and approaches to nonlinear regression. Extensions to other data structures such as longitudinal data and the fundamentals of causal inference will also be introduced.

We know we want to build more equitable technology, but how? In this course we’ll review the latest developments in how to build more equitable algorithms, including definitions of (un)fairness, the challenges of explaining how ML works, making sure we can get accountability, and much more.

Deep Learning belongs to a broader family of machine learning methods. It is a particular version of artificial neural networks that emphasizes learning representation with multiple layers of networks. Deep Learning, plus the specialized techniques that it has inspired (e.g. convolutional neural networks, recurrent neural networks, and transformers), have led to rapid improvements in many applications, such as computer vision, machine learning, sound understanding, and robotics. This course gives students an overview of the prominent techniques of Deep Learning and its applications in computer vision, language understanding, and other areas. It also provides hands-on practice of implementing deep learning algorithms in Python. A final project will implement an advanced piece of work in one of these areas.

DATA 2060. Machine Learning

Data science techniques and tools are all around us. Machine learning is a term used across many different disciplines, and often people use machine learning tools without a thorough understanding of how and why the tools work. This course will provide students with a foundation of machine learning grounded in the mathematical models behind the techniques. The course will cover the theory, computational methods, and visualization inherent in the application of machine learning models. In this course, you will learn the statistical learning framework, common assumptions in the data generation process, the mathematics behind machine learning models, including supervised and unsupervised techniques, as well as how to implement machine learning models in Python from scratch.

CSCI 1420. Machine Learning

How can artificial systems learn from examples and discover information buried in data? We explore the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised learning. Specific topics include empirical risk minimization, probably approximately correct learning, kernel methods, neural networks, maximum likelihood estimation, the expectation maximization algorithm, and principal component analysis. This course also aims to expose students to relevant ethical and societal considerations related to machine learning that may arise in practice. 

The practicum experience is a hands-on thesis project that entails an in-depth study of a current problem in data science. Students will synthesize their knowledge of probability and statistics, machine learning, and data and computational science. Students will work in teams on projects with Brown faculty members or with external companies. The project will be completed as part of a course that includes additional career-oriented skills development.  

See examples

Domain knowledge relevant to individual interest, 1 credit, must be a graduate level course with 4-digit course number starting with a non-0 digit. Most graduate level CSCI and APMA courses qualify. Please contact the DGS if you plan to take a course from a different department.