University of Colorado Boulder
Program Overview & Curriculum
The MS-DS program offers a career-ready and interdisciplinary curriculum that provides students with the opportunity to work and collaborate across a variety of scientific and business fields. Learn theory, methods, tools, and programming languages commonly used in data science, such as SQL, Apache Spark, AWS, and Hadoop. Become proficient in Predictive Modeling, Risk Analysis, Data Visualization, Machine Learning, and AI.
Complete 21 credits of core coursework in statistics, computer science, and vital skills for data scientists, as well as 9 credits of elective coursework, for a total of 30 credits in the following subject areas:
- Computer Science
- Vital Skills for Data Scientists
- Electives in Data Science (e.g., High Performance and Parallel Computing, Natural Language Processing)
We recommend following one of these two learner pathways:
Statistics Learner Journey
Consider this route if you are strong in statistics.
- Data Science Foundations: Statistical Inference pathway (3 credits)
- Vital Skills for Data Scientists courses (4 credits)
- Other core and elective courses (23 credits)
Computer Science Learner Journey
Consider this route if you are strong in computer science.
- Data Science Foundations: Data Structures & Algorithms pathway (3 credits)
- Vital Skills for Data Scientists courses (4 credits)
- Other core and elective courses (23 credits)
Looking for something different? Design your own learner journey!
Start with whatever interests you most — perhaps a core course on data mining or an elective on high-performance computing. Then, complete a full pathway (3 courses) with a cumulative 3.0 GPA or better when you're ready to earn admission to the program. Credits you earn before admission will apply toward the degree. Note that you must complete all courses within 8 years.
Choose from three types of courses:
Pathway courses are part of a 3-course series (1 credit per course) focusing on either statistics or computer science. Complete all 3 courses in 1 pathway with a cumulative 3.0 GPA or better to be admitted to the MS-DS program.
Core courses cover required content for the MS-DS program. All students must complete 21 core courses (21 credits) to earn the degree. Note that credit from pathway courses count as core credits after you are admitted to the degree.
Elective courses are also required to earn the degree, but allow more flexibility. Students must complete 9 elective credits to earn the degree, and can choose from a variety of available options.
Follow a recommended learner journey or design your own route. You can start with a pathway, core or elective course. There's no need to complete a full pathway before taking other core or elective courses. Just remember you will need to complete your pathway to be officially admitted to the degree.
(Reference MS-DS on Coursera Curriculum)
Pathway – Data Science Foundations: Statistical Inference (All 3 courses live)
Statistical inference is a method used by data scientists to draw larger conclusions from samples of data, and is an essential skill for anyone pursuing a successful career in the data science field. In this course, you’ll get an introduction to exploratory data analysis, probability theory, statistical inference, and data modeling, and cover key topics such as discrete and continuous probability distributions, expectation, laws of large numbers, central limit theorem, statistical parameter estimation, hypothesis testing, and regression analysis. A particular focus will be on application in the R programming language.
Pathway – Data Science Foundations: Data Structures and Algorithms (All 3 courses live)
Having a solid understanding of data structures and fundamental algorithms is increasingly essential for data scientists. This course provides learners a comprehensive overview of data structures and algorithms with an emphasis on processing large datasets. Topics include basic data structures such as arrays, heaps, and hash tables, as well as complex data structures like quadtrees for special applications. Also covered are algorithms involving these data structures such as sorting, order-statistics, traversals, and more. Special topics include spatial data structures, geometric algorithms and parallel algorithms for data processing. Learners will benefit from basic programming exercises that reinforce core concepts.
Vital Skills for Data Scientists (All 4 courses live)
Advancing a career in data science requires understanding the field as a whole and going beyond just building technical competence. In these courses, learners receive an introduction to the entire field of data science, including the terminology of the field, the typical stages of problem-solving, and some primary application areas for data science. Learners will also analyze and understand ethical issues in the field of data science, visualization strategies, the challenges of providing security in dealing with large data sets, and the leading approaches and techniques for data security.
