Be sure to review the program requirements overview.

Only courses on this page are approved to satisfy the requirements of the MSE in Data Science.  Approval must be granted by the Oversight Committee for any course not listed on this page.  To request a course be added to this list, please send an email to Lisa Wetzelberger with the course number, name, and description.

Introduction to Data Science (required)

Offered fall and spring semesters
EN.553.636 Introduction to Data Science

Core Areas

One course in each of the four Core Areas below.  Courses chosen in this section must be distinct from the courses used to satisfy from the Electives section.  Courses in italics are recommended.

Course # Dept Course Title Semester Typically Offered
EN.553.613 AMS Applied Statistics and Data Analysis F
EN.553.614 AMS Applied Statistics and Data Analysis II S
EN.553.630 AMS Introduction to Statistics F/S
EN.553.632 AMS Bayesian Statistics F
EN.553.639 AMS Time Series Analysis S
EN.553.730 AMS Statistical Theory I F
EN.553.731 AMS Statistical Theory II S
EN.553.733 AMS Advanced Topics in Bayesian Analysis S
EN.553.735 AMS Topics in Statistical Pattern Recognition (EN.553.735) F
EN.553.738 AMS High-Dimensional Approximation, Probability, and Statistical Learning (EN.553.738) S
EN.553.739 AMS Statistical Pattern Recognition Theory & Methods (EN.553.739) S
EN.570.654 EHE Geostatistics:  Understanding Spatial Data S
EN.601.677 CS Causal Inference F

Course # Dept Course Title Semester Typically Offered
EN.520.612 ECE Machine Learning for Signal Processing (EN.520.612) F
EN.520.637 ECE Foundations of Reinforcement Learning F
EN.520.638 ECE Deep Learning S
EN.520.647 ECE Information Theory F
EN.520.648 ECE Compressed Sensing and Sparse Recovery S
EN.520.651 ECE Random Signal Analysis (EN.520.651) F
EN.520.666 ECE Information Extraction (EN.520.666) S
EN.525.724 EP-ECE Introduction to Pattern Recognition (online) F
EN.535.741 EP-ME Topics in Data Analysis S
EN.553.602 AMS Research and Design in Applied Mathematics:  Data Mining (EN.553.602) S
EN.553.738 AMS High-Dimensional Approximation, Probability, and Statistical Learning (EN.553.738) S
EN.553.740 AMS Machine Learning I (EN.553.740) F
EN.553.741 AMS Machine Learning II (EN.553.741) S
EN.570.654 EHE Geostatistics:  Understanding Spatial Data S
EN.601.634 CS Randomized and Big Data Algorithms F
EN.601.674 CS Machine Learning: Learning Theory F
EN.601.675 CS Machine Learning F/S
EN.601.676 CS Machine Learning: Data to Models S
EN.601.677 CS Causal Inference F
EN.601.679 CS Machine Learning: Representation Learning
EN.601.682 CS Machine Learning: Deep Learning F
EN.601.780 CS Unsupervised Learning: Big Data to Low-Dimensional Representations F
EN.625.692 EP-ACM, EP-DS Probabilistic Graphical Models S

Course # Dept Course Title Semester Typically Offered
EN.520.618 ECE Modern Convex Optimization F
EN.553.665 AMS Introduction to Convexity (EN.553.665) F
EN.553.761 AMS Nonlinear Optimization I (EN.553.761) F
EN.553.762 AMS Nonlinear Optimization II (EN.553.762) S
EN.553.763 AMS Stochastic Search and Optimization (EN.553.763) S
EN.553.765 AMS Convex Optimization (Not Currently Offered)
EN.553.766 AMS Combinatorial Optimization (EN.553.766) S
EN.601.681 CS Machine Learning: Optimization S
EN.625.615 EP-ACM, EP-DS Introduction to Optimization

Course # Dept Course Title Semester Typically Offered
EN.520.617 ECE Computation for Engineers S
EN.553.688 AMS Computing for Applied Mathematics (EN.553.688) F/S
EN.601.619 CS Cloud Computing
EN.601.620 CS Parallel Programming F
EN.601.633 CS Intro Algorithms F/S
EN.601.646 CS Sketching and Indexing for Sequence S
EN.601.647 CS Computation Genomics: Sequences F

Electives

Four additional courses.  Courses listed in the core areas may be taken to complete this requirement, provided that they are not double counted.  The following provide additional options, grouped into categories (but the chosen courses may be taken from different categories).

