For the Data Science track, a student must satisfactorily complete with a grade of C or better each of the following courses. Changes from the standard program must be approved by the department.
- Undergo rigorous training for a career as an AI architect, data scientist, machine learning engineer, etc.
- Acquire quantitative skills to extract insights and draw conclusions from data in context.
- Achieve a mathematical understanding of statistical inference, machine learning, and related tools that underlie AI.
- Gain an appreciation for the use of AI in science, technology, and a variety of other fields.
Foundational Courses (29 credits)
- 50:640:121, 122, 123 Calculus I, II, and III (12 credits)
- 50:640:250 Linear Algebra OR 50:640:253 Linear Algebra with Applications (3 credits)
- 50:960:183 Elementary Applied Statistics OR 50:960:185 Introduction to Data Science (3 credits)
- 50:198:111 Programming Fundamentals (4 credits)
- 50:198:113 Object Oriented Programming (3 credits)
- 50:198:171 Mathematical Foundations of Computer Science (3 credits)
- 50:640:199 Exploring Careers in Mathematics (1 credit)
Mid-level Required Courses (6 credits)
- 50:640:331 Probability and Stochastic Processes (3 credits)
- 50:198:213 Data Structure (3 credits)
Mid-level Elective Courses (3 credits – choose 1 course from the list below)
- 50:640:314 Elementary Differential Equations (3 credits)
- 50:198:357 Introduction to Computational Mathematics (3 credits)
Upper-level Required Courses (9 credits)
- 50:960:489 Statistical Models (3 credits)
- 50:198:414 Artificial Intelligence (3 credits)
- 50:198:454 Machine Learning (3 credits)
Upper-level Elective Courses (9 credits – choose 3 courses from the list below)
- 50:640:450 Advanced Linear Algebra (3 credits)
- 50:640:470 Introduction to Optimization (3 credits)
- 50:640:497 Advanced Computational Math (3 credits)
- 50:640:499 Data Visualization (3 credits)
- 50:960:481 Mathematical Statistics (3 credits)
- 50:960:491 Regression and Time Series (3 credits)
Total: 56 credits