Summer Term
6 credit hours, two core courses
Introduction to Machine Learning and neural networks
This hands-on course introduces core machine learning concepts, algorithms, and applications, covering supervised and unsupervised learning, model evaluation, and overfitting. Real-world business examples are used to build a foundation for more advanced MS Business Analytics and AI coursework.
3 Credits | Core
Data Science and agentic Programming
This course teaches the tools and programming skills needed to extract insights from business data, using Python and Pandas. Topics progress from introductory Python through data wrangling, visualization, classification, and clustering.
3 Credits | Core
Fall Term
Average of 15 credit hours taken in fall; 13 credit hours of core courses
Optimization for decision making
This is the first course in a two-course optimization sequence, covering quantitative techniques for decision-making in business contexts including finance, marketing, statistics, and revenue management. Topics include linear programming, integer programming, nonlinear programming, and neural networks, implemented in Python with Gurobi.
2 Credits | Core
Information Management
In this course, you will develop core skills through hands-on experience with database technologies, data transformation, and query tools, while building a strong conceptual foundation in information management. You will also gain exposure to how data supports modern AI systems, enabling you to prepare, manage, and utilize data effectively for both traditional analytics and emerging AI-driven applications.
3 Credits | Core
Analytics for Unstructured Data
This course explores how enterprises harness unstructured data analytics (text, images, audio, video) combined with Generative AI to unlock business value. Techniques range from sentiment analysis and recommender systems to RAG pipelines and Agentic AI, with hands-on Python experience throughout.
2 Credits | Core
Financial Management
This course provides an introduction to the fundamental concepts of both managerial accounting and business finance for MS Business Analytics and AI students. The accounting half covers cost behavior, cost allocation, variance analysis, and the Balanced Scorecard; the finance half covers time value of money, NPV, company valuation, and stock analysis.
3 Credits | Core
Advanced DEEP Learning and Machine Learning
This course builds upon introductory machine learning to study advanced predictive analytics techniques with particular emphasis on deep understanding of models and scalability to large datasets, using Python (Scikit-Learn and PyTorch). The central goal is to convey the pros and cons of different predictive modeling techniques for real-world business problems.
3 Credits | Core
Marketing Analytics I
This course introduces students to the data, models, and analytical techniques that businesses use to make marketing decisions, covering the full process of transforming data into actionable insights using Excel and R. Students work through five thematic units — understanding markets, market demand, data communication, customers, and opportunities.
2 Credits | Elective
Supply Chain Analytics
3 Credits | Elective
privacy preserving Analytics
2 Credits | Elective
digital product and process management
2 Credits | Elective
Analytics for Supply Chain Distribution and Planning
2 Credits | Elective
Spring Term
Average of 15 credit hours taken in spring; 7 credit hours of core courses
Business Intelligence Capstone
This industry-sponsored practicum course has students solve real-world analytics problems by applying skills acquired throughout the MS Business Analytics and AI program on behalf of a business sponsor. Students develop skills across data modeling, functional business knowledge, technology, and analytical storytelling through iterative team-based project work.
3 Credits | Core
Unsupervised Learning
This course covers the major families of unsupervised machine learning methods — including clustering, dimensionality reduction, density estimation, anomaly detection, and association rule mining — implemented in R with a focus on statistical reasoning and model validation. Students learn to apply these methods to real-world data problems without labeled outcomes.
2 credits | Core
Dynamic Optimization and Reinforcement Learning
This is the second course in a two-course optimization sequence, covering advanced quantitative techniques including neural networks (transformers), simulation, bandit problems, dynamic programming, and reinforcement learning. Applications span finance, marketing, statistics, and revenue management, with Python used throughout.
2 Credits | Core
Demand Analytics/Pricing
This course will introduce data and tools used to analyze the business environment. The focus will be using marketing models and analytical techniques to enhance business decision making. Using real world data and problems, strategic market opportunities will be evaluated and the impact of marketing decisions in the marketplace will be assessed.
2 Credits | Elective
Advanced Data Analytics in Marketing
This course develops rigorous data-driven marketing analytics skills, covering statistical models for managerial decision-making including hierarchical models, Bayesian analysis, discrete choice models, causal inference, and nonparametric methods. Students apply these methods to marketing problems such as customer acquisition, segmentation, attrition, pricing, and A/B testing.
2 Credits | Elective
Financial Technology
This course offers an in-depth examination of technological advancements reshaping the financial services industry, including blockchain, distributed ledgers, cryptocurrencies, machine learning in finance, APIs, open banking, and digital payments. Students critically evaluate how financial institutions leverage emerging technologies to create value and competitive advantage.
2 Credits | Elective
Social Media Analytics
This course covers the strategic, analytical, and technical aspects of leveraging social media data for business value, focusing on network analysis, influence measurement, community detection, and predictive modeling. Students develop expertise in analyzing social media chatter, building network-based models, and linking social media activity to business performance outcomes.
