Program Requirements
The 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.
| Code | Title | Credits |
|---|---|---|
| Core Requirements | ||
| EN.553.636 | Introduction to Data Science | 4.0 |
| Core Areas | ||
| Select one course in each of the four Core Areas: | 12 - 16 | |
| ==Statistics== | ||
| EN.553.613 | Applied Statistics and Data Analysis | 4 |
| EN.553.614 | Applied Statistics and Data Analysis II | 3 |
| EN.553.630 | Mathematical Statistics (NOTE: EN.553.630 may not be taken after EN.553.730.) | 4 |
| EN.553.632 | Bayesian Statistics | 3 |
| EN.553.639 | Time Series Analysis | 3 |
| EN.553.730 | Statistical Theory | 4 |
| EN.553.731 | Statistical Theory II | 3 |
| EN.553.733 | Nonparametric Bayesian Statistics | 3 |
| EN.553.735 | Topics in Statistical Pattern Recognition | 3 |
| EN.553.738 | High-Dimensional Approximation, Probability, and Statistical Learning | 3 |
| EN.553.739 | Statistical Pattern Recognition Theory & Methods | 3 |
| EN.570.654 | Geostatistics: Understanding Spatial Data | 3 |
| EN.601.677 | Causal Inference | 3 |
| EN.625.603 | Statistical Methods and Data Analysis | 3 |
| EN.625.664 | Computational Statistics | 3 |
| ==Machine Learning== | ||
| EN.520.612 | Machine Learning for Signal Processing | 3 |
| EN.520.637 | Foundations of Reinforcement Learning | 3 |
| EN.520.638 | Deep Learning | 3 |
| EN.520.647 | Information Theory | 3 |
| EN.520.648 | Compressed Sensing and Sparse Recovery | 3 |
| EN.520.651 | Random Signal Analysis | 4 |
| EN.520.666 | Information Extraction | 3 |
| EN.525.724 | Introduction to Pattern Recognition | 3 |
| EN.530.641 | Statistical Learning For Engineers | 3 |
| EN.535.741 | Optimal Control and Reinforcement Learning | 3 |
| EN.553.602 | Research and Design in Applied Mathematics: Data Mining | 4 |
| EN.553.738 | High-Dimensional Approximation, Probability, and Statistical Learning | 3 |
| EN.553.740 | Machine Learning I | 3 |
| EN.553.741 | Machine Learning II | 3 |
| EN.553.743 | Equivariant Machine Learning | 3 |
| EN.570.654 | Geostatistics: Understanding Spatial Data | 3 |
| EN.601.634 | Randomized and Big Data Algorithms | 3 |
| EN.601.674 | ML: Learning Theory | 3 |
| EN.601.675 | Machine Learning | 3 |
| EN.601.676 | Machine Learning: Data to Models | 3 |
| EN.601.677 | Causal Inference | 3 |
| EN.601.682 | Machine Learning: Deep Learning | 4 |
| EN.601.779 | Machine Learning: Advanced Topics | 3 |
| EN.601.780 | Unsupervised Learning: From Big Data to Low-Dimensional Representations | 3 |
| EN.625.692 | Probabilistic Graphical Models | 3 |
| ==Optimization== | ||
| EN.520.618 | Modern Convex Optimization | 3 |
| EN.553.653 | Mathematical Game Theory | 4 |
| EN.553.662 | Optimization for Data Science | 3 |
| EN.553.665 | Introduction to Convexity | 4 |
| EN.553.669 | Large-Scale Optimization For Data Science | 3 |
| EN.553.761 | Nonlinear Optimization I | 3 |
| EN.553.762 | Nonlinear Optimization II | 3 |
| EN.553.763 | Stochastic Search & Optimization | 3 |
| EN.553.766 | Combinatorial Optimization | 3 |
| EN.553.797 | Introduction to Control Theory and Optimal Control | 3 |
| EN.625.615 | Introduction to Optimization | 3 |
| ==Computing== | ||
| EN.520.617 | Computation for Engineers | 3 |
| EN.553.688 | Computing for Applied Mathematics | 3 |
| EN.601.619 | Cloud Computing | 3 |
| EN.601.620 | Parallel Computing for Data Science | 3 |
| EN.601.633 | Intro Algorithms | 3 |
| EN.601.646 | Sketching and Indexing for Sequences | 3 |
| EN.601.647 | Computational Genomics: Sequences | 3 |
| EN.625.664 | Computational Statistics | 3 |
| EN.685.621 | Algorithms for Data Science | 3 |
| 4 Additional Courses | ||
