**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: Mathematical Foundations 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 algorithm design and scalable computation for machine learning could take:

- ECS 222A: Design and Analysis of Algorithms
- ECS 223: Parallel Algorithms
- ECS 231: Large-scale Scientific Computation

A student interested in algorithm design and scalable computation for machine

learning could take the following topics courses:

- EEC 289Q Data Analytics in Computer Engineering
- EEC 289A Introduction to Reinforcement Learning

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 student interested in applications of data science could take:

- EEC 274 Internet Measurements, Modeling, and Analysis
- EEC 193AB Design Projects in AI-Systems

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: