The MS-CS requires a minimum of 30 credit hours of approved, degree-eligible graduate-level coursework. Before graduation, students must have a minimum cumulative grade-point average (GPA) of 3.00 and a grade of B or better in each breadth class (including the two required pathways).

Course release dates will be posted next to the course when the availability of enrollment is confirmed. To avoid any confusion we will not provide estimated release timelines.


This program does not require formal prerequisites, we recommend learners be familiar with particular subjects. See Are there any prerequisites to for the program? on our FAQ page for an outline of those subjects and suggested basic courses. These suggested courses are not required and do not count for credit toward the MS-CS degree.

You will complete:

  • 15 credits of breadth coursework across two pathways and three specializations

  • 15 credits of elective coursework across your choice of a variety of topic areas listed below

This degree is designed for students who have:

  • A strong foundation in computer science either via education or professional experience
  • Programming and software development experience
  • An understanding of linear algebra, discrete math, probability and statistics (calculus required for select electives)

Learner Journeys Examples PDF


You may complete courses in any order. When you are ready to earn admission to the program, complete all three courses in one pathway with a B or better in each course. Please note that you DO NOT have to be admitted to take more courses for credit to make progress toward your degree.

Credits you earn before admission will apply toward the degree. You must earn a B or better in your breadth courses, and C or better in your electives courses for credit toward your degree. Courses with grades below these minimums will not count toward your degree, but they will apply to your GPA.

Complete all 15 breadth credits.

Complete either pathway with a B grade or better in each course to earn admission. Complete both pathways to graduate.

Pathway Breadth: Foundations of Data Structures and Algorithms (3 credits)

Learn more.

Pathway Breadth: Software Architecture for Big Data (3 credits)

Learn more.


Machine Learning: Theory & Hands-On Practice with Python (3 credits)

Computing, Ethics, and Society (3 credits)

Network Systems: Principles and Practice (Linux and Cloud Networking) (3 credits)

This specialization is currently in development.

Complete 15 elective credits, including at least four full specializations. 

  • You may choose to complete five specializations or a combination of four specializations plus three 1-credit courses from different specializations.
  • Up to six credits from other CU Boulder degrees on Coursera can be applied toward MS-CS elective credit requirements. See Outside Electives below for details.

More courses to come! Various additional specializations are in development.

Big Data Challenges and NoSQL Solutions (3 credits)

This specialization is currently in development.

Data Mining Foundations and Practice (3 credits)

Foundations of Autonomous Systems (3 credits)

Generative AI (3 credits)

This specialization is currently in development.

Internet Policy: Principles and Problems (3 credits)

This specialization is currently in development.

  • CSCA 5433: When to Regulate? The Digital Divide and Net Neutrality (available Summer 2 session 2024)
  • CSCA 5443: Protecting Individual Privacy on the Internet
  • CSCA 5453: Cybersecurity in Crisis: Information and Internet Security

Introduction to Computer Vision (3 credits)

This new specialization is currently in development.

  • CSCA 5522: Introduction to Computer Vision (available Summer 2 session 2024)
  • CSCA 5322: Deep Learning for Computer Vision
  • CSCA 5422: Computer Vision for Generative AI

Introduction to Human-Computer Interaction (3 credits)

This specialization is currently in development.

  • CSCA 5859: Ideating and Prototyping Interfaces
  • CSCA 5869: User Interface Testing and Usability
  • CSCA 5879: Emerging Topics in HCI: Designing for VR, AR, AI

Introduction to Robotics with Webots (3 credits)

Natural Language Processing: Deep Learning Meets Linguistics (3 credits)

This specialization is currently in development.

  • CSCA 5832: Fundamentals of Natural Language Processing
  • CSCA 5842: Deep Learning for Natural Language Processing
  • CSCA 5852: Model and Error Analysis for Natural Language Processing

Object-Oriented Analysis & Design (3 credits) 

This specialization is currently in development.

Standalone Elective Courses

These one-credit courses are not part of any specialization. Remember you must complete four full specializations to earn the MS-CS. These courses are currently in development.

Outside Elective Courses

You can apply up to six graduate-level credit hours of courses offered by other CU degrees on Coursera toward the MS-CS on Coursera degree*. All courses must be graduate level, offered through Coursera, and meet all applicable academic standards. This includes all courses offered by the ME-EM on Coursera, the MS-DS on Coursera, and the MS-EE on Coursera programs except the following courses.

*If you are applying outside elective credits to your degree, please contact the MS-CS program advisor at after your grade posts for the courses.
You cannot apply credit from the following courses toward MS-CS on Coursera requirements:

  • DTSA 5302 Cybersecurity for Data Science
  • DTSA 5303 Ethical Issues in Data Science
  • DTSA 5501 Algorithms for Searching, Sorting, and Indexing
  • DTSA 5502 Trees and Graphs: Basics
  • DTSA 5707 Deep Learning Applications for Computer Vision - The exclusion of this course will take effect in AY 24-25. If you were admitted in AY 23-24 this course was still part of your catalog year and accepted toward electives in the MS-CS degree.

Courses that begin with a "CSCA" prefix and courses that are cross-listed with a CSCA-prefixed course are not considered outside electives and do not count against this six-credit limit.  

If you want to complete degrees in more than one program, you must complete all the requirements for both degrees with no shared or overlapping course work.