Minor Requirements
22-24 credits
Computer Science Requirements (8 credits)
| CSCI-1040 | Computer Science I: Intro to Comp Sci | 4 credits |
| CSCI-2025 | Data Manipulation and Visualization | 4 credits |
Mathematical Foundations (8 credits)
| MATH-1050 | Applied Calculus: A Modeling Approach | 4 credits |
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| or | |
| MATH-1075 | Single Variable Calculus | 4 credits |
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| or | |
| MATH-2075 | Multivariable Calculus | 4 credits |
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| MATH-2025 | Multiple Regression Analysis | 4 credits |
Data in Context (4 credits)
Option 1
Complete one 4-credit course and any associated lab from the following.
If possible, students are strongly encouraged to complete courses from their major or major field. Each of the following classes requires students to think about at least one of the following: 1) What does data look like in their chosen field of study? 2) Why is data important for their chosen field of study? 3) Why is their chosen field of study important for data science?
Option 2
Alternatively, complete PHIL-3018 Artifical Intelligence and at least two additional credits of CSCI at any level or MATH at the 3000 level or higher.
| CSCI- | Elective CSCI coursework, any level | 2+ credits |
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| or | |
| MATH- | Elective MATH coursework, 3000- and/or 4000-level | 2+ credits |
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| PHIL-3018 | Artificial Intelligence | 2 credits |
Advanced Elective (2 -4 credits)
Complete one additional course and any associated lab from the following. These courses provide students with at least one of the following: 1) additional computational skills applicable to data science; 2) additional mathematical or statistical content applicable to data science; and/or 3) experience analyzing data with a field of study of the student’s choosing.