BUS 60200 - Discovery With Data Mining

A predictive analytics course with an overview of creating and discovering value with several techniques such as principal component analysis, linear and logistic regression, clustering techniques. The techniques seek to find patterns and classifications that look toward the future, which not only provides a more complete understanding of data but enables managers to make better decisions.

Cr. 3.
Student Learning Outcomes

1. Determine and break down the differences between predictive, descriptive, and prescriptive analytics.
2. Compare and contrast the basic structure of supervised and unsupervised learning algorithms.
3. Operationalize predictive algorithms like linear and logistic regression, classification and regression trees, cluster analysis, and neural nets.
4. Design and build a data-driven story by analyzing objective definitions, applying computational analysis, generating a summary, and presenting visual insights gleaned from real-world datasets.
5. Create a final group project by acquiring data from real-world sources and applying and interpreting several statistical models to generate insight through effective delegation of responsibilities.