Core Courses: Here is the list of core courses that all data science graduate students should take:
- ECS 260: Software Engineering
- ECS 270: Artificial Intelligence (or ECS 271: Machine Learning and Discovery)
- EEC 266: Information Theory and Coding
- MAT 270: Mathematics of Data Science
- STA 208: Statistical Methods in Machine Learning
Optional Courses: Depending on his/her research interests, a data science graduate student can take different optional courses. For example, a student interested in statistical methodologies for big data analysis could take:
- STA 209: Optimization for Big Data Analytics
- STA 220: Data & Web Technologies for Data Analysis
- STA 221: Big Data & High Performance Statistical Computing
- STA 243: Computational Statistics
On the other hand, a student eager to learn computational and optimization aspects of machine learning including relevant feature extraction techniques could take:
- MAT 226B: Numerical Methods: Large-Scale Matrix Computations
- MAT 258A: Numerical Optimization
- MAT 258B: Discrete and Mixed-Integer Optimization
- MAT 271: Applied and Computational Harmonic Analysis
A student interested in fundamental aspects of machine learning could take the following topics courses:
A student interested in algorithm design, scalable computation, and hardware for machine learning could take:
- ECS 222A: Design and Analysis of Algorithms
- ECS 223: Parallel Algorithms
- ECS 231: Large-scale Scientific Computation
- EEC 289Q Deep Learning Hardware
A student interested in applications in data science and machine learning could take the following topics courses:
- EEC 174BY Applied Machine Learning Senior Design Project
- EEC 175AB Internet of Things Senior Design Project
- EEC 289Q Practical AI
A student interested in signal/image processing could take:
- EEC 201 Digital Signal Processing
- EEC 206 Digital Image Processing
- EEC 263 Optimal and Adaptive Filtering
- EEC 264 Detection and Estimation of Signals in Noise
A graduate student can also take courses outside the four departments, if appropriate. For example, the following courses will provide a strong foundation for a data science graduate student focusing on neuroscience: