Online MBA in Business Analytics

Survey of the concepts, principles, and foundational ideas about data that unpin the practice of data mining and analytics in professional business settings.  Prerequisite: QM 2013 with a grade of C or higher. 

An introductory survey of the technologies used to uncover patterns and associations in large databases. The goal is to better understand business situations and improve business decision making. While data mining techniques can be applied to various disciplines, this course specifically focuses on their use to improve business processes and performance. Applications will focus on various business problems and activities, such as fraud detection, market basket analysis, cross-selling, churn, market segmentation, credit rating, process management, and sports management. Students will gain hands-on experience in using computer software to access and mine business data sets.

This course, “AI for Business,” is designed to equip future business leaders with the essential knowledge and strategic insights necessary to harness the power of artificial intelligence (AI) in modern business environments. The course focuses on the transformative potential of AI technologies across various business sectors, including finance, marketing, operations, and human resources. Students will explore the foundational concepts of AI and machine learning, emphasizing their strategic applications rather than the underlying technical complexities. Through case studies from leading industries and interactive discussions, the course will cover successful implementations of AI that enhance decision-making, optimize operational efficiency, and drive customer engagement.

An introduction to basic concepts in collection, presentation, analysis, and interpretation of data, emphasizing capabilities of different statistical techniques and business applications. Topics include descriptive statistics, probability theory, statistical estimation, hypothesis testing, analysis of variance, regression analysis, and nonparametric statistical techniques. Statistical computing using R and an introduction to data visualization using R and/or Tableau.