BUS 317 - Data Mining for Business Analytics

Credits: 3

A prerequisite for this course is the successful completion of an R tidyverse centric data analytics course. Preference to BSADM majors or DS, DSBA, ENTR minors during initial registration. Prerequisite: either BIOL 185, BUS 316, or CBSC 240; and at least junior class standing. Data mining is the science of discovering structure and making predictions in large, complex data sets. In the era of e-commerce and information economy, enormous amounts of data are generated daily from business transactions, networked sensors, social networking activities, website traffic, GPS systems, etc. Data-driven decision-making has become essential across a wide variety of functional areas in businesses such as targeted advertising, market segmentation, personalized recommendation, supplier/customer relationship management, product design, credit scoring, fraud detection and workforce management. This course serves as an introduction to Data Mining for students interested in Business Analytics. Students will learn about many commonly-used methods for predictive and descriptive analytics tasks. They will also learn to assess the methods’ predictive and practical utility.