MS Degree


Female student paying attention in class


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.

All graduate students in the Data Science Program work with an assigned advisor to choose electives, create a plan of study and navigate questions related to prerequisites and course registration. Professors and advisors also work to connect students with opportunities that can open doors to internships and full-time positions.



"I enjoyed meeting and learning with my fantastic classmates who provided me with valuable insight during our group projects."

Robert Rapena

MS ’18


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


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.

3 older students discussing in a data science capstone course


Course Requirements

The following requirements must be fulfilled:

The general requirements stated under Columbian College of Arts and Sciences, Graduate Programs.

30 credits, including 24 credits in required courses and 6 credits in elective courses.

DATS 6101Introduction to Data Science
DATS 6102Data Warehousing
DATS 6103Introduction to Data Mining
DATS 6501Data Science Capstone
DATS 6202Machine Learning I: Algorithm Analysis
DATS 6203Machine Learning II: Data Analysis
DATS 6401Visualization of Complex Data
DATS 6402High Performance Computing and Parallel Computing
6 credits in elective courses from the following:
DATS 6201Numerical Linear Algebra and Optimization
DATS 6499Data Science Applied Research
DATS 6450Topics in Data Science
GEOG 6304Geographical Information Systems I
STAT 6210Data Analysis
STAT 6214Applied Linear Models
STAT 6242Modern Regression Analysis