Handbook

The proliferation of digital platforms, IoT devices, and online transactions has led to an explosion of data. In today's data-driven world, organizations are increasingly leveraging advanced analytics methods to extract business insights from their forever growing data. Predictive analytics have emerged as pivotal tools for organizations to gain insights into customer behaviors, market trends, and operational efficiencies.

This course will guide students through fundamental business analytics methods to conduct predictive analytics. These methods are categorized into Supervised, Unsupervised, Reinforcement learning. We primarily use supervised learning and Reinforcement learning methods for predictive analytics projects. However, in the course of a predictive analytics project, analysts may use unsupervised learning techniques to understand the data and to expedite the model building process. 

Students will apply these business analytics methods using Python and SAS Viya. Python is a versatile programming language with rich libraries for data wrangling and analysis, while SAS Viya is a leading analytics platform that enables advanced predictive analytics at scale. Both meaningful hands-on experience and case studies describing organizational experiences with business analytics are included.