2 Credit | Elective
Time Series Analysis
2 Credits | Elective
applications of data storytelling
2 Credits | Elective
Financial Elective Option
The MS Business Analytics and AI offers a financial analytics elective option that offers a focus on combining education in empirical methods in finance with advanced data science through the selection of specific electives that pair with the general curriculum. Students interested in pursuing the financial analytics elective options must have exposure to finance coursework. Those interested in pursuing the financial analytics elective option that have not had previous exposure to finance coursework are expected to complete a specific online course for admitted students, titled “Principles of Financial Analysis.”
Summer Term
7 credit hours required in summer
Introduction to Machine Learning and neural networks
This hands-on course introduces core machine learning concepts, algorithms, and applications, covering supervised and unsupervised learning, model evaluation, and overfitting. Real-world business examples are used to build a foundation for more advanced MS Business Analytics and AI coursework.
3 Credits | Core
Data Science and agentic Programming
This course teaches the tools and programming skills needed to extract insights from business data, using Python and Pandas. Topics progress from introductory Python through data wrangling, visualization, classification, and clustering.
3 Credits | Core
Intro to Finance Analytics
This fast-paced course provides a rigorous introduction to financial analysis, investment management, and quantitative techniques needed for the Financial Analytics program. Topics span present value, bond and equity valuation, capital budgeting, and simulation-based risk analysis.
2 Credit | Elective
Fall Term
Average of 15 credit hours taken in fall
Advanced DEEP LEARNING AND Machine Learning
This course builds upon introductory machine learning to study advanced predictive analytics techniques with particular emphasis on deep understanding of models and scalability to large datasets, using Python (Scikit-Learn and PyTorch). The central goal is to convey the pros and cons of different predictive modeling techniques for real-world business problems.
3 Credits | Core
Optimization for decision making
This is the first course in a two-course optimization sequence, covering quantitative techniques for decision-making in business contexts including finance, marketing, statistics, and revenue management. Topics include linear programming, integer programming, nonlinear programming, and neural networks, implemented in Python with Gurobi.
2 Credits | Core
Adv. Corp. Fin./Investment THEORY
A comprehensive multi-module course covering Advanced Corporate Finance, Valuation Theory, Applied Valuation, Portfolio Theory and Capital Markets, and Empirical Finance, led by faculty experts. It provides a strong foundation in finance for students in the MS Business Analytics and AI, Financial Analytics Elective Track.
6 Credits | Elective
Analytics for Unstructured Data
This course explores how enterprises harness unstructured data analytics (text, images, audio, video) combined with Generative AI to unlock business value. Techniques range from sentiment analysis and recommender systems to RAG pipelines and Agentic AI, with hands-on Python experience throughout.
2 Credits | Core
Information Management
In this course, you will develop core skills through hands-on experience with database technologies, data transformation, and query tools, while building a strong conceptual foundation in information management. You will also gain exposure to how data supports modern AI systems, enabling you to prepare, manage, and utilize data effectively for both traditional analytics and emerging AI-driven applications.
3 Credits | Core
elements of quantitative finance
3 Credits | Elective
Spring Term
Average of 15 credit hours taken in spring
Unsupervised Learning
This course covers the major families of unsupervised machine learning methods — including clustering, dimensionality reduction, density estimation, anomaly detection, and association rule mining — implemented in R with a focus on statistical reasoning and model validation. Students learn to apply these methods to real-world data problems without labeled outcomes.
2 credits | Core
Dynamic Optimization and Reinforcement learning
This is the second course in a two-course optimization sequence, covering advanced quantitative techniques including neural networks (transformers), simulation, bandit problems, dynamic programming, and reinforcement learning. Applications span finance, marketing, statistics, and revenue management, with Python used throughout.
2 Credits | Core
Financial Modeling/Testing
3 Credits | Elective
Business Intelligence Capstone
This industry-sponsored practicum course has students solve real-world analytics problems by applying skills acquired throughout the program on behalf of a business sponsor. Students develop skills across data modeling, functional business knowledge, technology, and analytical storytelling through iterative team-based project work.
3 Credits | Core
Financial Technology
This course offers an in-depth examination of technological advancements reshaping the financial services industry, including blockchain, distributed ledgers, cryptocurrencies, machine learning in finance, APIs, open banking, and digital payments. Students critically evaluate how financial institutions leverage emerging technologies to create value and competitive advantage.
2 Credits | Elective
Fixed Income Analysis
This course introduces students to fixed income securities markets and the analytical methods used to value and manage them, including bond pricing, interest rate risk, derivatives, and default modeling. Students use Bloomberg extensively and learn to interpret market signals from bond prices and yield curves.
2 Credits | Elective
Analytic Finance/Machine Learning
2 Credits | Elective
principles of empirical Finance
2 Credits | Elective
Industry-led Curriculum
Note: This video references Master of Science in Business Analytics (MSBA). As of August 2026, the program has been renamed The Master of Science in Business Analytics and Artificial Intelligence.