Curriculum

The Master of Science in Data Science requires 30hrs of coursework and a capstone project. Students may elect to conduct research and write a thesis instead of the capstone.

Non-thesis Track

Course List

Code	Title	Hours
Statistics Requirements		6
STAT 408	Applied Regression Analysis
STAT 410	Categorical Data Analysis
Computer Science Requirements		6
COMP 453	Database Programming
COMP 458	Big Data Analytics
Data Science Core		9
DSCI 401	Introduction to Data Science
STAT 438	Introduction to Predictive Analytics
or COMP 479	Machine Learning
DSCI 470	Data Science Consulting (Capstone)
Three Statistics or Computer Science 400 Level Electives 1		9
Total Hours		30

¹

A list of all 400 level Computer Science courses can be found here. A list of all 400 level Statistics courses can be found here. 

Thesis Track

Course List

Code	Title	Hours
Statistics Requirements		6
STAT 408	Applied Regression Analysis
STAT 410	Categorical Data Analysis
Computer Science Requirements		6
COMP 453	Database Programming
COMP 458	Big Data Analytics
Data Science Core		7
DSCI 401	Introduction to Data Science
STAT 438	Introduction to Predictive Analytics
or COMP 479	Machine Learning
Research		11
DSCI 494	Data Science Research Design
DSCI 499	Data Science Research
DSCI 595	Thesis Supervision
Total Hours		30

All PhD students and Master’s students who are writing a thesis must successfully complete UNIV 370 Responsible Conduct in Research and Scholarship or other approved coursework in responsible conduct of research as part of the degree requirements. It is strongly recommended that students complete this two-day training before beginning the dissertation/thesis stage of the program.

Graduate & Professional Standards and Regulations

Students in graduate and professional programs can find their Academic Policies in Graduate and Professional Academic Standards and Regulations under their school. Any additional University Policies supercede school policies.

Learning Outcomes

• The ability to manage large data sets in preparation for data science analysis.
• A working knowledge of statistical techniques and computer algorithms, and the ability to apply these methods to a wide array of real-world problems.
• The ability to perform a data science analysis from beginning to end while adhering to the principles of reproducible and ethical research.
• The ability to program in both the R and Python programming languages.
• Complete a project demonstrating competence in the field of data science.
  • Non-thesis track: Students will be required to complete a real-world data science project prior to graduating from this program, either through our consulting course, an internship, an independent study, or other appropriate project
  • Thesis track: Students will be required to undertake a research project culminating in a thesis