STATISTICS AND PROGRAMMING FOR BUSINESS ANALYTICS
A compact primer in statistics and an introduction to programming as a foundation for data-driven business analyses. The first part covers elementary concepts such as random variables, probability distributions, estimation, and ordinary least-squares regression. In the second part, the course exposes students to Python and R programming, including numerical and statistical packages that are relevant for practical applications in business.
Prerequisites & course information
Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Summer 2026 Learning Outcomes: 1. Derive measures related to the characteristics of probability functions such as moments, mean, variance, median,mode, and percentiles. Audience: Graduate 2. Carry out an ordinary least-squares (OLS) regression and interpret the parameter estimates as well as predictions. Audience: Graduate 3. Prepare, compile, and run basic computer programs in a variety of programming languages including C/C++,Java, Python, R, Stata, and VBA. Audience: Graduate 4. Implement algorithms for business analytics problems in a structured fashion in Python and R. Audience: Graduate 5. Pick suitable numerical functions from available packages, including quadrature (numerical integration), numerical solution of non-linear equations, optimization, and random-number generation, and use the functionality for business analyses. Audience: Graduate 6. Run a Monte Carlo simulation using R and Python. Audience: Graduate Learning Outcomes: 1. Derive measures related to the characteristics of probability functions such as moments, mean, variance, median,mode, and percentiles. Audience: Graduate 2. Carry out an ordinary least-squares (OLS) regression and interpret the parameter estimates as well as predictions. Audience: Graduate 3. Prepare, compile, and run basic computer programs in a variety of programming languages including C/C++,Java, Python, R, Stata, and VBA. Audience: Graduate 4. Implement algorithms for business analytics problems in a structured fashion in Python and R. Audience: Graduate 5. Pick suitable numerical functions from available packages, including quadrature (numerical integration), numerical solution of non-linear equations, optimization, and random-number generation, and use the functionality for business analyses. Audience: Graduate 6. Run a Monte Carlo simulation using R and Python. Audience: Graduate
Sources
- Course catalogue ↗2026-2027