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Master of Science in Data 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

Admissions Information

Enrollment for the Spring 2 2023 session is now open!

Contact the CU Boulder MS-DS team at if you have any questions.

Important Dates

  • February 27: Spring 2 Enrollment opens
  • March 10: Spring 1 Classes end
  • March 13: Spring 2 Classes start
  • April 21: Spring 2 Enrollment closes
  • May 5: Spring 2 Classes end


Looking for a new way into a master’s degree program?

With no application or transcripts, start with whatever interests you most. Perhaps a core course on data mining or an elective on high-performance computing? Credits you earn before admission into the program will count towards your degree.

Start on any course and work through the program in your own time, at your own pace.
Program length
Choose from 6 enrollment terms throughout the year to finish 30 courses in 18 months or more.
Available in English Subtitles: English, Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Spanish, 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

Enrollment for the Spring 2 2023 session is now open!

Contact the CU Boulder MS-DS team at if you have any questions.

Important Dates

  • February 27: Spring 2 Enrollment opens
  • March 10: Spring 1 Classes end
  • March 13: Spring 2 Classes start
  • April 21: Spring 2 Enrollment closes
  • May 5: Spring 2 Classes end

A flexible curriculum designed to empower you in your career goals

The MS-DS program offers a career-ready, interdisciplinary curriculum. It gives you hands-on practice applicable to a variety of scientific and business fields. Learn theory, methods, tools, and programming languages to excel in data science. Become proficient in Predictive Modeling, Risk Analysis, Data Visualization, Machine Learning, AI, and more to start or advance your career.



Data Science Foundations: Statistical Inference Pathway (3 credits)

  1. Probability Theory: Foundation for Data Science
  2. Statistical Inference for Estimation in Data Science
  3. Statistical Inference and Hypothesis Testing in Data Science Applications

This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language.

OR (choose one specialization)

Data Science Foundations: Data Structures and Algorithms Pathway (3 credits)

  1. Algorithms for Searching, Sorting, and Indexing
  2. Trees and Graphs: Basics
  3. Dynamic Programming, Greedy Algorithms

Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms.

Vital Skills for Data Scientists (4 credits)

  1. Data Science as a field
  2. Ethical issues in Data Science
  3. Cybersecurity for Data Science
  4. Fundamentals of Data Visualization

Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss.

Data Mining Foundations and Practice Specialization (3 credits)

  1. Data Mining Pipeline
  2. Data Mining Methods
  3. Data Mining Project

The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project.

Machine Learning: Theory and Hands-on Practice with Python Specialization (3 credits)

  1. Introduction to Machine Learning: Supervised Learning
  2. Unsupervised Algorithms in Machine Learning
  3. Introduction to Deep Learning

In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems. We finish with an introduction to deep learning basics, including choosing model architectures, building/training neural networks with libraries like Keras, and hands-on examples of CNNs and RNNs.

Statistical Modeling for Data Science Applications Specialization(3 credits)

  1. Modern Regression Analysis in R
  2. ANOVA and Experimental Design
  3. Generalized Linear Models and Nonparametric Regression

Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language.

Databases for Data Scientists Specialization (2 credits)

  1. Relational Database Design
  2. The Structured Query Language (SQL)
  3. Advanced Topics and Future Trends in Database Technologies (elective)

Whether you are a beginning programmer with an interest in Data Science, a data scientist working closely with content experts, or a software developer seeking to learn about the database layer of the stack this specialization is for you! We focus on the relational database which is the most widely used type of database. Relational databases have dominated the database software marketplace for nearly four decades and form a core, foundational part of software development.

Enrollment is open for Spring 2 2023

The deadline to enroll is April 21, 2023