MSBA Courses

Statistics for Business Analytics

This course introduces foundational concepts to help students understand statistical tools used throughout the program and apply them to organizational problems. Emphasis is placed on how statistics can improve decision-making through appropriate use and analytical thinking. Topics include describing and transforming data, understanding variation and uncertainty, measuring dependence, inferential methods, goodness of fit, and regression models.

Machine Learning Foundations

Machine Learning Foundations introduces the core principles of supervised learning with a focus on building reliable, validated models. Students learn regression, logistic regression, feature engineering, and decision trees — anchored by cross-validation as the foundation for evaluating model performance. The course emphasizes applying these methods to real business problems and prepares students for more advanced machine learning techniques in the next course.

Advanced Machine Learning and AI

Advanced Machine Learning and AI builds on the Machine Learning Foundations course to introduce the techniques that serve as the foundation of AI. Students explore ensemble learning, neural networks, and the fundamentals of deep learning, alongside methods for modeling sequential data and creating compact representations of large datasets. The course also covers clustering, pattern mining, and evaluation approaches for complex data. Together, these topics provide the essential skills to understand and apply machine learning methods that power modern AI.

AI Modeling in Practice

This course takes students beyond foundational machine learning to explore how predictive models are built, tuned, and applied in real-world business contexts. The course covers model tuning, recommendation systems, and approaches for ensuring explainability and fairness in machine learning and AI systems. Students also gain exposure to emerging areas such as generative and transformer models, and multimodal learning. The course emphasizes evaluating models in practice, understanding their business impact, and applying them across different domains such as marketing and finance. By the end, students will be equipped to design and assess advanced predictive models that connect cutting-edge AI methods to business decision-making.

Language Analytics

In this course, students learn core techniques for extracting, processing, analyzing, visualizing, and modeling text. Topics include real-time text collection, data preparation, sentiment analysis, topic modeling, word embeddings, transformers, and transfer learning — the foundations of today’s large language models. Students also explore how “custom AI” tools are built on top of foundational language models using open-source tools. By the end of the course, students will be able to analyze text at scale, generate business insights, and apply language analytics across domains such as marketing, customer engagement, and risk management.

Programming I: Introduction to Data 

In this course, students learn how to write practical applications for business problems in a coding language. This course provides programming skills to create, clean, and handle data for managerial decisions. The course covers basic programming topics such as data types, variables and constants, and their declaration, arithmetic operators, repetition control structures, basic file operations, elementary searching, and sorting. Students will be exposed to programming tools that are commonly used in the business analytics field using R.

Programming II: Data Infrastructure 

This course focuses on Python and equips students with intermediate programming skills, including object-oriented programming, common data structures, string manipulation, control statements, conditional logic, and user-defined functions. Students also learn techniques for data processing, analysis, and visualization using well-known Python packages widely applied in business analytics. All concepts are demonstrated through real business applications.

Programming III: Working with Big Data 

This course equips students with the tools and concepts needed to manage and analyze massive datasets. Building on earlier programming and data infrastructure skills, it introduces relational and non-relational databases, SQL for querying and managing data, and connections to databases through Python. Students also explore big data platforms and techniques, including distributed computing, Apache Spark, and cloud computing environments such as AWS. Throughout the course, emphasis is placed on applying these technologies to real business problems, giving students both the technical skills and the practical judgment to work effectively with big data in organizational settings.

Applied Economics and Modeling

Correlation is not causation. Or is it? This course focuses on how managers can apply statistical methods to learn about causal empirical relationships, leveraging pre-existing knowledge about market conditions. The methods include randomized controlled trials, regression discontinuity design, differences-in-differences regression, and instrumental variables. Students learn the theoretical rationale for these methods and apply them in a series of business and economic applications. The ultimate goal is to provide a better understanding of when data supports actionable business recommendations.

Applied Product Management

This course prepares students to make data-driven product and portfolio decisions in competitive markets. The course emphasizes analytical approaches to product design, positioning, and execution, with a focus on how these decisions drive performance and profitability. A central component is the MARKSTRAT simulation, where teams act as product managers, develop strategic marketing plans, and make iterative tactical decisions based on customer insights, competitive dynamics, and financial outcomes. By combining rigorous analysis with hands-on practice, students gain the skills and confidence needed to succeed in product management and related roles. The course also highlights how these techniques connect to emerging areas in technology and AI, ensuring students are prepared for the evolving landscape of product management.

