(AOL) Business Data Analytics Majors

Predictive Modeling ( BDAN 420 )

Taught by Dr. Lily Popova Zhuhadar ( Website Profile)

Course Overview

This course offers a comprehensive introduction to the essentials of data mining and data science through a hands-on, project-based approach. It focuses on applying the 7-Step Business Analytics Process Framework to real-world business scenarios. The curriculum covers a broad range of supervised machine learning algorithms and techniques, including regression, classification, regularization, dimensionality reduction, tree-based methods, as well as fully-connected, convolutional, and recurrent neural networks.Additionally, students will delve into advanced unsupervised learning techniques. They will learn to identify clusters of individuals and variables through cluster analysis and block clustering. The course also includes exploration of relationships among categorical variables using association rules, along with anomaly detection through autoencoders and probabilistic learning methods.

Learning Objectives

By the end of the semester, students will be able to:

• Critically Assess Solutions: Identify the most effective algorithms and methods to address the challenges posed in the final Capstone project.
• Effectively Present Solutions: Demonstrate the ability to articulate and present solutions to analytical problems encountered in the Business Analytics Final Project.

Student Deliverables

• Project Report: Students are required to submit a comprehensive project report of at least 10 pages, detailing their analysis, methodology, results, and conclusions.
• Final Presentation: Students will also deliver their findings and recommendations through a video pitch presentation, summarizing the key aspects of their project.

Capstone Final Project Overview

In lieu of a traditional final exam, students engage in a comprehensive Capstone project that serves as the pinnacle of their business data analytics education. This project allows students to showcase their abilities in strategic business thinking, effective communication, and consulting skills. They will tackle real-world business cases from a variety of industries and application areas, demonstrating the strategic value of analytics.Students will be assigned specific themes, which they will use to develop detailed business and project execution plans. These themes span various domains, including business analytics, finance analytics, marketing analytics, health analytics, and sports analytics. This ensures that each student can focus on a sector of particular interest to them.For dual-major students, there is the option to select Capstone projects that are closely aligned with their primary major, with a specific emphasis on analytics modeling. Throughout the project, students will work independently, applying their chosen analytics focus to deliver insightful, industry-applicable solutions.

Course Syllabus

Here is a direct link to Syllabus.