Foundational Component (12 required courses)

• CS 151  -  Computer Science through Programming
• CS 295  -  Discrete Structures or
• MA 295  -  Discrete Structures or
• MA 395  -  Discrete Methods
• DS 303  -  Discovering Information in Data or
• CS 403  -  Discovering Information in Data
• DS 496  -  Ethical Data Science Capstone
• IS 251  -  Data Analytics, Artificial Intelligence, and Information Systems or
• BH 251  -  Data Analytics, Artificial Intelligence, and Information Systems *
• IS 353  -  Data Management and Database Systems or
• CS 485  -  Database Management Systems
• IS 358  -  Applied Machine Learning
• MA 251  -  Calculus I
• ST 210  -  Introduction to Statistics or
• ST 265  -  Biostatistics or
• EC 220  -  Business Statistics
• ST 310  -  Statistical Computing
• ST 465  -  Advanced Statistical Methods
• ST 472  -  Applied Multivariate Analysis

•  

  * Restricted to Sellinger Scholars.

Elective Component

Students must complete three courses from the following:

• CS 312  -  Object-Oriented Software Design
• CS 456  -  Web Programming or
• IS 458  -  Web-Enabled Entrepreneurial Project with Artificial Intelligence
• CS 484  -  Machine Learning
• EC 420  -  Econometrics
• EC 425  -  Applied Economic Forecasting
• IS 301  -  Location Analytics
• IS 310  -  Climate Technology and Analytics
• IS 330  -  Data Pipelines and Platforms
• IS 420  -  Artificial Intelligence in Business
• IS 453  -  Information Systems Analysis and Design
• IS 454  -  Artificial Intelligence Systems Architecture
• IS 460  -  Data Visualization
• IS 470  -  Sports Analytics
• MA 301  -  Introduction to Linear Algebra
• MA 481  -  Operations Research
• MA 485  -  Stochastic Processes
• MK 415  -  Digital Marketing and Analytics
• PH 307  -  Mathematical Methods in Physics
• ST 461  -  Elements of Statistical Theory I: Distributions
• ST 466  -  Experimental Design
• ST 473  -  Statistical Learning and Big Data
• ST 475  -  Survival Analysis and Generalized Linear Models
• ST 476  -  Statistical Sports Modelling
• Appropriate course as approved by program director

First Year

Sophomore Year

Junior Year

Senior Year

Notes:

1. EC 102  and EC 103  are recommended to fulfill the Social Science Core.
2. MA 151  may be substituted for MA 251  with permission of the program director.
3. CS 212  is a prerequisite for CS 312 . MA 351  is a prerequisite for ST 461 .
4. Up to two graduate-level Data Science courses may be taken as a DS Elective with approval from the program director.
5. CS 485  is offered every other year.
6. ST 465  is offered every year in the fall semester, while ST 472  is offered every year in the spring semester.
7. ST 310  is offered every year in the fall and spring semesters.
8. CS 403  should be taken instead of DS 303  by double majors in computer science and data science. CS 312  is a prerequisite for CS 403 .
9. The Data Science major cannot be combined with a minor in Computer Science, Information Systems and Data Analytics, or Statistics. Students may double major with these programs.