Business Artificial Intelligence

Units: 3

This course will discuss artificial intelligence [AI] and its applications to help make data-driven decisions in business and management. Students will learn to understand the language of AI, discuss different applications of AI, and work with actual AI tools at a high level to develop new insights. Hands-on examples will be complemented by applications of AI drawn from different industries.

Offered in Fall and Spring

Units: 3

This course is designed around the full analytics lifecycle which encompasses the business problem, the data, the analysis, and the decision. Students will learn to identify and clearly explain business problems that can be addressed with analytics. They will learn to determine which analytic methods are best suited to solve particular problems and clearly explain the results of an analytic model and how those results might impact the business bottom line. Analytical methods to be covered include data, visualization, a review of regression analysis; logistic regression; classification and regression trees [including boosting and bagging methodologies]; and clustering [segmentation] methods. Students will also develop at least a beginning proficiency with several statistical software packages including Tableau, JMP, R, and SAS Enterprise Miner. Emphasis will be placed on analyzing real data and understanding how analytical thinking can be applied to solve big data problems.

Offered in Fall and Spring

Units: 3

This course examines how to collect and process data to make it useful, how to validate, protect, and process data to make it available, and how to create a place to properly store data.

Offered in Fall and Summer

Units: 2

This class will give you the opportunity to explore the ethical issues associated with the development and use of Big Data and data science activities. The data science lifecycle will serve as a backdrop within which to examine ethical concerns related to the design, collection, use and dissemination of data. These include issues of AI and bias, transparency in communications and data design, privacy and data collection, among others.

Offered in Spring Only

Units: 3

This course focuses on solving a real-world business problem that includes a heavy data analytic component. The business problem will vary according to the client but could include problems from finance, human resources, marketing, finance, supply chain, or other management areas.

Offered in Fall and Spring

Units: 3

Analytical techniques to convert a wealth of data on customers and markets into insights to guide business decisions. Taking a hands-on and systematic approach on the steps involved in harnessing knowledge from data, the course covers the various data techniques and steps involved in data- and model-driven management decisions. Techniques include market response models, conjoint analysis, discrete choice models.

Offered in Fall and Spring

Units: 3

This course covers the basics of digital marketing from an analytics perspective. Each channel of digital marketing, such as search engine optimization, social, mobile, web, email, and video, are examined and their relationship to overall firm marketing strategy is explored.

Offered in Fall and Spring

Units: 1 - 6

Presentation of material not normally available in regular courses offerings or offering of new courses on a trial basis.