Curriculum & Requirements

Curriculum Overview

The minor in Machine Learning and Data Science consists of 8 courses:

  • 1 course in Programming Foundations
  • 1 course in Statistics Foundations
  • 4 specialization courses focused in machine learning, data science, or a hybrid between the two
  • 2 electives in machine learning and data science.

Important Notes

  • At least 4 courses must be unique to the minor, and may not be used towards the 21-unit major program, or towards other minors or certificates.
    * For students in Catalog year 2021 or before: at least 5 courses must be unique to the minor, and may not be used towards the 16-unit major program, or towards other minors or certificates. Courses used towards the basic engineering requirement or unrestricted electives are not considered part of the major program. 
  • Students are strongly encouraged to discuss curriculum choices for the MLDS minor with their major advisors before proceeding. See the advising page for more major-specific curriculum guidance.
  • Courses with a grade lower than C- cannot be applied to the minor.

Minor Requirements

Students must complete the specified credits in each of the following areas:

Programming Foundations (1 credit)

Statistics Foundations (1 credit)

Specialization (4 credits)

Data engineering track:

Machine learning track*:

* Note: Students majoring in Computer Science are not eligible for the Machine Learning specialization. 

Hybrid track:


MLDS Electives (2 credits)

*Note: special topics courses in areas other than those listed will not be accepted.