Applied Analytics (AA)

AA 630. Data Management for Analytics. 3 Credit Hours.

This course introduces students to the fundamentals of working with data through hands-on experience in SQL and data visualization tools. In the first part of the course, students will learn the fundamentals of data management, how data is stored, structured, and accessed in relational databases, and how to write efficient SQL queries to extract, transform, and analyze data. In the second part of the course, students will explore how to communicate insights through visual storytelling using data visualization software. They’ll learn principles of effective data visualization and how to use software tools to create clear, compelling visuals that support analysis and decision-making. By the end of the course, students will be able to manage data confidently and create visualizations that make complex information accessible and meaningful.
Session Cycle: Every Spring.

AA 640. Data Visualization and Text Mining. 3 Credit Hours.

This course introduces students to Python-based techniques for accessing, cleaning, and analyzing data. The first half focuses on structured data, guiding students through building data processing pipelines, implementing transformation workflows, and applying analytics techniques to prepare data for analysis. Students will learn how to handle common data challenges and write efficient Python code for data manipulation. In the second half, the course explores text mining and the unique challenges of working with unstructured data such as text. Students will be introduced to tools and methods for extracting insights from text in various contexts, including social media, surveys, and documents. By the end of the course, students will be able to build end-to-end workflows for both structured and unstructured data, applying Python to solve real-world data problems.
Session Cycle: Every Spring.

AA 645. Data Mining and Predictive Analytics. 3 Credit Hours.

This course provides a broad understanding of the role of predictive analytics for decision-making in different application domains. Students will be exposed to a number of predictive analytics techniques originated in related fields of statistics, machine learning, and artificial intelligence. Techniques covered will include statistical techniques such as linear and logistic regression, classification techniques such as decision trees and random forests and boosted trees, association analysis techniques such as market basket analysis, and cluster analysis techniques such as K-means and hierarchical clustering. Applications of each of the techniques for decision-making applications will be emphasized. Various tools including Python, Excel, and Tableau will be used throughout the course. Other tools may be introduced as needed.
Session Cycle: Summer
Yearly Cycle: Annual.

AA 651. Analytics Capstone. 3 Credit Hours.

The Analytics Capstone course provides students with the opportunity to apply the knowledge and skills that they have acquired to realistic problems that involve large data sets. The course will revolve around a project based on a data set from a business partner of Bryant University that will provide real data and define a typical decision set that can be solved using the data. Students will present the results of their analysis and recommendations to other students in the class and if appropriate to the client. Students are expected to create a professional presentation of their work and to deliver it confidently. The project will consist of multiple predictive models to assist the client that will be developed using Python. Multiple predictive modeling techniques learned in prior classes will be used.
Prerequisites: AA 630, AA 640, AA645.

AA 691. Directed Independent Study in Applied Analytics. 3 Credit Hours.

This course is designed to allow an individual academic program to be tailored to fit the unique interest of a graduate student. At the initiation of the graduate student, the faculty member and the student will develop an academic plan that is submitted to the College of Business for final approval.