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.
For more detailed information, see the curriculum worksheet.
- 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.
Required Courses (47 credits)
- 50:640:121 Calculus I OR 50:640:123 Active Calculus I (4 credits)
- 50:640:122 Calculus II OR 50:640:124 Active Calculus II (4 credits)
- 50:640:221 Calculus III (4 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)
- 50:198:213 Data Structures (3 credits)
- 50:640:314 Elementary Differential Equations OR 50:198:357 Introduction to Computational Mathematics (3 credits)
- 50:640:331 Probability and Stochastic Processes (3 credits)
- 50:960:336 Applied Statistics OR 50:960:489 Statistical Models (3 credits)
- 50:198:414 Artificial Intelligence (3 credits)
- 50:198:454 Machine Learning (3 credits)
400-level Elective Courses (9 credits)
- Any three Mathematics (640), Statistics (960), or Computer Science (198) courses at 400-level
Total: 56 credits