This course covers fundamental statistical concepts and basic computational tools in data analysis. The goal is to learn how to perform descriptive and predictive data analysis based on real datasets. This course also serves as a quantitative foundation for Chicago Booth elective courses in marketing, finance, economics and more advanced courses in data science.
The topics to be covered are: (1) descriptive data analysis, and data visualization; (2) statistical modeling and inference, bootstrap; (3) regression analysis: linear, logistic regression, (4) model fitting and diagnostics; (5) basic predictive tools in machine learning.
If you have a weak math background, math review course prior to start of class is recommended.
Business Statistics or Applied Regression?
Please take the pre-MBA exam to figure out which class would be a better fit.
You can access the exam and related materials from the canvas site below:
https://canvas.uchicago.edu/courses/36612
There are no required texts for the course.
Based on homework, a mid-term, and final exam. No pass/fail grades. No auditors.
- Mandatory attendance week 1
- No auditors
- No pass/fail grades
Description and/or course criteria last updated: June 05 2024
