Teaching

This course introduces students to the foundational and advanced techniques in artificial intelligence and machine learning as applied to business analytics. Through hands-on work in Python, students will gain proficiency in programming, data handling, and algorithmic thinking essential for modern data-driven decision-making. The course explores supervised and unsupervised learning methods, reinforcement learning for dynamic decision environments, and emerging applications of generative AI in business. In addition, select advanced topics will be covered to expose students to cutting-edge developments in AI and analytics. Emphasis is placed on applying these tools in real-world business contexts while also developing the skills to evaluate and communicate model performance effectively.

Modern businesses can benefit from analyzing information stored within datasets. Predictive analytics is one form of analysis that aims to discover trends or patterns within datasets so that the mathematical relationships that are found can be used to make proactive, future-based, data-driven decisions. Predictive analytics is an important field of study because the techniques that are utilized can find complex relationships that experts overlook or simply do not know during the time of the analysis. This course covers a variety of predictive analytics techniques and theories from the viewpoint of various business applications. In order to achieve the course learning objectives, students will utilize major software tools that are commonly used in business to develop, test, and apply continuous, classification, and clustering models.

Prescriptive analytics uses multiple techniques that recommend which course of action a decision-maker should take within a business environment. The goal is to utilize these techniques to determine optimal strategies that can improve the results related to business decisions. In this course, students will be introduced to concepts related to developing various linear models within major software tools that are commonly used by business professionals. Students will conduct an analysis of the prescriptive and optimization models as well as investigate other business scenarios that require additional methods such as linear regression.

In this course, you'll learn how to use data to gain valuable insights and make predictions that can help drive success in a business setting. We'll cover everything from data preprocessing and cleaning, to building and evaluating models, to post-processing and presenting your results. All using Python, which is one of the most popular and powerful programming languages for data analysis. You'll get hands-on experience with real-world datasets, and learn how to use popular libraries like pandas, numpy, and scikit-learn. You will learn how to apply these skills to various business scenarios such as customer segmentation, forecasting, and product optimization. By the end of this course, you'll have a good understanding of the principles of predictive business analytics, and have the skills to turn data into actionable insights to improve operations, increase revenue and optimize your performance.