BUSINESS STATISTICS USING PYTHON

GENBUS 656

An introduction to predictive modeling for business applications beginning with some of the foundations. Leads to development of linear regression and classification models, and discussion of building models for prediction. Topics include selection, regularization, and the bias-variance tradeoff.

GENBUS 730

Introduction to fundamentals of prescriptive analytics with emphasis on business applications. Modeling and mathematical optimization using Excel and Python. Designing, building, testing, and analyzing models, including sensitivity and risk analysis. Developing and solving optimization models, including linear, integer, and nonlinear problems. Course includes some principles of model-building and fundamentals of optimization theory but emphasizes practical application, hands-on learning, and problem-driven exercises.

GENBUS 206

Learn basic business statistical skills to comprehend business reports, and to calculate statistical findings from business data using Excel. Understand simple probability calculations and how to apply probability to specific business uncertainties. Build capability to use simple statistical models and tests to estimate business variables of interest, and learn about predictive relationships between variables. Become comfortable using Excel for data manipulation, data analytics, and data visualization. Apply statistical analysis to address business issues.

GENBUS 705

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.

GENBUS 704

Exploration of statistical inference and data analytics tools. Review of relevant foundations of statistics, machine learning and probability theory. Emphasis on applying the resulting concepts to canonical business examples, using both Excel and R.