BUSINESS ANALYTICS II

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 207

Enables you to understand and apply useful approaches to analyzing and presenting data to support business decision making. Emphasis on applications of predictive and prescriptive analytics. Predictive approaches use historical data to infer relationships and forecast future outcomes. Prescriptive methods formulate decision models to identify choices that are optimal with respect to a desired, measurable outcome. Provides experience integrating diverse data sources, modelling uncertainty, and visualizing key insights.

GENBUS 656

Introduction to machine learning techniques in business. Focus on applications for solving business problems, including hands-on practice in the context of various real-world data sets. Materials covered include machine learning foundations, different methodological approaches, and implementation tools for machine learning for business applications. Methods include both supervised learning techniques (linear regression and classification, non-linear regression, CARTs, random forests, SVMs, artificial neural nets, etc.) as well as unsupervised learning techniques (clustering, principal components, etc.).

GENBUS 306

Development of quantitative intuition through practical applications and use of analysis tools. Specifically, emphasis will be on how to manage, summarize, explore, and visualize databases. The essentials of probability will be introduced and applied to decision problems where there is uncertainty. Emphasis on hypothesis testing and regression analysis and include an introduction to simulation methods. Throughout, attention will be paid to effective communication of data analysis. The use of business cases will connect the course material to both real world settings and recent advances in data analysis, including big data and data mining.

GENBUS 886

An introduction to predictive modeling for business applications beginning with some of the foundations, including a primer in matrix algebra and numerical optimization. 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.