In this document, we list existing undergraduate and graduate courses offered by our four departments that are highly relevant to Data Science. Note that several new courses have been proposed, which are currently pending approval as indicated below. Note also that the course numbers below 200 designate undergraduate courses while those above 200 designate graduate courses.
Department of Computer Science
- ECS 122A: Algorithm Design and Analysis
- ECS 124: Theory and Practice of Bioinformatics
- ECS 129: Computational Structural Bioinformatics
- ECS 130: Scientific Computation
- ECS 132: Probability and Statistical Modeling for Computer Science
- ECS 153: Computer Security
- ECS 158: Programming on Parallel Architectures
- ECS 162: Web Programming
- ECS 163: Information Interfaces
- ECS 165A: Database Systems
- ECS 170: Artificial Intelligence
- ECS 171: Machine Learning
- ECS 173: Image Processing and Analysis
- ECS 174: Introduction to Computer Vision
- ECS 188: Ethics in an Age of Technology
- ECS xxx: Natural Language Processing (under development)
- ECS 220: Theory of Computation
- ECS 222A: Design and Analysis of Algorithms
- ECS 222B: Advanced Design and Analysis of Algorithms
- ECS 223: Parallel Algorithms
- ECS 224: String Algorithms and Applications in Computational Biology
- ECS 225: Graph Theory
- ECS 226: Computational Geometry
- ECS 229: Advanced Computational Structural Bioinformatics
- ECS 230: Applied Numerical Linear Algebra
- ECS 231: Large-scale Scientific Computation
- ECS 234: Computational Functional Genomics
- ECS 260: Software Engineering
- ECS 271: Machine Learning and Discovery
- ECS 272: Information Visualization
Department of Electrical & Computer Engineering
- EEC 193AB: Design Projects in AI-Systems (e.g., Self-Driving Cars)
- EEC 206: Digital Image Processing
- EEC 263: Optimal and Adaptive Filtering
- EEC 264: Detection and Estimation of Signals in Noise
- EEC 266: Information Theory and Coding
- EEC 274: Internet Measurements, Modeling, and Analysis
- EEC 289A: Introduction to Reinforcement Learning
- EEC 289Q: Data Analytics in Computer Engineering
Department of Mathematics
- MAT 167: Applied Linear Algebra
- MAT 168: Optimization
- MAT 170: Mathematics for Data Analytics and Decision Making
- MAT 226B: Numerical Methods: Large-Scale Matrix Computations
- MAT 258A: Numerical Optimization
- MAT 258B: Discrete and Mixed-Integer Optimization
- MAT 270: Mathematical Foundations of Data Science
- MAT 271: Applied and Computational Harmonic Analysis
- MAT 280: Topics in Mathematics (Big Data Analysis; Harmonic Analysis on Graphs and Networks were offered as topics course, and some of them is in the process of becoming a regular course.)
Department of Statistics
- STA 32: Gateway to Statistical Data Science
- STA 135: Multivariate Data Analysis
- STA 137: Applied Time Series Analysis
- STA 141A: Fundamentals of Statistical Data Science
- STA 141B: Data & Web Technologies for Data Analysis
- STA 141C: Big Data & High Performance Statistical Computing
- STA 142A: Statistical Learning I
- STA 142B: Statistical Learning II
- STA 208: Statistical Methods in Machine Learning
- STA 209: Optimization for Big Data Analytics
- STA 220: Data & Web Technologies for Data Analysis
- STA 221: Big Data & High Performance Statistical Computing
- STA 237A: Time Series Analysis
- STA 237B: Time Series Analysis
- STA 242: Introduction to Statistical Programming
- STA 243: Computational Statistics