FOUNDATIONS OF STATISTICAL LEARNING FOR BUSINESS ANALYTICS

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 657

An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.

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 317

Statistical inference and analyses based on models will be introduced and applied in a business context using a calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified so decision makers have information about the quality of the estimates. Regression and time series models are commonly used in business analytics applications and will be used to analyze business data and make inferences and predictions.

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.