Master of Science in Data Science
Online on the Coursera platform
datascience@colorado.edu
The online Master of Science degree in Data Science (MSDS) 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 MSDS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics and statistics.
The MSDS 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 online Master of Science degree in Data Science 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 CU Boulder on Coursera program does not align with standard campus policies. Please refer to the Online Programs section of the catalog for more information.
Requirements
Admission
The MS-DS on Coursera uses performance-based admissions, which means students earn program admission simply by performing well in a three-course pathway. Students enroll in and complete their preferred three-course pathway with a GPA of 3.0 or better in each of the three courses to be admitted to the program. Pathway courses are a required part of the curriculum, which means students make direct progress toward the degree while they work toward program admission.
There is no traditional application for admission to the degree. The University of Colorado Boulder never asks for transcripts, previous test scores (like GRE or TOEFL), application essays, letters of recommendation or application fees. A prior degree is not required for admission. Because this program is fully online, students do not need to complete a background check to enroll.
A student must complete four required protocols to earn admission to the MS-DS on Coursera:
- Earn at least a grade of C in each for-credit course within one of the following pathway specializations:
- Data Science Foundations: Statistical Inference
DTSA 5001 Probability Foundations for Data Science and AI
DTSA 5002 Statistical Estimation for Data Science and AI
DTSA 5003 Hypothesis Testing for Data Science
- Foundations of 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
- Pass one pathway with a pathway GPA of 3.0 or higher.
- Have a cumulative GPA of at least 3.00 for all for-credit courses taken to date.
- Officially declare intent to seek the degree via the account creation form or Program Action Form. This can be done, before during, or after completing any work in a pathway specialization.
Upon completion of these three steps, the student is admitted to the MS-DS on Coursera. Students may successfully complete a designated pathway of specialization and declare intent at any point in their academic journey. Completion of a pathway specialization is not required for students to begin earning academic credit, only to earn the degree.
Nondegree students may also enroll in for-credit courses. All courses attempted and/or completed for credit will appear on official CU Boulder transcripts (unless dropped by the drop deadline) and will count toward the cumulative GPA.
Both pathways are required to meet degree requirements. Pick one to attempt admission.
Prerequisite Knowledge
There are no formal prerequisites, 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.
Program Requirements
The MSDS 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.
The MSDS on Coursera requires a minimum cumulative GPA of 3.00 and a grade of C or better in each core class (including the two required pathway specializations and the five additional required core specializations). Courses in which grades below C (2.0) are received may not be applied toward degree requirements.
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Data Science Foundations: Statistical Inference (Pathway Specialization) | 3 | |
| Probability Foundations for Data Science and AI | ||
| Statistical Estimation for Data Science and AI | ||
| Hypothesis Testing for Data Science | ||
| Foundations of Data Structures and Algorithms (Pathway Specialization) | 3 | |
| Algorithms for Searching, Sorting, and Indexing | ||
| Trees and Graphs: Basics | ||
| Dynamic Programming, Greedy Algorithms | ||
| Vital Skills for Data Scientists | 4 | |
| Data Science as a Field | ||
| Cybersecurity for Data Science | ||
| Ethical Issues in Data Science | ||
| Fundamentals of Data Visualization | ||
| Statistical Modeling for Data Science | 3 | |
| Modern Regression Analysis in R | ||
| ANOVA and Experimental Design | ||
| Generalized Linear Models and Nonparametric Regression | ||
| Data Mining: Foundations & Practice | 3 | |
| Data Mining Pipeline | ||
| Data Mining Methods | ||
| Data Mining Project | ||
| Machine Learning: Theory and Hands-on Practice with Python | 3 | |
| Introduction to Machine Learning: Supervised Learning | ||
| Unsupervised Algorithms in Machine Learning | ||
| Introduction to Deep Learning | ||
| Databases for Data Scientists | 2 | |
| Relational Database Design | ||
| The Structured Query Language (SQL) | ||
| Data Science Elective Courses | 9 | |
| Total Credit Hours | 30 | |
Electives
Students must complete 9 credit hours of elective coursework, selected from the courses listed below. Up to 6 of these credits may be taken from approved electives offered by other CU Boulder on Coursera graduate programs (see 'Other Electives' below). The list of approved electives may continue to grow as additional curriculum is developed.
