BAX 357 BAX 357. Predictive Analytics. 3 Hours.
Restricted to students in the McCombs School of Business Introduction to machine learning and artificial intelligence techniques with a focus on business applications and decision-making. Examine predictive frameworks, including tree-based techniques and artificial neural networks and their applications in business contexts. Explore solid foundations for evaluating models to understand their impact in a given business context, discuss issues related to algorithmic decision-making, and algorithmic bias and fairness. Three lecture hours a week for one semester. Only one of the following may be counted: Business Analytics 357, 372 (Topic 2), Management Information Systems 373 (Topic 17), Marketing 372 (Topic: Predictive Analytics and Data Mining), 372 (Topic 22). Offered on the letter-grade basis only. Prerequisite: Upper-division standing; Statistics 301, 301H, 309, or 309H; and Business Analytics 304, 305, or Management Information Systems 304.
Businesses are generating and collecting a massive amount of data from both business transactions and user generated data. Students who graduate with a degree in Business Analytics will be prepared to leverage statistical analysis, data mining, natural language processing, optimization, and machine learning to provide practical recommendations to improve business results in a wide variety of areas, including finance, marketing and supply chain management. They will also understand the ethical issues surrounding the design, development, and use of these technologies.