| Courses 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.410.633 | Introduction to Bioinformatics | 4 |
| AS.410.635 | Bioinformatics: Tools for Genome Analysis | 4 |
| AS.410.671 | Gene Expression Data Analysis and Visualization | 4 |
| EN.520.659 | Machine learning for medical applications | 3 |
| EN.553.650 | Computational Molecular Medicine | 4 |
| EN.580.688 | Foundations of Computational Biology and Bioinformatics | 3 |
| EN.601.651 | Introduction to Computational Immunogenomics | 3 |
| EN.605.620 | Algorithms for Bioinformatics | 3 |
| or EN.605.621 | Foundations of Algorithms | |
| EN.605.653 | Computational Genomics | 3 |
| ==Computer Vision== | ||
| EN.520.614 | Image Processing & Analysis | 3 |
| EN.520.615 | Image Processing & Analysis II | 3 |
| EN.520.623 | Medical Image Analysis | 3 |
| EN.520.635 | Digital Signal Processing | 3 |
| EN.520.646 | Wavelets & Filter Banks | 3 |
| EN.520.648 | Compressed Sensing and Sparse Recovery | 3 |
| EN.525.733 | Deep Learning for Computer Vision | 3 |
| EN.553.693 | Mathematical Image Analysis | 4 |
| EN.601.661 | Computer Vision | 3 |
| EN.601.783 | Vision as Bayesian Inference | 3 |
| EN.605.626 | Image Processing | 3 |
| ==Mathematical Finance== | ||
| EN.553.627 | Stochastic Processes and Applications to Finance | 4 |
| EN.553.628 | Stochastic Processes and Applications to Finance II | 4 |
| EN.553.641 | Equity Markets and Quantitative Trading | 3 |
| EN.553.642 | Investment Science | 4 |
| EN.553.644 | Introduction to Financial Derivatives | 4 |
| EN.553.645 | Interest Rate and Credit Derivatives | 4 |
| EN.553.646 | Risk Measurement/Management in Financial Markets | 4 |
| EN.553.647 | Quantitative Portfolio Theory and Performance Analysis | 4 |
| EN.553.648 | Financial Engineering and Structured Products | 4 |
| EN.553.649 | Advanced Equity Derivatives | 4 |
| EN.553.743 | Equivariant Machine Learning | 3 |
| EN.553.753 | Commodity Markets and Green Energy Finance | 4 |
| ==Mathematics of Data Science== | ||
| EN.553.633 | Monte Carlo Methods | 4 |
| EN.553.738 | High-Dimensional Approximation, Probability, and Statistical Learning | 3 |
| EN.553.740 | Machine Learning I | 3 |
| EN.553.741 | Machine Learning II | 3 |
| EN.553.792 | Matrix Analysis and Linear Algebra | 4 |
| EN.601.634 | Randomized and Big Data Algorithms | 3 |
| ==Language and Speech== | ||
| EN.520.666 | Information Extraction | 3 |
| EN.520.680 | Speech and Auditory Processing by Humans and Machines | 3 |
| EN.601.665 | Natural Language Processing | 3 |
| EN.601.668 | Machine Translation | 3 |
| EN.601.671 | Natural Language Processing: Self-Supervised Models | 3 |
| EN.601.769 | Events Semantics in Theory and Practice | 3 |
| ==Additional Courses== | ||
| EN.520.640 | Machine Intelligence on Embedded Systems | 3 |
| EN.520.650 | Machine Intelligence | 3 |
| EN.520.665 | Machine Perception | 3 |
| EN.553.653 | Mathematical Game Theory | 4 |
| EN.580.691 | Learning, Estimation and Control | 3 |
| EN.601.615 | Databases | 3 |
| EN.601.642 | Modern Cryptography | 3 |
| EN.601.663 | Algorithms for Sensor-Based Robotics (Recommended pre-requisite EN.601.226) | 3 |
| EN.601.664 | Artificial Intelligence | 3 |
| EN.601.666 | Information Retrieval and Web Agents | 3 |
| EN.650.683 | Cybersecurity Risk Management | 3 |
| Capstone Experience | ||
| EN.553.806 | Capstone Experience in Data Science | 3 - 10 |
In 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 by Coursera
- The communication skills requirement (Communication Skills Practicum)
- Course on Responsible Conduct of Research
- University Orientation and Academic Ethics
Additional 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).