Minor in Data Science
Data science is a methodological approach rather than a substantive field, one which is integrated into the ways in which we assess the world. This is reflected in the requirement that ¼ø»Æʦapp who minor in data science take introductory coursework and then apply insights from this coursework to an empirical project.
Chaves-Fonnegra, Fily, Hill, Lanning, McGovern, Ruest, Verma
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Note that at least 9 credits of the minor must be at the upper level (3000 or 4000) and only 4 credits of courses may be double-counted with the student’s concentration.
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I. Three required courses (10 credits): Students must complete all of the following courses with a minimum GPA of 2.0:
| Course | Title | Prereq | Credit |
|---|---|---|---|
| COP 3076 | Honors Introduction to Data Science | STA 2023 | 3 credits |
| COP 2000* | Honors Foundations of Programming | Ìý | 3 credits |
| MAC 2311 | Honors Calculus with Analytic Geometry I | MAC 1147 | 4 credits |
* If COP 2000 is unavailable, COP 2220 (Introduction to Programming in C) may be taken via Distance Learning through the College of Engineering and Computer Science.
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II. Minimum 6-8 credits of data-relevant courses: Students must take at least six credits in additional data-relevant courses from the following list. No more than four of these credits may be counted towards the major concentration:
| Course | Title | Prereq | Credit |
|---|---|---|---|
| ART 3657C | Honors Introduction to Programming for Visual Arts | Ìý | 4 cr |
| BSC 4930 | Honors Experimental Design and Data Analysis for Biology | STA 2023 | 3 cr |
| CHM 3121/L | Honors Quantitative Analysis/Lab | CHM 2045/L, 2046/L | 4 cr |
| COP 3012 | Honors Advanced Programming (or any other upper level course with COP or COT prefix) | COP 2000 | 3 cr |
| ECO 4412 | Honors Econometrics: Applied Regression Analysis | STA 2023 | 3 cr |
| GIS 3044C | Honors Geographic Information Systems | Ìý | 3 cr |
| ISS 4304 | Honors Computational Social Science | Ìý | 3 cr |
| IDS 3932 | Honors Beginner’s Programming for Biologists | Ìý | 3 cr |
| IDS 3932 | Honors Empirical Analysis of Investment/Financial Markets | Ìý | 3 cr |
| IDS 3932 | Honors Art and Science of Data Visualization | Ìý | 1 cr |
| MAD 2104 | Honors Discrete Mathematics | Ìý | 3 cr |
| MAT 4930 | Honors Intro to Computational Science | Ìý | 3 cr |
| PHY 4523 | Statistical Physics | PHY 2049 | 3 cr |
| PHY 4936 | Honors Computational Physics | PHY 3221, PHY 3323 | 3 cr |
| PSY 3213/L | Honors Research Methods in Psychology/Lab | PSY 1012 | 4 cr |
| STA 3164 | Honors Intermediate Statistics (or any upper level course with STA prefix) | STA 2023 | 3 cr |
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III. Data proficiency (0-3 credits):
The student must demonstrate data proficiency. This may be accomplished either by:
- Submitting a thesis in the student’s Concentration to the Data Science Minor Advisory Board which the board approves as demonstrating data proficiency.
- Directed Independent Study (1-3 credits) in which the student demonstrates data proficiency as documented by completing the form at: http://bit.ly/WHCDSMinorProficiency and having a board member review and confirm.
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TOTAL CREDITS: 16-21 (NOT INCLUDING PREREQUISITE COURSES)