2026-2027 Undergraduate & Graduate Catalog [In Progress]
Bachelor of Science in Data Science and Analytics
University Degree Requirements as identified in the General Academic Regulations section of the catalog.
Data Science and Analytics Major Foundation Courses (18 credits)
- CIS 161 - Computing for Data Science Applications I (3 credits)
- CIS 164 - Computing for Data Science Applications II (3 credits)
- DSA 220 - Introduction to Data Science and Analytics (3 credits)
- MTH 204 - Linear Algebra I (3 credits)
- STA 215 - Introductory Applied Statistics (3 credits)
- STA 216 - Intermediate Applied Statistics (3 credits)
Required Courses (48 credits)
Data science and analytics majors must complete the following courses with a minimum 2.0 GPA.
- CIS 263 - Data Structures and Algorithms (3 credits)
- CIS 320 - Visualization of Data and Information (3 credits)
- CIS 335 - Data Mining (3 credits)
- CIS 358 - Information Assurance (3 credits)
- CIS 378 - Applied Machine Learning (3 credits)
- CIS 360 - Information Management and Science (3 credits)
- COM 203 - Argument and Analysis (3 credits)
- DSA 390 - Ethics and Professionalism in Data Science (3 credits)
- DSA 490 - Internship in Data Science and Analytics (1 to 3 credits)
- DSA 495 - Data Science and Analytics Capstone (3 credits)
- MTH 201 - Calculus I (4 credits)
- MTH 205 - Linear Algebra II (3 credits)
- STA 321 - Applied Regression Analysis (3 credits)
- STA 418 - Statistical Computing and Graphics with R (3 credits)
- STA 426 - Multivariate Data Analysis (3 credits)
- STA 311 - Introduction to Survey Sampling (3 credits)
Elective Courses (9 credits)
Data science and analytics majors must select nine or more credits of elective courses from the following:
CIS elective Choose one:
- CIS 331 - Data Analysis Tools and Techniques (3 credits)
- CIS 333 - Database Management and Implementation (3 credits)
- CIS 353 - Database (3 credits) OR AI 201 - Introduction to Artificial Intelligence (3 credits)
- HCC 304 - Usability Design and Evaluation (3 credits)
Statistics electives Choose two:
- STA 301 - Questionnaire Design and Execution (3 credits)
- STA 310 - Introduction to Biostatistics (3 credits)
- STA 314 - Statistical Quality Methods (3 credits)
- STA 315 - Design of Experiments (3 credits)
- STA 318 - Statistical Computing (3 credits)
- STA 421 - Bayesian Data Analysis (3 credits)
Application Domain Courses (6 credits)
Data science and analytics majors must complete 6 credits in an applied domain. Below is a non-exclusive list of pre-approved domain application courses.
- ANT 420 - Applied Anthropology (3 credits)
- ANT 305 - Methods in Biological Anthropology (3 credits)
- BIO 375 - Genetics (3 credits)
- CMB 451 - Bioinformatics: Tools and Techniques for Life Scientists (3 credits)
- CMB 452 - Computer Modeling and Drug Design (3 credits)
- CMB 460 - Genomics and Molecular Diagnostics (3 credits)
- ECO 300 - Data Analytics for Economics and Business (3 credits)
- ECO 385 - GIS in Urban and Regional Analysis (3 credits)
- ECO 400 - Econometrics and Forecasting (3 credits)
- GPY 307 - Introduction to Geographic Information Systems (3 credits)
- GPY 365 - GIS for Economic and Business Decision-Making (3 credits)
- GPY 385 - GIS in Urban and Regional Analysis (3 credits)
- GPY 407 - Advanced GIS (4 credits)
- GPY 470 - Digital Image Processing (3 credits)
- PLS 300 - Political Analysis (3 credits)
- PLS 350 - Comparative Public Opinion (3 credits)
Suggested Order of Coursework for a Major in Data Science and Analytics
This suggested order of coursework assumes that students will complete the D.S.A. foundation and general education courses and apply for admission with the help of their advisor.
First Year
- CIS 161 - Computing for Data Science Applications I (3 credits)
- CIS 164 - Computing for Data Science Applications II (3 credits)
- COM 203 - Argument and Analysis (3 credits)
- MTH 201 - Calculus I (4 credits)
- STA 215 - Introductory Applied Statistics (3 credits)
- STA 216 - Intermediate Applied Statistics (3 credits)
- Appropriate general education coursework (3 credits)
Second Year
- CIS 263 - Data Structures and Algorithms (3 credits)
- CIS 320 - Visualization of Data and Information (3 credits)
- DSA 220 - Introduction to Data Science and Analytics (3 credits)
- MTH 204 - Linear Algebra I (3 credits)
- MTH 205 - Linear Algebra II (3 credits)
- STA 311 - Introduction to Survey Sampling (3 credits)
- Appropriate general education coursework (12 credits)
Third Year
- CIS 335 - Data Mining (3 credits)
- CIS 258 - Introduction to Cybersecurity (3 credits)
- CIS 360 - Information Management and Science (3 credits)
- DSA 390 - Ethics and Professionalism in Data Science (3 credits)
- STA 321 - Applied Regression Analysis (3 credits)
- STA elective (3 credits)
- Appropriate general education coursework (12 credits)
Fourth Year
- CIS 378 - Applied Machine Learning (3 credits)
- DSA 490 - Internship in Data Science and Analytics (1 to 3 credits)
- DSA 495 - Data Science and Analytics Capstone (3 credits)
- STA 418 - Statistical Computing and Graphics with R (3 credits)
- STA 426 - Multivariate Data Analysis (3 credits)
- CIS elective (3 credits)
- STA elective (3 credits)