Statistics and Data Science, Minor
The aim of statistical modeling is to empower effective decision making, and the field's unique contribution is its ability to incorporate multiple levels of uncertainty in the framing of wise decisions. Over the last few years, the development of new computational tools and the unprecedented evolution of “big data” have propelled statistical modeling to new levels. Today, statistical modeling and machine learning have reached a level of impact that no large organization can afford to ignore. The information landscape is changing as it has never changed before.
For more information: https://statistics.wharton.upenn.edu/programs/undergraduate/statistics-minor/
Statistics and Data Science, Minor
This minor is for students outside of Wharton. Single-degree and dual-degree students with Wharton may pursue a statistics concentration instead.
| Code | Title | Course Units |
|---|---|---|
| Three Required Courses | ||
| MATH 1410 | Calculus, Part II | 1 |
| or MATH 1080 | Mathematics of change, Part II | |
| or MATH 1610 | Honors Calculus | |
| STAT 1020 | Introductory Business Statistics | 1 |
| or STAT 1028 | Introductory Business Statistics | |
| or STAT 1120 | Introductory Statistics | |
| or STAT 4310 | Statistical Inference | |
| or ESE 4020 | Statistics for Data Science | |
| or ECON 2310 | Econometric Methods and Models | |
| STAT 4300 | Probability | 1 |
| or ESE 3010 | Engineering Probability | |
| Additionally select 4 CU's from the following electives: | 4 | |
| Statistical Computing with R | ||
| Data Collection and Acquisition: Strategies and Platforms | ||
| Predictive Analytics for Business | ||
| Applied Machine Learning in Business | ||
| Text Analytics | ||
| Mathematical Statistics | ||
| Stochastic Processes | ||
| Forecasting Methods for Management | ||
| Introduction to Bayesian Data Analysis | ||
| Data Analytics and Statistical Computing | ||
| Modern Data Mining | ||
| Data Science Using ChatGPT | ||
| Sample Survey Design | ||
| Applied Probability Models in Marketing | ||
| Introduction to Python for Data Science | ||
| Advanced Statistical Computing | ||
| Convex Optimization for Statistics and Data Science | ||
| Numerical Optimization for Data Science and Machine Learning | ||
| Applied Econometrics I | ||
| Applied Econometrics II | ||
| Total Course Units | 7 | |
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2025 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.