Topics are chosen from business analytics topics that extend explorations of content in existing courses or allow exploration of content not duplicated in regular course offerings. May be repeated for credit with different topics. Prerequisites: Open to students by permission of instructor or to those who satisfy prerequisites determined by the instructor.
| Distribution Area | Prerequisites | Credits |
|---|---|---|
| Open to students by permission of instructor or to those who satisfy prerequisites determined by the instructor. | 1/4-1/2-1 course |
Fall Semester information
McKenzie Lamb390A: Topics:Advanced Data Visualization in Tableau
Assorted data visualization topics, including data wrangling using Tableau Prep Builder; connecting to and querying databases; relationships, joins, blends, and other aspects of the Tableau Data Model; data densification; geospatial functions; dynamic dashboards; principles of design for interactive graphics. Based on real-world case studies. A solid understanding of Tableau basics is essential, but no other technical background is required.
Staff
390B: Topics:Marketing Analytics
The course is designed to equip students with the skills necessary to extract meaningful insights from data and apply them to marketing challenges. Students will learn how to apply data analytics techniques in critical marketing decisions such as market segmentation, demand estimation, customer lifetime value (CLV) assessment, pricing strategies and product development. The analytical techniques will include fundamental concepts such as data visualization, basic descriptive statistics and linear regression; more advanced methods such as logistic regression, cluster and factor analysis, KNN, random forests and decision trees; and higher-level methods such as text and image processing techniques, neural networks, generative AI and large language models. Students will also be exposed to methods used for causal inference, the trade-off between prediction accuracy and interpretability, and model validation.