| Code | Title | Credit Hours |
|---|---|---|
| Data Science Elective Courses | 9 | |
| Statistical Learning for Data Science | 3 | |
| Statistical Learning for Data Science: Regression and Classification | ||
| Statistical Learning for Data Science: Resampling, Selection and Splines | ||
| Statistical Learning for Data Science: Trees, SVM and Unsupervised Learning | ||
| Software Architecture for Big Data | 3 | |
| Fundamentals of Software Architecture for Big Data | ||
| Software Architecture Patterns for Big Data | ||
| Applications of Software Architecture for Big Data | ||
| Computer Vision | 3 | |
| Introduction to Computer Vision | ||
| Deep Learning for Computer Vision | ||
| Modern AI Models for Vision and Multimodal Understanding | ||
| High-Performance and Parallel Computing | 3 | |
| Introduction to High Performance and Parallel Computing | ||
| Efficient Programming | ||
| Parallel Computing with MPI | ||
| Data Science Methods for Quality Improvement | 3 | |
| Managing, Describing, and Analyzing Data | ||
| Stability and Capability in Quality Improvement | ||
| Measurement Systems Analysis | ||
| Bayesian Statistics for Data Science | 3 | |
| Introduction to Bayesian Statistics for Data Science | ||
| Computational Bayesian Statistics for Data Science | ||
| Bayesian Statistical Modeling for Data Science Applications | ||
| Internet Policy: Principles and Problems | 3 | |
| When to Regulate? The Digital Divide and Net Neutrality | ||
| Protecting Individual Privacy on the Internet | ||
| Cybersecurity in Crisis: Information and Internet Security | ||
| Modeling and Predicting Climate Anomalies | 3 | |
| Global Climate Policies and Analysis | ||
| Modeling Climate Anomalies with Statistical Analysis | ||
| Predicting Extreme Climate Behavior with Machine Learning | ||
| Natural Language Processing: Deep Learning Meets Linguistics | 3 | |
| Fundamentals of Natural Language Processing | ||
| Deep Learning for Natural Language Processing | ||
| Model and Error Analysis for Natural Language Processing | ||
| Text Marketing Analytics | 3 | |
| Supervised Text Classification for Marketing Analytics | ||
| Unsupervised Text Classification for Marketing Analytics | ||
| Network Analysis for Marketing Analytics | ||
| Effective Communication | 2 | |
| Effective Communication: Writing, Design, and Presentation | ||
| Effective Communication Capstone Project | ||
| Stand-Alone Electives | ||
| DTSA 5707 | Deep Learning Applications for Computer Vision | 1 |
| DTSA 5735 | Advanced Topics and Future Trends in Database Technologies | 1 |
| DTSA 5739 | Security and Ethical Hacking: Attacking Web and AI Systems | 1 |
| DTSA 5841 | IBM Capstone Project | 1 |
Other Electives
Up to 6 graduate-level credit hours of courses offered by other CU degrees on Coursera may be applied as elective credits toward the MSDS on Coursera degree. All courses must be graduate level, offered through Coursera, and meet all applicable academic standards. This includes all courses offered by the MEEM, MSCS, MSAI and MSECE programs on Coursera, with the exceptions of CSCA 5214, CSCA 5224 and CSCA 5234. Students wishing to complete degrees in more than one program must complete all the requirements for both degrees with no shared or overlapping coursework.
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.
Faculty members who develop courses and/or serve as instructor of record for graduate level courses will have approved Graduate Faculty Appointments.
Prior Learning Credit
The online Master of Science in Data Science (hosted on Coursera) may accept credit for prior learning in limited instances for students formally admitted to the degree program. Please visit the MSDS on Coursera Curriculum Page for more information on eligibility, requirements, and credit approval.
Time Limit
Courses used toward the MSDS on Coursera degree must have been completed within eight years of the degree conferral date. Courses taken more than eight years prior to graduation will appear on the transcript and be calculated in the cumulative GPA but may not be used toward the degree. Students may continue to pursue the degree even after eight years, but they must accrue 30 credits within an eight-year window in order to earn the degree.
The eight-year restriction is applied to courses on a rolling basis and is determined by the date that credit was awarded in the course.
Learning Outcomes
By the completion of the program, students will be able to:
- Develop proficiency in foundational and cutting edge data science tools.
- Conduct exploratory data analyses and apply statistical and probabilistic modeling techniques to draw insights and support decision-making in data-driven projects.
- Develop and implement efficient algorithms for processing and analyzing large-scale data sets, selecting appropriate computational approaches for complex problems.
- Apply machine learning and data mining techniques to analyze large data sets, identify patterns, make predictions and derive actionable insights.
- Recognize and address ethical issues and data security concerns, employing best practices to manage sensitive data responsibly and maintain privacy.
- Clearly communicate complex analytical findings and insights to both technical and non-technical audiences, bridging the gap between data science outputs and actionable understanding.