CURRICULUM
Our Master of Science in Business Analytics and AI degree is expertly designed to emphasize practical, real-world
Our curriculum offers flexible pathways for both fall and spring entry. Fall entrants follow a five-semester plan, while spring entrants take advantage of an accelerated four-semester option, completing three courses in their first spring and summer terms. This structure enables both groups to merge seamlessly for the remainder of the program to create a unified learning experience.
Upon graduation, your MSBAi course curriculum will allow you to become a proficient programmer in R, Python, and SQL, while experiencing an array of cutting-edge big data management and cloud analysis tools like Amazon SageMaker, Hadoop, Hive and Pig. These tools will allow you to confidently collect, manage, and visualize data, build models, and use this information to make better business decisions. Our MSBAi curriculum concludes with a capstone practicum course, where you will implement analytics solutions for a rotating set of companies, using real problems and real data.
FOUNDATION
Introduction to Business Analytics
Provides an overview of the business analytics process and important analytic techniques, data visualization, data mining, optimization, and simulation. Exposes students to a variety of business problems in analytics (marketing, finance, operations). Throughout the course, students will learn to model and analyze complex business decisions with various tools on spreadsheets to improve decision making across business functions.
Programming with AI
Learn R and Python as programming languages assisted by AI, with emphasis on applications in business. Students will learn fundamentals of both languages and will be exposed to cutting edge packages and libraries to execute analytic tasks.
Data Models
Covers the concepts and techniques used to analyze and report structured data. Students will learn tools and methods for understanding the data models supporting various business processes and for analyzing data from structured databases.
Decision Analytics and AI
This course involves a practical application of predictive data mining, optimization, and simulation to a variety of business problems in analytics (marketing, finance, operations). The course builds on the foundation skillset (data management, descriptive data mining) developed in 8100. Students learn to model & analyze complex business decisions with various tools to improve decision making across business functions.
Data Mining
The objective of this course is teaching students how to use various data mining techniques. Topics include logistic regression, decision tree networks, and neural networks. Student will mine datasets from various business areas and use their findings to support decision-making.
Business Intelligence
This course examines the concepts and approaches in Business Intelligence (BI) from a business user/analyst perspective. Students will learn to use BI tools for creating applications and dashboards in the context of fact-based decision-making.
PLEASE NOTE: All foundation courses are required.
CORE
LLMs and Text Analytics
This course teaches text analytics and how to transform unstructured text into useful information. Students learn the advantages and limitations of traditional text analytics, natural language processing, and AI large language models (LLM).
Machine Learning & Artificial Intelligence
This course covers the use of machine learning algorithms in business decision making and the potential drawbacks and ethical challenges. A particular focus will be on preprocessing, coding and evaluation methodologies for deep learning.
Advanced Topics
This course focuses on advanced applications in business. Case discussion will be used to expose students to diverse applications of analytics in organizations. Assignments include case discussion, problem solving, projects to further students' knowledge and skills.
PLEASE NOTE: All Students Will Have The Option To Explore Classes Throughout Our Diverse Curriculum.
CAPSTONE
Analytics Practicum
Capstone course for the MSBAi program. This course focuses on the application-based practicum project completed during the capstone term. Students will combine the concepts and skill set learned throughout the program to navigate the analytics process and partner with an organization on a real business analytics project. The course will blend lectures and assignments to help students obtain communication skills and project management skills needed to support their project and interactions with the client.
PLEASE NOTE: Curriculum is subject to change.
FACULTY SPOTLIGHT
Our curriculum is built upon the expertise of industry-leading professionals like David Anderson, ensuring a dynamic and practical learning experience for our students. David brings a wealth of experience, imparts invaluable real-world insights, bridging the gap between theory and application. As a seasoned expert, he brings a unique perspective to the classroom, offering students the opportunity to learn from actual industry practices and challenges. Recently, David was featured in the on his work on addressing the paygap.
鈥淎ll my experiences in the real-world working as a data scientist comes directly into the classroom where I teach analytics. So, the process of taking data and using it to make better decisions is directly brought into the classroom.鈥� 鈥�David Anderson, Ph.D.,The Michele and Christopher Iannaccone 鈥�91 Associate Professor, Management & Operations - MSBA Faculty Director