Master of Science in Data Science
Online on the Coursera platform
datascience@colorado.edu
The online Master of Science degree in Data Science (MS-DS) on Coursera is an interdisciplinary degree program offered through the University of Colorado Boulder and hosted online through Coursera’s learning platform. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics and statistics.
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, statistics, and computer science, and in domain applications such as information science, geography, business, media and the humanities.
The MS-DS on Coursera provides learners with a strong foundation in acquiring, cleaning and managing data. Students will learn to analyze large datasets using data mining and machine learning techniques. Students will also design, conduct, and run statistical experiments and models; draw rational conclusions from data using probability theory and statistics; and more.
Graduates of the MS-DS on Coursera program will be well-prepared to apply data science skills to a specific domain area. Graduates will also be able to clearly communicate the results of data science analysis to a non-technical audience; structure effective meetings and projects using collaboration skills; and act ethically in the role of professional data scientist.
Topic Areas
General Data Science
Data science is a multidisciplinary field that uses scientific methods, processes, applications, algorithms and systems to extract knowledge and insights from structured and unstructured data.
Applied Mathematics
The Department of Applied Mathematics in the College of Arts and Sciences offers a range of courses and research opportunities in many areas, including computational mathematics, mathematical biology, nonlinear phenomena, physical applied mathematics, and probability and statistics.
Computer Science
Computer science is an exciting and challenging field that has an impact on many parts of our lives. Computer scientists craft the technologies that enable the digital devices we use every day. They develop the large-scale software that powers business and industry, advance the computational techniques and write the software that supports scientists in their study of the world around us. Many new applications of computing technology remain to be discovered. Computing will be at the heart of future revolutions in business, science and society. Students who study computer science will be at the forefront of these important advances.
Information Science
Information science considers the relationships between people, places and technology and the information those interactions yield. The internet is a broad example of a socio-technical system that is comprised of hardware and software, but in daily life is better understood as a constantly changing social infrastructure upon which complex forms of human-human and human-information interaction rest. Scholars and students of information science develop new methods to study these socio-technical phenomena, and translate those findings to the design and development of useful and meaningful technology.
Program Policies
This specialized program does not align with standard campus policies. Please refer to the Special Online Programs section of the catalog for more information.
Requirements
Admission Requirements
Students are automatically admitted to the degree program after meeting all admission requirements below. All admitted students receive an official offer letter via email. See the program website for details.
- Pass one pathway with a pathway GPA of 3.0 or higher
- Earn a C or better in all pathway courses within their chosen pathway
- Earn an overall cumulative GPA of 3.0 or higher
- Indicate interest in degree admission (via the enrollment form)
Performance-Based Admission
To be admitted to the MS-DS on Coursera as a degree-seeking student, students must enroll in and complete a pathway with a 3.00 GPA or better. A pathway is a series of 3 one-credit courses with a focus on either statistics or computer science—students choose the pathway that is right for them. Pathway courses are an important part of the required curriculum, so students make direct progress toward their degree as they complete their pathway.
Prerequisite Knowledge
There are no formal prerequisites for the MS-DS on Coursera, but students should be knowledgeable in the following:
- Python
- R programming
- Calculus including derivatives and integrals
- Linear algebra including matrix multiplication, matrix inversion and solving linear systems using matrices
If students do not yet feel ready to complete their pathway courses, the program suggests reviewing courses on the Coursera platform. Students can enroll in a pathway as a non-credit learner, which gives them the option of previewing course content. Then, they can upgrade to the for-credit version and pay tuition when they are ready.
Required Courses and Credits
The MS-DS is a non-thesis degree that requires 30 credit hours of coursework. Students must complete 21 credits of core coursework in statistics, computer science, and general core concepts as well as 9 credits of elective coursework. Students will also participate in practical, hands-on projects that utilize cloud-based programming environments and Jupyter Notebooks. Coursework includes access to real-world big data sets to prepare students for their future careers.
Learner Journeys
Students may complete courses in any order, but are advised to follow one of the recommended learner journeys below.
