Data Science, Minor
Data Science applies core concepts in computer science, statistics and mathematics to problems in a wide variety of fields, from physical, social, biomedical, and behavioral sciences to arts and humanities. The minor targets students with strong analytical abilities and some existing programming experience, and requires courses in statistics, data-centric programming, data management, and data analysis. It also points to courses across the University that deal with data in areas of importance to Data Science.
SEAS Second Major or Minor Option
Students interested in a second major (College students only) or minor with SEAS are required to meet with the Undergraduate Curriculum Chair from the major/minor department you wish to declare to discuss requirements and obtain approval on the Second Major or Minor form. The approved form must be returned to the SEAS Research and Academic Services Office, 109 Towne Building.
For more information: http://www.seas.upenn.edu/undergraduate/degrees/minors.php
Data Science Minor
| Code | Title | Course Units |
|---|---|---|
| Core Requirements | 4 | |
| Programming Languages and Techniques I | ||
| Applied Machine Learning 1 | ||
or STAT 4710 | Modern Data Mining | |
or CIS 5200 | Machine Learning | |
| Scalable and Cloud Computing | ||
or CIS 5450 | Big Data Analytics | |
| Engineering Probability | ||
or ESE 4020 | Statistics for Data Science | |
or STAT 4300 | Probability | |
or STAT 4310 | Statistical Inference | |
| Data Science Electives | 2 | |
| Two electives required from two of the categories below. Approval required. | ||
| Data-Centric Programming | ||
| Computational Data Exploration | ||
| Introduction to Scientific Computing | ||
| Foundations of Data Science | ||
| Statistical Computing with R 2 | ||
| Data Analytics and Statistical Computing | ||
| Statistics | ||
| Statistics for Biologists | ||
| Discrete Probability, Stochastic Processes, and Statistical Inference | ||
| Engineering Probability | ||
| Probability | ||
| Applied Probability Models in Marketing | ||
| Data Collection, Representation, Management and Retrieval | ||
| Internet and Web Systems | ||
| Database and Information Systems | ||
| Crowdsourcing and Human Computation | ||
| Analytics in Excel VBA | ||
| Sample Survey Design | ||
| Data Analysis 1 | ||
| Applied Machine Learning | ||
| Artificial Intelligence | ||
| Machine Learning | ||
| Natural Language Processing | ||
| Data and Analysis for Marketing Decisions | ||
| Experiments for Business Decision Making (Center Special Topic) | ||
| Predictive Analytics for Business 2 | ||
| Forecasting Methods for Management | ||
| Modern Data Mining | ||
| Modern Regression for the Social, Behavioral and Biological Sciences | ||
| Applied Econometrics I | ||
| Modeling | ||
| Theory of Networks | ||
| Models for Marketing Strategy | ||
| Computer Simulation Models | ||
| Mathematical Modeling and its Application in Finance | ||
| Stochastic Processes | ||
| Total Course Units | 6 | |
- 1
Both CIS 4190/5190 and CIS 5200 cannot be taken for credit toward the minor.
- 2
STAT 4050 & STAT 4220 are 0.5 CU courses
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2024 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.