BSAN 450. Data Mining and Predictive Analytics. 3 Hours.

Overview of techniques for gathering, exploring, transforming, modeling, and summarizing data sets including very large data sets, both structured and unstructured. Basic data mining techniques including neural networks, decision trees, clustering algorithms, linear programs, text and web mining in business setting. Modeling approaches include techniques from supervised and unsupervised machine learning. Discussion of data cleaning and data preparation issues, including noise, missing and unbalanced data, discrete versus continuous features, and feature selection. Some techniques are implemented from scratch, while in other cases real-world tools such as R, Python packages and commercial data modeling tools are applied to large-scale data sets. Prerequisite: BSAN 415 or SCM 415. Enrollment restricted. LEC.

Bachelor of Science in Business

http://catalog.ku.edu/business/bs/

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