Statistics Pathway
We recommend that students who are skilled in statistics complete their courses in the following order:
| Code | Title | Credit Hours |
|---|---|---|
| Data Science Foundations: Statistical Inference Courses | 3 | |
| Probability Theory: Foundation for Data Science | ||
| Statistical Inference for Estimation in Data Science | ||
| Hypothesis Testing for Data Science | ||
| Vital Skills for Data Scientists Courses | 4 | |
| Data Science as a Field | ||
| Cybersecurity for Data Science | ||
| Ethical Issues in Data Science | ||
| Fundamentals of Data Visualization | ||
| Core Courses | ||
| Complete in any order | ||
| Statistical Modeling for Data Science Courses | 3 | |
| Modern Regression Analysis in R | ||
| ANOVA and Experimental Design | ||
| Generalized Linear Models and Nonparametric Regression | ||
| Foundations of Data Structures & Algorithms Courses | 3 | |
| Algorithms for Searching, Sorting, and Indexing | ||
| Trees and Graphs: Basics | ||
| Dynamic Programming, Greedy Algorithms | ||
| Data Mining: Foundations & Practice | 3 | |
| Data Mining Pipeline | ||
| Data Mining Methods | ||
| Data Mining Project | ||
| Machine Learning Courses | 3 | |
| Introduction to Machine Learning - Supervised Learning | ||
| Unsupervised Algorithms in Machine Learning | ||
| Introduction to Deep Learning | ||
| Databases Courses | 2 | |
| Relational Database Design | ||
| The Structured Query Language (SQL) | ||
| Data Science Elective Courses | 9 | |
| Total Credit Hours | 30 | |
Computer Science Pathway
We recommend that students who are skilled in computer science complete their courses in the following order:
| Code | Title | Credit Hours |
|---|---|---|
| Foundations of Data Structures and Algorithms Courses | 3 | |
| Algorithms for Searching, Sorting, and Indexing | ||
| Trees and Graphs: Basics | ||
| Dynamic Programming, Greedy Algorithms | ||
| Vital Skills for Data Scientists Courses | 4 | |
| Data Science as a Field | ||
| Cybersecurity for Data Science | ||
| Ethical Issues in Data Science | ||
| Fundamentals of Data Visualization | ||
| Core Courses | ||
| Complete in any order | ||
| Data Science Foundations: Statistical Inference for Data Science Courses | 3 | |
| Probability Theory: Foundation for Data Science | ||
| Statistical Inference for Estimation in Data Science | ||
| Hypothesis Testing for Data Science | ||
| Statistical Modeling for Data Science Courses | 3 | |
| Modern Regression Analysis in R | ||
| ANOVA and Experimental Design | ||
| Generalized Linear Models and Nonparametric Regression | ||
| Data Mining Foundations and Practice Courses | 3 | |
| Data Mining Pipeline | ||
| Data Mining Methods | ||
| Data Mining Project | ||
| Machine Learning Courses | 3 | |
| Introduction to Machine Learning - Supervised Learning | ||
| Unsupervised Algorithms in Machine Learning | ||
| Introduction to Deep Learning | ||
| Databases Courses | 2 | |
| Relational Database Design | ||
| The Structured Query Language (SQL) | ||
| Data Science Elective Courses | 9 | |
| Total Credit Hours | 30 | |
Other Electives
Students may apply up to six graduate-level credit hours of select courses offered by the engineering management and computer science degrees on Coursera toward the MS-DS on Coursera degree. All courses are graduate level, offered through Coursera, and meet all applicable academic standards. Students wishing to complete degrees in more than one program must complete all the requirements for both degrees with no shared or overlapping coursework. Select courses include:
Engineering Management Electives
| Code | Title | Credit Hours |
|---|---|---|
| Project Management Courses | 3 | |
| Project Management: Foundations and Initiation | ||
| Project Planning and Execution | ||
| Agile Project Management | ||
| Product Development Courses | 3 | |
| Product Cost and Investment Cash Flow Analysis | ||
| Project Valuation and the Capital Budgeting Process | ||
| Financial Forecasting and Reporting | ||
| Total Credit Hours | 6 | |
Computer Science Electives
Courses that begin with a "DTSA" prefix and courses that are cross-listed with a DTSA-prefixed course are not considered outside electives and do not count against the six-credit limit.