Program Overview

Master of Science in Data Science

The Master of Science in Data Science (MS-DS) is an online data science master's degree designed to prepare the next generation of data scientists to successfully work and collaborate with others across a variety of scientific, business, and other fields.

Academic Overview

Data science is a multidisciplinary field that focuses on the extraction of knowledge and insight from large datasets. Data scientists are tasked with using a range of skills in applied mathematics and statistics, computer science, and applications.

Curriculum

The MS-DS provides learners with a strong foundation in acquiring, cleaning, and managing data. You will learn to analyze large datasets using data mining and machine learning techniques. You will also design, conduct, and run statistical experiments and models; draw rational conclusions from data using probability theory and statistics; and more.

Coursework

You will complete 21 credits of core coursework in statistics, computer science, and foundational concepts as well as 9 credits of elective coursework. You may complete courses in any order, but we suggest following one of our recommended learner journeys below.

Statistics Learner Journey

If you are skilled in statistics, we recommend you complete your courses in the following order:

1. Data Science Foundations: Statistical Inference Pathway

• DTSA 5001 Probability Theory: Applications for Data Science
• DTSA 5002 Statistical Inference for Estimation in Data Science
• DTSA 5003 Hypothesis Testing for Data Science

2. Vital Skills for Data Scientists

• DTSA 5301 Data Science as a Field
• DTSA 5302 Cybersecurity for Data Science
• DTSA 5303 Ethical Issues in Data Science
• DTSA 5304 Fundamentals of Data Visualization

3. Core and Elective Courses

• Complete your remaining courses in any order:
  • Data Science Foundations: Data Structures & Algorithms Courses (3 credits)
  • Statistical Modeling for Data Science Courses (3 credits)
  • Data Mining: Foundations & Practice Courses (3 credits)
  • Machine Learning Courses (3 credits)
  • Databases Courses (2 credits)
  • Data Science Elective Courses (9 credits)

Computer Science Learner Journey

If you are skilled in computer science, we recommend you complete your courses in the following order:

1. Data Science Foundations: Data Structures and Algorithms Pathway

• DTSA 5501 Algorithms for Searching, Sorting & Indexing
• DTSA 5502 Trees and Graphs: Basics
• DTSA 5503 Dynamic Programming, Greedy Algorithms

2. Vital Skills for Data Scientists

• DTSA 5301 Data Science as a Field
• DTSA 5302 Cybersecurity for Data Science
• DTSA 5303 Ethical Issues in Data Science
• DTSA 5304 Fundamentals of Data Visualization

3. Core and Elective Courses

• Complete your remaining courses in any order:
  • Data Science Foundations: Statistical Inference Courses (3 credits)
  • Statistical Modeling for Data Science Courses (3 credits)
  • Data Mining Foundations & Practice Courses (3 credits)
  • Machine Learning Courses (3 credits)
  • Databases Courses (2 credits)
  • Data Science Elective Courses (9 credits)

