BSAN - Business Analytics
Courses numbered 100 to 299 = lower-division; 300 to 499 = upper-division; 500 to 799 = undergraduate/graduate.
BSAN 675. Analytics Decision Modeling with Spreadsheets (3).
Cross-listed as FIN 675. Introduces key principles of business analytics modeling: descriptive, predictive and prescriptive. Models covered in each area may differ from semester to semester. Students learn how to make decisions not based on intuition or “gut feel,” but on models and data. Course adopts a practical approach to the modeling of a wide variety of business problems in various functional areas. Models are built in Excel and add-ins to Excel, allowing students to gain advanced Excel skills, which will benefit them in their careers. For undergraduate credit only. Prerequisites: DS 350 and FIN 340 each with a grade of C or better; BADM 162, ECON 231, and ECON 232 or equivalents; junior standing; advanced standing; or instructor's consent.
BSAN 734. Introduction to Data Mining and Analytics (3).
Introduces the theory, application and interpretation of basic analysis methods for analyzing existing datasets. Topics include data preprocessing, data exploration and visualization as well as specific methods for regression, classification, cluster analysis and association analysis. Focuses on learning the data mining tasks that each method addresses, the assumptions of each method, the inputs needed, the outputs, interpretation of results, and evaluation of the quality of the analysis. Uses Python to cover these different methods. Course is mainly targeted for graduate students. Students cannot receive credit for both BSAN 734 and IME 734.
BSAN 775. Perspectives on Business Analytics (3).
Overview of the different perspectives of the field of analytics from math to computer science to business and more. Focuses on business analytics, starting with sources of big data, data collection and the ethical challenges associated with using data. Covers the various deterministic and prescriptive optimization models using scenarios from various business functions (operations/supply chain, finance, marketing, human resources, etc.). Students learn how to frame the problem, formulate it, solve it with Excel, then analyze and report the results. Course provides a good understanding of data analytics as applied to business, but also an appreciation for the importance of the field of data science. For graduate credit only. Prerequisite(s): familiarity with Excel and graduate student status.