Data Mining Foundations and Practice (All 3 courses live)
Data mining—the process of structuring raw data and identifying patterns—is an increasingly in-demand skill, and in this course, learners will be introduced to basic data mining concepts and techniques for discovering interesting patterns hidden in large-scale data sets, with a focus on issues relating to effectiveness and efficiency. Topics covered include data preprocessing, data warehouse, association, classification, and clustering, as well as techniques for mining specific data types such as time-series, social networks, multimedia, and Web data.
Big Data Architecture (Launching soon)
In these courses, learners will cover the primary problem-solving strategies, methods and tools needed for data-intensive programs, including the collection, storage, processing and analytics of big data. They will explore the design, development, and evaluation of different systems and algorithms, with a focus on both efficiency and effectiveness in real-world applications.
Machine Learning (Launching soon)
Machine learning’s ability to reliably and efficiently solve labor-intensive problems have made it an extremely valuable tool for data scientists, and in this course, learners will gain knowledge in the three main subfields of machine learning: supervised learning, reinforcement learning, and unsupervised learning. The emphasis of the course is on practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines, Q-learning), and it addresses connections to data mining and statistical modeling. A strong foundation in probability, statistics, multivariate calculus, and linear algebra is highly recommended for learners interested in enrolling.
Statistical Modeling for Data Science (All 3 courses live)
The application of statistical models allows for more strategic interpretation of data. Statistical modeling is widely used by data scientists and analysts to identify relationships, make predictions, and create visualizations. This course offers learners the opportunity to continue building expertise with essential statistical techniques. Topics include modern regression analysis, analysis of variance (ANOVA), experimental design, nonparametric methods, and an introduction to Bayesian data analysis. There is a continuing emphasis on application in the R programming language.
When you earn your MS-DS degree, you'll be able to:
- Use the latest industry tools and technologies to manage, visualize, and analyze complex data sets
- Apply data science skills to solve business challenges and drive critical decision-making
- Communicate complex analysis clearly and effectively across your organization
- Join theory with application to create the most effective solutions for your organization’s data science needs
- Boost your resume with marketable skills like SQL, Apache Spark, AWS, Hadoop
- Stand out to potential employers via mastery of predictive modeling, risk analysis, data visualization, machine learning, and AI
- Use your new-found data intuition to deep dive into large datasets and share insights with colleagues
- Successfully navigate a data science career by understanding the field as a whole, including ethical issues and best practices surrounding cybersecurity
Try a degree course today
Sample the MS in Data Science learning experience and test your skills by enrolling in a non-credit course first. You can always upgrade later to the for-credit experience and pay tuition to apply credit toward the full degree.
Here are some courses you can start with:
- Data Science Foundations: Data Structures and Algorithms Pathway (3 courses)
- Data Science Foundations: Statistical Inference for Data Science Pathway (3 courses)
- Data Science as a Field (core course)
- Data Mining Pipeline (core course)
- Introduction to High-Performance and Parallel Computing (elective course)
- Managing, Describing, and Analyzing Data (elective course)
The length of the program is dependent on your learning goals. On average, you can expect to complete courses and the full graduate degree in the following time frames:
Individual courses: Each session is 8 weeks long, but courses usually only take 4–6 weeks to complete. There are 6 sessions per year, and we recommend taking no more than three courses per session.
Earn a graduate degree: 24 months.
Coursera on Mobile
Access all course materials anywhere with the mobile app, used by over 80 percent of degree students on Coursera. Available on iOS and Android.
Using the mobile app, you can:
- Save a week’s worth of content for offline access with one click
- Save and submit quizzes offline
- View text transcripts of lecture videos
- Take notes directly in the app
- Set reminder alerts to help you make progress
Download Coursera's mobile app
Coursera does not grant credit, and does not represent that any institution other than the degree granting institution will recognize the credit or credential awarded by the institution; the decision to grant, accept, or transfer credit is subject to the sole and absolute discretion of an educational institution.