数据科学概论（必选）
Introduction to Data Science
核心领域
以下四个核心领域各一门课程。在本节中选择的课程必须与用于满足选修课部分的课程不同。推荐使用斜体的课程。
统计数据
Applied Statistics and Data Analysis
Applied Statistics and Data Analysis II
Introduction to Statistics
Bayesian Statistics
Time Series Analysis
Statistical Theory I
Statistical Theory II
Advanced Topics in Bayesian Analysis
Topics in Statistical Pattern Recognition
High-Dimensional Approximation,Probability, and Statistical Learning
Statistical Pattern Recognition Theory &
Methods
Geostatistics: Understanding Spatial Data
Causal Inference
Statistical Methods and Data Analysis
Computational Statistics
机器学习
Machine Learning for Signal Processing
Foundations of Reinforcement Learning
Deep Learning
Information Theory
Compressed Sensing and Sparse Recovery
Random Signal Analysis
Information Extraction
Introduction to Pattern Recognition (online)
Topics in Data Analysis
Research and Design in Applied Mathematics: Data Mining
High-Dimensional Approximation, Probability, and Statistical Learning
Machine Learning I
Machine Learning II
Equivariant Machine Learning
Geostatistics: Understanding Spatial Data
Randomized and Big Data Algorithms
Machine Learning： Learning Theory
Machine Learning
Machine Learning: Data to Models
Causal Inference
Machine Learning： Deep Learning
Machine Learning: Advanced Topics
Unsupervised Learning: Big Data to Low-Dimensional Representations
Probabilistic Graphical Models
优化
Modern Convex Optimization
Mathematical Game Theory
Optimization in Finance
Optimization in Data Science
Introduction to Convexity
Large-Scale Optimization for Data Science
Nonlinear Optimization I
Nonlinear Optimization II
Stochastic Search and Optimization
Combinatorial Optimization
Introduction to Control Theory and Optimization Control
Machine Learning: Optimization
Introduction to Optimization
计算
Computation for Engineers
Computing for Applied Mathematics
Cloud Computing
Parallel Programming
Intro Algorithms
Sketching and Indexing for Sequence
Computation Genomics: Sequences
Computational Statistics
Algorithms for Data Science
选修课
四门选修课程。核心领域中列出的课程可用于完成此要求，前提是它们没有重复计算。以下提供了附加选项，按类别分组（但所选课程可能来自不同类别）。
学期提供的信息基于过去提供的课程，并且可能会在未来几年因部门计划而发生变化。
计算医学
Introduction to Bioinformatics
Bioinformatics： Tools for Genome Analysis
Gene Expression Data Analysis and Visualization
Machine Learning for Medical Application
Computational Molecular Medicine
Foundations of Computational Biology and Bioinformatics
Introduction to Computational Immunogenetics
Algorithms for Bioinformatics
Foundations of Algorithms
Computational Genomics
计算机视觉
Image Processing and Analysis
Image Processing and Analysis II
Medical Image Analysis
Digital Signal Processing
Wavelets & Filter Banks
Compressed Sensing and Sparse Recovery
Deep Learning for Computer Vision (online)
Mathematical Image Analysis
Computer Vision
Vision as Bayesian Inference
Image Processing
数学金融
Stochastic Processes in Finance I
Stochastic Processes in Finance II
Equity Markets and Quantitative Trading
Investment Science
Introduction to Financial Derivatives
Interest Rate and Credit Derivatives
Risk Measurement and Management in
Financial Markets
Quantitative Portfolio Theory & Performance Analysis
Financial Engineering and Structured Products
Advanced Equity Derivatives
Equivariant Machine Learning
Commodities and Commodity Markets
数据科学数学
Monte Carlo Methods
High-Dimensional Approximation, Probability, and Statistical Learning
Machine Learning I
Machine Learning II
Matrix Analysis
Randomized and Big Data Algorithms
语言和语音
Information Extraction
Speech and Auditory Processing by Humans and Machines
Natural Language Processing
Machine Translation
Event Semantics in Theory and Practice
附加课程
Machine Intelligence on Embedded Systems
Machine Intelligence
Machine Perception
Mathematical Game Theory
Learning, Estimation and Control
Databases
Algorithms for Sensor-Based Robotics
Modern Cryptography
Artificial Intelligence
Information Retrieval and Web Agents
Cybersecurity Risk Management
数据科学Capstone Experience
EN.553.806 Capstone Experience
