Business Data Analytics

This course provides students with a practical introduction to the core concepts of business analytics, ensuring they become familiar with descriptive, predictive, and prescriptive analytics. It is designed as a rigorous but accessible program for motivated students who want to understand how data and information streams drive today’s businesses and organizations.

The course begins with descriptive analytics, focusing on data analysis and data visualization to understand, manipulate, and evaluate complex data. It then develops the tools needed for predictive analytics, utilizing statistical techniques, data mining, and machine learning to forecast future trends and alternative actions. Along the way, students will apply prescriptive analytics by using various analytical models, such as optimization and simulation, to support decision-making. Furthermore, students will explore the growing role of Artificial Intelligence (AI) in transforming business analytics and automating complex, data-driven decisions.

Learning Outcomes

By the end of this course, students should be able to:

• undertake a wide range of business analytics projects using primary data sources for analysis
• apply descriptive and predictive analytics, including data visualization, statistical techniques, and machine learning, to evaluate complex data and forecast future business trends
• implement prescriptive analytical models, such as optimization and Monte Carlo simulation, to support robust decision-making alongside emerging Artificial Intelligence applications

Topics Covered

• Introduction to business analytics: descriptive, predictive, and prescriptive frameworks
• Descriptive statistics and data visualization using tables and charts
• Descriptive data mining, including cluster analysis
• Predictive analytics: linear regression, logistic regression, and classification trees
• Prescriptive analytics: spreadsheet modeling and optimization
• Monte Carlo simulation
• Managerial issues in analytics and practical case applications
• Emerging topics such as Artificial Intelligence (AI) and machine learning

Capstone Project

Students will select a business analytics topic of their choice in consultation with the instructor. The project involves delivering a presentation on the chosen topic at the end of the course and is broken down into two main components:

• Proposal: Students must submit a project proposal that outlines their selected topic and explains why it is interesting. 
• In-Person Presentation: Students will deliver a live presentation to their classmates, who serve as the target audience. Students are required to submit their presentation slides in PDF format, which must include a proper bibliography and footnotes. The presentations will also be peer-evaluated by other students.

Students have the flexibility to choose from a wide variety of subjects for this project. A partial list of suitable topics includes predictable AI & machine learning, operations and revenue management applications, the use of LLMs, sports analytics, real-time analytics and Internet-of-Things (IoTs), and explainable AI (XAI) and data governance.

Hands-on Activities

The course includes practical, computer-based exercises and a comprehensive group case study to apply business analytics concepts.

• Excel labs: Students will use Microsoft Excel for in-class activities during designated "lids up" times. The labs align with the lecture material to provide immediate practical application.
• Case study: Students will work collaboratively to analyze a real-world business scenario, presenting their findings to a specific target audience.

*Information is subject to change as instructors finalize course syllabi