Elective Courses

• Bayesian Statistics
  • DTSA 5726 Introduction to Bayesian Statistics for Data Science Applications
• Big Data Architecture
  • DTSA 5507 Fundamentals of Software Architecture for Big Data
  • DTSA 5508 Big Data Architecture in Production
  • DTSA 5714 Applications of Software Architecture for Big Data
• Computer Vision
  • DTSA 5512 Introduction to Computer Vision
  • DTSA 5513 Deep Learning for Computer Vision
  • DTSA 5514 Modern AI Models for Vision and Multimodal Understanding
• Data Mining Foundations and Practice
  • DTSA 5504 Data Mining Pipeline
  • DTSA 5505 Data Mining Methods
  • DTSA 5506 Data Mining Project
• Data Science Foundations: Data Structures and Algorithms
  • DTSA 5501 Algorithms for Searching, Sorting, and Indexing
  • DTSA 5502 Trees and Graphs: Basics
  • DTSA 5503 Dynamic Programming, Greedy Algorithms
• Data Science Foundations: Statistical Inference
  • DTSA 5001 Probability Theory: Foundation for Data Science
  • DTSA 5002 Statistical Inference for Estimation in Data Science
  • DTSA 5003 Statistical Inference and Hypothesis Testing in Data Science Applications
• Data Science Methods for Quality Improvement
  • DTSA 5704 Managing, Describing, and Analyzing Data
  • DTSA 5705 Stability and Capability in Quality Improvement
  • DTSA 5706 Measurement Systems Analysis
• Databases
  • DTSA 5733 Relational Database Design
  • DTSA 5734 The Structured Query Language (SQL)
  • DTSA 5735 Advanced Topics and Future Trends in Database Technologies
• Deep Learning Applications for Computer Vision
  • DTSA 5707 Deep Learning Applications for Computer Vision
• Effective Communication
  • DTSA 5842 Effective Communication: Writing, Design and Presentation
  • DTSA 5843 Effective Communication Capstone Project
• Finance for Technical Managers
  • EMEA 5021 Product Cost & Investment Cash Flow Analysis
  • EMEA 5022 Project Valuation and the Capital Budgeting Process
  • EMEA 5023 Financial Forecasting and Reporting
• Foundations of Autonomous Systems
  • CSCA 5834 Modeling of Autonomous Systems
  • CSCA 5844 Requirement Specifications for Autonomous Systems
  • CSCA 5854 Verification and Synthesis of Autonomous Systems
• Foundations of Data Structures and Algorithms
  • CSCA 5424 Approximation Algorithms and Linear Programming
  • CSCA 5454 Advanced Data Structures RSA and Quantum Algorithms
• Generative AI
  • CSCA 5112 Introduction to Generative AI
• High Performance and Parallel Computing
  • DTSA 5701 Introduction to High Performance Computing
  • DTSA 5702 Efficient Programming
  • DTSA 5703 Parallel Computing with MPI
• Industry Collaboration
  • DTSA 5841 IBM Capstone Project
• Internet Policy
  • DTSA 5736 When to Regulate? The Digital Divide and Net Neutrality
• Introduction to Robotics with Webots
  • CSCA 5312 Basic Robotic Behaviors and Odometry
  • CSCA 5332 Robotic Mapping and Trajectory Generation
  • CSCA 5342 Robotic Path Planning and Task Execution
• Machine Learning
  • DTSA 5509 Introduction to Machine Learning: Supervised Learning
  • DTSA 5510 Unsupervised Algorithms in Machine Learning
  • DTSA 5511 Introduction to Deep Learning
• Modeling and Predicting Climate Anomalies
  • DTSA 5740 Global Climate Change Policies and Analysis
  • DTSA 5741 Modeling Climate Anomalies with Statistical Analysis
  • DTSA 5742 Predicting Extreme Climate Behavior with Machine Learning
• Network Systems: Principles and Practice (Linux and Cloud Networking)
  • CSCA 5063 Network Systems Foundation
  • CSCA 5073 Linux Networking
  • CSCA 5083 Network Principles in Practice: Cloud Networking
• NLP: Natural Language Processing
  • DTSA 5747 Fundamentals of Natural Language Processing
  • DTSA 5748 Deep Learning for Natural Language Processing
• Object-Oriented Analysis & Design
  • CSCA 5428 Object-Oriented Analysis and Design: Foundations and Concepts
  • CSCA 5438: Object-Oriented Analysis and Design: Patterns and Principles
  • CSCA 5448: Object-Oriented Analysis and Design: Practice and Architecture
• Project Management
  • EMEA 5031 Project Management: Foundations and Initiation
  • EMEA 5032 Project Planning and Execution
  • EMEA 5033 Agile Project Management
• Security and Ethical Hacking
  • DTSA 5739 Security and Ethical Hacking: Attacking Web and AI Systems
  • CSCA 5303: Security & Ethical Hacking: Attacking the Network
  • CSCA 5313: Security & Ethical Hacking: Attacking Unix and Windows
• Statistical Learning for Data Science
  • DTSA 5020 Regression and Classification
  • DTSA 5021 Resampling, Selection, and Splines
  • DTSA 5022 Trees, SVM, and Unsupervised Learning
• Statistical Modeling for Data Science
  • DTSA 5011 Modern Regression Analysis in R
  • DTSA 5012 ANOVA and Experimental Design
  • DTSA 5013 Generalized Linear Models and Nonparametric Regression
• Text Marketing Analytics
  • DTSA 5798 Supervised Text Classification for Marketing Analytics
  • DTSA 5799 Unsupervised Text Classification for Marketing Analytics
  • DTSA 5800 Network Analysis for Marketing Analytics
• Vital Skills for Data Scientists
  • DTSA 5301 Data Science as a Field
  • DTSA 5302 Cybersecurity for Data Science
  • DTSA 5303 Ethical Issues in Data Science
  • DTSA 5304 Fundamentals of Data Visualization

Earning Multiple Credentials

It is possible to earn multiple degrees and certificates offered by CU Boulder through Coursera as listed below. However, courses may not be double counted toward two credentials of the same level. If you have questions about earning multiple credentials, please contact your advisor.

• MS-DS + DS certificate (30 credits)
• MS-DS + DS Certificate + AI Certificate (33 credits)
• MS-DS + AI Certificate (30 credits)
• MS-DS + EM Certificate (33 credits)
• MS-DS + ME-EM (60 credits)

Please note that it is not possible to earn the MS-DS, plus the MS-CS or MS-AI.