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Master of Science in Computer Science

Join us February 6 for our live webinar about the MS in Computer Science Program. Register here!

University of Colorado Boulder logo

Master of Science in Computer Science

University of Colorado Boulder

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Fully accredited online program

Graduate from the University of Colorado Boulder

$15,750 USD total tuition

Flexible payment options with no hidden costs or fees

Finish in 24 months

Complete 30 courses (30 credit hours) full or part time

100% online learning

Lecture videos, hands-on projects, and connection with instructors and peers

Academics

Theoretical knowledge meets technical experience

In the Master of Science in Computer Science program from CU Boulder, you’ll combine theoretical knowledge with technical experience across both broad computer science courses and specific electives on topics like human-computer interaction, robotics, data mining, autonomous systems, and more.

Flexibility
Start on a pathway course and gain credit towards the degree program.
Program length
Choose from six enrollment terms throughout the year. You can finish the entire 30-credit degree in around 24 months.
Language
Taught in English with subtitles in English, Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Spanish, and Persian.
Learn on mobile
Take your studies on the go with mobile-friendly learning on iOS and Android. Some assignments can’t be completed on a mobile device.

Admissions Information

Contact the CU Boulder MS-CS team at cuboulder-mscs@coursera.org if you have any questions.

Important Dates

Enrollment Open: January 2 - February 21, 2025.

For-credit Course Access: January 13 - March 7, 2025.

Join us February 6 for our live webinar about the MS in Computer Science Program. Register here!

Admissions Information

Contact the CU Boulder MS-CS team at cuboulder-mscs@coursera.org if you have any questions.

Important Dates

Enrollment Open: January 2 - February 21, 2025.

For-credit Course Access: January 13 - March 7, 2025.

Join us February 6 for our live webinar about the MS in Computer Science Program. Register here!

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Build the skills you’ll need in programming, statistics and data analysis

Build a solid foundation for computer science expertise, with key programming concepts and problem-solving techniques using Python, one of the most widely used and versatile programming languages.

A focus on real-life problems and practical exercises will provide you with a strong working knowledge and sought-after computer science skills.

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Curriculum

You’ll build computing-specific academic skills across two distinct areas:

  • Qualitative reasoning skills: Exploring computer ethics, you’ll learn to think critically and communicate effectively about legal, moral and ethical issues related to your field of study.

  • Quantitative reasoning skills: You’ll use data analysis and produce engaging dashboards, apply common descriptive statistics to summarise datasets, and use data visualisation approaches to demonstrate patterns.

  • This module will provide practical experience in applying theoretical programming concepts in creative ways to solve real-world problems. Using Python, one of the most popular and versatile programming languages, you’ll build essential skills in this vital discipline.
  • On completion of this module, you’ll be proficient in implementing and manipulating data structures, managing and analysing data.
  • Topics covered include control flow statements, data structures, object-oriented programming, data wrangling with Pandas, databases and NumPy library.

  • This module provides a comprehensive knowledge base in the mathematics that underpin computer science.
  • You’ll refresh and secure your understanding of arithmetic and algebra, gain an overview of the functions and fundamentals of calculus and trigonometry, and explore a range of other mathematical concepts that will be relevant to your ongoing studies of computer science.
  • Topics covered include quadratic equations and parabolae, number bases and modular arithmetic, sequences and series, and vectors and geometry.

  • You’ll build a strong foundation of statistical knowledge on this module. You’ll grasp the fundamental principles of probability theory, allowing you to understand and apply standard statistical operators and recall essential probability distributions.
  • You’ll perform statistical inference in order to make informed decisions based on data analysis, and construct causal models to meet the requirements of different statistical analysis contexts.
  • Topics covered include discrete and continuous probability distributions, interval estimation, hypothesis testing principles, contingency tables and the chi-squared test, and simple linear regression.

Enrollment for Spring 1 session opens on January 2, 2025

Spring 1 enrollment closes on February 21, 2025.