Basic information on relevant courses

Core Courses: Here is the list of core courses that all data science graduate students should take:

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:

On the other hand, a student eager to learn computational and optimization aspects of machine learning including relevant feature extraction techniques could take:

A student interested in algorithm design and scalable computation for machine learning could take:

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:

A student interested in applications of data science could take:

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: