MS in Data Science
Drawing on statistics, computer science and mathematics, the Master of Science in Data Science focuses on the effective use of a vast array of information drawn from the natural and social sciences. Because of the interdisciplinary nature of the curriculum and unique access to collaborative outside agencies and organizations, the program offers a rich, hands-on experience.
Students are equipped with the latest tools for analysis and data visualization and are immersed in complex topics, such as how to identify patterns from large swathes of data. Courses also cover machine learning and Python, JavaScript and R programming languages.
Program Objectives and Focus Areas
Learning Objectives
Students who complete the MS in Data Science are equipped to apply data science techniques to solve real-world problems, communicate findings and effectively present those findings using data visualization tools.
Specifically, students graduate with:
- Thorough working knowledge of statistical data analysis techniques
- Experience with data-mining software tools
- Experience with cutting-edge tools and technologies to analyze big data
- Practical skills for visualizing and transforming data
- Communication skills and working effectively in teams
Focus Areas
Both the master’s degree and the graduate certificate program combine courses from four areas:
- Methods: Basics of data management and data analytics; deep expertise in the programing languages essential for data science, including Python, JavaScript and R
- Applications: Elective courses in data science applied to a specific knowledge domain, such as astrophysics, political science and geography
- Skills: Teamwork, project management and communication skills
- Technology: Hands-on exposure to data and visualization software and languages
“I started the Data Science program with a background in economics and having never coded before. This program has given me all the tools and prepared me to face any data-related challenge. I feel more than confident and prepared to implement everything I learned in the industry.”
Guillermina Sutter Schneider '20
Capstone Project
As a culmination of the master’s program, students enroll in a three-credit capstone course and spend their final semester applying the skills and knowledge they learned in data analysis. For the capstone, students work in groups on a practical application of data science principles. Capstone team projects are chosen in consultation with the course instructor.
Course Requirements
The general requirements stated under Columbian College of Arts and Sciences, Graduate Programs.
30 credits, including 18 credits in required courses and 12 credits in elective courses.
| Code | Title | Credits |
|---|---|---|
| Required | ||
| DATS 6101 | Introduction to Data Science | |
| DATS 6102 | Data Warehousing | |
| DATS 6103 | Introduction to Data Mining | |
| DATS 6501 | Data Science Capstone | |
| DATS 6202 | Machine Learning I: Algorithm Analysis | |
| DATS 6401 | Visualization of Complex Data | |
| Electives | ||
| 12 credits in elective courses in DATS numbered 6000 or above. | ||