Curriculum
The MS Business Analytics and AI curriculum is designed to let students complete their 36 credit hours and receive their STEM-eligible degree in just 10 months.
Video: Keeping Curriculum Current
Capstone in Business Analytics and AI
All MS Business Analytics and AI students participate in an industry-sponsored practicum course where they 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.
Classroom of students

Summer Term

6 credit hours

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

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

Explore various concepts of data management and develop expertise in managing data from the design and modeling of a database to data querying and processing. Learn big data storing principles that can be applied to various database products, such as Hadoop, Map Reduce, and Spark.

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

    Supply Chain Analytics

    Supply Chain Management (SCM) is the management of activities governing the flow and transformation of resources from initial suppliers to ultimate consumers to make goods and services available at the right time, place, price, and condition in the most profitable and cost-effective manner. In this course, we will consider analytics applied to important problems found in the management of supply chains. The first half of the course introduces the context for the application of analytics in operations. The second half of the course addresses the use of analytics in supply chain management.

    3 Credits | Elective

    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

    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

    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

    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

    Demand Analytics/Pricing

    Strategic problems, policies, models, and concepts for the design and control of new or existing operations systems.

    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

    Survey of important time series models and methods. The two primary tasks of time series analytics: forecasting and explanation. Confirmatory models such as regression, random walks, autoregression, ARIMA, and state space. Exploratory methods such as neural nets, trees, random forests, and other ensemble methods.

    2 Credits | Elective

    Financial Analysis Elective Track
    The MS Business Analytics and AI offers a Financial Analytics Elective Track that has a distinctive focus to combine education in empirical methods in finance and advanced data science.

    Students interested in pursuing the Financial Analytics elective track must have exposure to finance coursework. Those interested in pursuing the Financial Analytics elective track 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

    Explore various concepts of data management and develop expertise in managing data from the design and modeling of a database to data querying and processing. Learn big data storing principles that can be applied to various database products, such as Hadoop, Map Reduce, and Spark.

    3 Credits | Core

    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 MSBA 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

    Supply Chain Management & Marketing Elective Track
    The MS Business Analytics and AI program offers a Supply Chain & Marketing Elective Track that has a distinctive focus to combine education in Supply Chain and Marketing analytics.

    Summer Term

    6 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

    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

    Financial Management

    This course provides an introduction to the fundamental concepts of both managerial accounting and business finance. 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

    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

    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

      Explore various concepts of data management and develop expertise in managing data from the design and modeling of a database to data querying and processing. Learn big data storing principles that can be applied to various database products, such as Hadoop, Map Reduce, and Spark.

      3 Credits | Core

      Supply Chain Analytics

      Supply Chain Management (SCM) is the management of activities governing the flow and transformation of resources from initial suppliers to ultimate consumers to make goods and services available at the right time, place, price, and condition in the most profitable and cost-effective manner. In this course, we will consider analytics applied to important problems found in the management of supply chains. The first half of the course introduces the context for the application of analytics in operations. The second half of the course addresses the use of analytics in supply chain management.

      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

      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

      Business Intelligence Capstone

      This industry-sponsored practicum course has students solve real-world analytics problems by applying skills acquired throughout the MSBA 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

      Demand Analytics/Pricing

      Strategic problems, policies, models, and concepts for the design and control of new or existing operations systems.

      2 Credits | Elective

      Time Series Analysis

      Survey of important time series models and methods. The two primary tasks of time series analytics: forecasting and explanation. Confirmatory models such as regression, random walks, autoregression, ARIMA, and state space. Exploratory methods such as neural nets, trees, random forests, and other ensemble methods.

      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

      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.