Semester offered information is based on past offering of the courses and may be subject to changes in department programs in future years.

Course # Dept Course Title Semester Typically Offered
AS.410.633 Biotech Introduction to Bioinformatics (AS.410.633), F/S
AS.410.635 Biotech Bioinformatics: Tools for Genome Analysis (AS.410.635) F/S
AS.410.661 Biotech Methods in Proteomics (AS.410.661)
AS.410.671 Biotech Gene Expression Data Analysis and Visualization (AS.410.671), F
EN.520.659 ECE Machine Learning for Medical Application S
EN.553.650 AMS Computational Molecular Medicine (EN.553.650) S
EN.580.688 BME Foundations of Computational Biology and Bioinformatics S
EN.605.620 EP-CS Algorithms for Bioinformatics F/S
EN.605.621 EP-CS Foundations of Algorithms F/S
EN.605.653 EP-CS Computational Genomics F
EN.605.754 EP-CS Analysis of Gene Expression and High-Content Biological Data

Course # Dept Course Title Semester Typically Offered
EN.520.614 ECE Image Processing and Analysis F/S
EN.520.615 ECE Image Processing and Analysis II S
EN.520.623 ECE Medical Image Analysis S
EN.520.646 ECE Wavelets & Filter Banks F
EN.520.648 ECE Compressed Sensing and Sparse Recovery S
EN.525.733 EP-ECE Deep Learning for Computer Vision (online) S
EN.553.693 AMS Mathematical Image Analysis S
EN.601.661 CS Computer Vision F/S
EN.601.783 CS Vision as Bayesian Inference S

Course # Dept Course Title Semester Typically Offered
EN.553.627 AMS Stochastic Processes in Finance I (EN.553.627) F
EN.553.628 AMS Stochastic Processes in Finance II (EN.553.628) S
EN.553.641 AMS Equity Markets and Quantitative Trading (EN.553.641) S
EN.553.642 AMS Investment Science (EN.553.642) F
EN.553.644 AMS Introduction to Financial Derivatives (EN.553.644) F
EN.553.645 AMS Interest Rate and Credit Derivatives (EN.553.645) S
EN.553.646 AMS Risk Measurement and Management in Financial Markets (EN.553.646) F
EN.553.647 AMS Quantitative Portfolio Theory & Performance Analysis (EN.553.647) S
EN.553.648 AMS Financial Engineering and Structured Products (EN.553.648) S
EN.553.649 AMS Advanced Equity Derivatives F
EN.553.753 AMS Commodities and Commodity Markets (EN.553.753) S
PH.140.644 Biostatistics Statistical Machine Learning: Methods, Theory, and Applications

Course # Dept Course Title Semester Typically Offered
EN.553.633 AMS Monte Carlo Methods (EN.553.633) F
EN.553.738 AMS High-Dimensional Approximation, Probability, and Statistical Learning (EN.553.738) S
EN.553.740 AMS Machine Learning I (EN.553.740) F
EN.553.741 AMS Machine Learning II (EN.553.741) S
EN.553.792 AMS Matrix Analysis (EN.553.792) F
EN.601.634 CS Randomized and Big Data Algorithms F

Course # Dept Course Title Semester Typically Offered
EN.520.666 ECE Information Extraction (EN.520.666) S
EN.520.680 ECE Speech and Auditory Processing by Humans and Machines (EN.520.680) S
EN.601.665 CS Natural Language Processing F
EN.601.769 CS Event Semantics in Theory and Practice S

Course # Dept Course Title Semester Typically Offered
EN.520.650 ECE Machine Intelligence (EN.520.650) S
EN.580.691 BME Learning, Estimation and Control
EN.601.615 CS Databases
EN.601.663* CS Algorithms for Sensor-Based Robotics
EN.601.666 CS Information Retrieval and Web Agents
EN.650.683 ISI Cybersecurity Risk Management S
*Please note the recommended pre-requisite course of EN.601.226 before registering for this course.

Data Science Capstone Experience

For details and instructions, click here.