Be sure to review the program requirements overview.

Request a course be added to the approved list.

Registering for a course taught by another school.

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 Mathematical Statistics (formerly 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 F
EN.553.738 AMS High-Dimensional Approximation, Probability, and Statistical Learning S
EN.553.739 AMS Statistical Pattern Recognition Theory & Methods S
EN.570.654 EHE Geostatistics:  Understanding Spatial Data S
EN.601.677 CS Causal Inference F
EN.625.603 EP-ACM, EP-DS Statistical Methods and Data Analysis S
EN.625.664 EP-ACM, EP-DS Computational Statistics

Course # Dept Course Title Semester Typically Offered
EN.520.612 ECE Machine Learning for Signal Processing 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 F
EN.520.666 ECE Information Extraction S
EN.525.724 EP-ECE Introduction to Pattern Recognition (online) F
EN.530.641 ME Statistical Learning for Engineers F
EN.535.741 EP-ME Topics in Data Analysis S
EN.553.602 AMS Research and Design in Applied Mathematics:  Data Mining S
EN.553.724 AMS Probabilistic Machine Learning S
EN.553.738 AMS High-Dimensional Approximation, Probability, and Statistical Learning S
EN.553.740 AMS Machine Learning I F
EN.553.741 AMS Machine Learning II S
EN.553.743 AMS Equivariant Machine Learning F
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.601.682 CS Machine Learning: Deep Learning F
EN.601.779 CS Machine Learning: Advanced Topics S
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.553.653 AMS Mathematical Game Theory
EN.553.661 AMS Optimization in Finance
EN.553.662 AMS Optimization in Data Science S
EN.553.665 AMS Introduction to Convexity
EN.553.669 AMS Large-Scale Optimization for Data Science
EN.553.761 AMS Nonlinear Optimization I F
EN.553.762 AMS Nonlinear Optimization II S
EN.553.763 AMS Stochastic Search and Optimization S
EN.553.766 AMS Combinatorial Optimization S
EN.553.797 AMS Introduction to Control Theory and Optimal Control S
EN.625.615 EP-ACM, EP-DS Introduction to Optimization S

Course # Dept Course Title Semester Typically Offered
EN.520.617 ECE Computation for Engineers S
EN.553.688 AMS Computing for Applied Mathematics 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
EN.625.664 EP-ACM, EP-DS Computational Statistics
EN.685.621 EP-DS, EP-AI Algorithms for Data Science S

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 F/S
AS.410.635 Biotech Bioinformatics: Tools for Genome Analysis F/S
AS.410.671 Biotech Gene Expression Data Analysis and Visualization F
EN.520.659 ECE Machine Learning for Medical Application S
EN.553.650 AMS Computational Molecular Medicine S
EN.580.688 BME Foundations of Computational Biology and Bioinformatics S
EN.601.651 CS Introduction to Computational Immunogenetics F
EN.605.620 or EN.605.621 EP-CS Algorithms for Bioinformatics F/S
EN.605.621 or EN.605.620 EP-CS Foundations of Algorithms F/S
EN.605.653 EP-CS Computational Genomics F

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.520.635 ECE Digital Signal Processing
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
EN.605.626 EP-CS Image Processing F

Course # Dept Course Title Semester Typically Offered
EN.553.627 AMS Stochastic Processes in Finance I F
EN.553.628 AMS Stochastic Processes in Finance II S
EN.553.641 AMS Equity Markets and Quantitative Trading S
EN.553.642 AMS Investment Science F
EN.553.644 AMS Introduction to Financial Derivatives F
EN.553.645 AMS Interest Rate and Credit Derivatives S
EN.553.646 AMS Risk Measurement and Management in Financial Markets F
EN.553.647 AMS Quantitative Portfolio Theory & Performance Analysis S
EN.553.648 AMS Financial Engineering and Structured Products S
EN.553.649 AMS Advanced Equity Derivatives F
EN.553.743 AMS Equivariant Machine Learning
EN.553.753 AMS Commodities and Commodity Markets S

Course # Dept Course Title Semester Typically Offered
EN.553.633 AMS Monte Carlo Methods F
EN.553.738 AMS High-Dimensional Approximation, Probability, and Statistical Learning S
EN.553.740 AMS Machine Learning I F
EN.553.741 AMS Machine Learning II S
EN.553.792 AMS Matrix Analysis 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 S
EN.601.665 CS Natural Language Processing F
EN.601.668 CS Machine Translation F
EN.601.671 CS Natural Language Processing:  Self-Supervised Models S
EN.601.769 CS Event Semantics in Theory and Practice S

Course # Dept Course Title Semester Typically Offered
EN.520.640 ECE Machine Intelligence on Embedded Systems S
EN.520.650 ECE Machine Intelligence S
EN.520.665 ECE Machine Perception F
EN.553.635 AMS Mathematical Game Theory S
EN.553.689 AMS Software Engineering for Data Science F
EN.580.691 BME Learning, Estimation and Control
EN.601.615 CS Databases
EN.601.642 CS Modern Cryptography F
EN.601.663* CS Algorithms for Sensor-Based Robotics
EN.601.664 CS Artificial Intelligence S
EN.601.666 CS Information Retrieval and Web Agents
EN.601.670 CS Artificial Agents F
EN.601.690 CS Introduction to Human-Computer Interaction S
EN.601.788 CS Machine Learning for Healthcare
EN.650.683 ISI Cybersecurity Risk Management S
*Please note the recommended pre-requisite course of EN.601.226 before registering for this course.

NOT APPROVED (or approved for electives only)

Course # Dept Course Title
EN.553.620 AMS Introduction to Probability
EN.553.767 AMS Iterative Algorithms in Machine Learning
EN.605.608 EP-CS Software Project Management
EN.605.631 EP-CS Statistical Methods for Computer Science
EN.605.635 EP-CS Cloud Computing
EN.625.604 EP-ACM Ordinary Differential Equations
EN.625.609 EP-ACM Matrix Theory
EN.625.636 EP-ACM Graph Theory
EN.705.601 PE-AI, PE-CS Applied Machine Learning

Course # Dept Course Title Decision
EN.601.665 CS Natural Language Processing NOT APPROVED AS CORE, BUT PERMITTED AS ELECTIVE.
EN.601.671 CS Self-Supervised Models NOT APPROVED AS CORE, BUT PERMITTED AS ELECTIVE

Data Science Capstone Experience

For details and instructions, click here.

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