Data Science, Master's Degree

Program Overview

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Program RequirementsThe Data Science Master's program is designed to be completed in three semesters of full-time graduate study. Please see our program website for the most current program requirements and information.Course ListCodeTitleCreditsCore RequirementsEN.553.636Introduction to Data Science4.0Core AreasSelect one course in each of the four Core Areas:12 - 16==Statistics==EN.553.613Applied Statistics and Data Analysis4EN.553.614Applied Statistics and Data Analysis II3EN.553.630Introduction to Statistics (NOTE:EN.553.630 may not be taken after EN.553.730.)4EN.553.632Bayesian Statistics3EN.553.639Time Series Analysis3EN.553.730Statistical Theory4EN.553.731Statistical Theory II3EN.553.733Nonparametric Bayesian Statistics3EN.553.735Topics in Statistical Pattern Recognition3EN.553.738High-Dimensional Approximation, Probability, and Statistical Learning3EN.553.739Statistical Pattern Recognition Theory & Methods3EN.570.654Geostatistics: Understanding Spatial Data3EN.601.677Causal Inference3EN.625.603Statistical Methods and Data Analysis3==Machine Learning==EN.520.612Machine Learning for Signal Processing3EN.520.637Foundations of Reinforcement Learning3EN.520.638Deep Learning3EN.520.647Information Theory3EN.520.648Compressed Sensing and Sparse Recovery3EN.520.651Foundations of Probabilistic Machine Learning4EN.520.666Information Extraction3EN.525.724Introduction to Pattern Recognition3EN.535.741Optimal Control and Reinforcement Learning3EN.553.602Research and Design in Applied Mathematics: Data Mining4EN.553.738High-Dimensional Approximation, Probability, and Statistical Learning3EN.553.740Machine Learning I3EN.553.741Machine Learning II3EN.570.654Geostatistics: Understanding Spatial Data3EN.601.634Randomized and Big Data Algorithms3EN.601.674ML: Learning Theory3EN.601.675Machine Learning3EN.601.676Machine Learning: Data to Models3EN.601.677Causal Inference3EN.601.682Machine Learning: Deep Learning4EN.601.779Machine Learning: Advanced Topics3EN.601.780Unsupervised Learning: From Big Data to Low-Dimensional Representations3EN.625.692Probabilistic Graphical Models3==Optimization==EN.520.618Modern Convex Optimization3EN.553.665Introduction to Convexity4EN.553.761Nonlinear Optimization I3EN.553.762Nonlinear Optimization II3EN.553.763Stochastic Search & Optimization3EN.553.766Combinatorial Optimization3EN.553.797Introduction to Control Theory and Optimal Control3EN.601.681Machine Learning: Optimization3EN.625.615Introduction to Optimization3==Computing==EN.520.617Computation for Engineers3EN.553.688Computing for Applied Mathematics3EN.601.619Cloud Computing3EN.601.620Parallel Computing for Data Science3EN.601.633Intro Algorithms3EN.601.646Sketching and Indexing for Sequences3EN.601.647Computational Genomics: Sequences3EN.685.621Algorithms for Data Science34 Additional CoursesCourses listed in the core areas may be taken to complete this requirement, provided they are not double-counted.The following provide additional options, grouped into categories (but the chosen courses may be taken from different categories).12-16==Computational Medicine==AS.410.633Introduction to Bioinformatics4AS.410.635Bioinformatics: Tools for Genome Analysis4AS.410.671Gene Expression Data Analysis and Visualization4EN.520.659Machine learning for medical applications3EN.553.650Computational Molecular Medicine4EN.580.688Foundations of Computational Biology and Bioinformatics3EN.605.620Algorithms for Bioinformatics3orEN.605.621 Foundations of AlgorithmsEN.601.621Object Oriented Software Engineering3EN.605.653Computational Genomics3==Computer Vision==EN.520.614Image Processing & Analysis3EN.520.615Image Processing & Analysis II3EN.520.623Medical Image Analysis3EN.520.646Wavelets & Filter Banks3EN.520.648Compressed Sensing and Sparse Recovery3EN.525.733Deep Learning for Computer Vision3EN.553.693Mathematical Image Analysis4EN.601.661Computer Vision3EN.601.783Vision as Bayesian Inference3==Mathematical Finance==EN.553.627Stochastic Processes and Applications to Finance4EN.553.628Stochastic Processes and Applications to Finance II4EN.553.641Equity Markets and Quantitative Trading3EN.553.642Investment Science4EN.553.644Introduction to Financial Derivatives4EN.553.645Interest Rate and Credit Derivatives4EN.553.646Risk Measurement/Management in Financial Markets4EN.553.647Quantitative Portfolio Theory and Performance Analysis4EN.553.648Financial Engineering and Structured Products4EN.553.649Advanced Equity Derivatives4EN.553.753Commodity Markets and Green Energy Finance4PH.140.644Statistical Machine Learning: Methods, Theory, and Applications4==Mathematics of Data Science==EN.553.633Monte Carlo Methods4EN.553.738High-Dimensional Approximation, Probability, and Statistical Learning3EN.553.740Machine Learning I3EN.553.741Machine Learning II3EN.553.792Matrix Analysis and Linear Algebra4EN.601.634Randomized and Big Data Algorithms3==Language and Speech==EN.520.666Information Extraction3EN.520.680Speech and Auditory Processing by Humans and Machines3EN.601.665Natural Language Processing3EN.601.668Machine Translation3EN.601.769Events Semantics in Theory and Practice3==Additional Courses==EN.520.640Machine Intelligence on Embedded Systems3EN.520.650Machine Intelligence3EN.520.665Machine Perception3EN.580.691Learning, Estimation and Control3EN.601.615Databases3EN.601.663Algorithms for Sensor-Based Robotics (Recommended pre-requisite EN.601.226)3EN.601.664Artificial Intelligence3EN.601.666Information Retrieval and Web Agents3EN.650.683Cybersecurity Risk Management3Capstone ExperienceEN.553.806Capstone Experience in Data Science3 - 10In addition to the above course requirements, all data science master's students will complete:An online Data Ethics course: Students must take an approved online data ethics course such as the one offered byCourseraThe communication skills requirement (Communication Skills Practicum)Course onResponsible Conduct of ResearchUniversity Orientation and Academic EthicsAdditional Notes:A course grade of B- or better is required to meet all course requirements. Consult the Department/Program website for additional information regarding Minimum Grade Requirements and the Academic Probation Policy.Courses cannot be double-counted for different requirements (even if they appear in several core areas).