Decision Modeling

This course introduces students to spreadsheet modeling, optimization, and simulation as essential tools for managerial decision-making under constraints and uncertainty. Examples include airlines maximizing revenue by adjusting prices under demand uncertainty, hospitals improving quality of care through staffing decisions, and manufacturers reducing costs by reorganizing supply chains to meet consumer demand. Students learn how to build and interpret models and derive actionable insights, with an emphasis on decision modeling as a bridge between quantitative analysis and managerial decision-making.

Customer Analytics

In this course, students explore how organizations use data to understand and anticipate customer behaviors, preferences, and needs. Students learn to collect and clean data from sources such as purchase histories, digital interactions, and social media, and apply methods like segmentation, customer lifetime value analysis, and churn prediction. The course emphasizes how analytics can guide retention strategies, personalization, and marketing decisions to strengthen customer relationships and drive business growth.

Fintech

This course introduces fintech — the application of technology to financial services — through key verticals such as open banking, payments, borrowing and lending, capital markets, investments, insuretech, and regtech. It also explores the fast-evolving world of digital assets, blockchains, cryptocurrencies, and decentralized finance (DeFi). Students apply data analytic skills to financial applications, strengthen their understanding of finance and technology, and gain insight into how fintech innovations create new opportunities and challenges across the industry.

Visualization and Storytelling with Data

Visualization and Storytelling with Data teaches students how to turn complex datasets into insights that inspire action. The course explores principles of cognition and visualization, showing how to present relationships, patterns, and trends effectively while ensuring the story is accurate and compelling. Students gain hands-on experience to create visualizations and dashboards, learning best practices for design, communication, and storytelling that enhance business decision-making.

Using Data to Lead Change

Using Data to Lead Change explores how leaders can leverage data to drive meaningful organizational transformation. The course emphasizes diagnosing problems, measuring outcomes, and communicating evidence effectively, while also addressing group dynamics, influence, and decision-making under uncertainty. Students learn how to translate data insights into action and guide organizations through change in ways that improve performance and impact.

Psychology of Big Data 

Psychology of Big Data explores how algorithms and human judgment intersect in organizational decision-making. Students critically evaluate evidence, examine biases and predictable errors in judgment, and learn how to communicate insights persuasively to both technical and non-technical audiences. The course emphasizes why metrics matter, how intuition and algorithms compare, and how data can be leveraged to solve consequential organizational and societal problems.

Business Experimentation

Business Experimentation introduces students to the design and analysis of experiments as a powerful tool for data-driven decision-making. The course emphasizes a “test and learn” approach, combining statistical methods with real-world case discussions to explore questions such as what to test, how to design experiments with minimal disruption to operations, and how to interpret results effectively. Students gain hands-on experience with A/B and multivariate testing, regression, and causal inference techniques, building both the technical and strategic skills needed to plan, analyze, and communicate persuasive business insights from experiments.

Data Ethics and Privacy

Data Ethics and Privacy examines how organizations can navigate the opportunities and risks of a data-driven world. The course explores the collection and use of sensitive data, challenges posed by AI and advanced analytics, and the tension between innovation, regulation, and individual rights. Students consider technical, legal, and governance frameworks — ranging from encryption and data security to ethical standards and public policy — while developing the judgment needed to make responsible, value-based decisions about data in business and society.

The Capstone Project is the peak intellectual learning experience for students in the Georgetown MSBA program. Working in teams and acting as consultants, students apply the concepts, methods, and tools learned in the program to a challenging business analytics problem with a local, national, or global organization as the project sponsor. Teams will put into practice the process of defining the organizational challenge, identifying and/or collecting necessary data, analyzing possible courses of action, and making recommendations, culminating in a presentation to faculty and project sponsors.

Read more about the Capstone Project experience.

The program includes two seven-day residencies, each occurring over a week (Sunday through Saturday). The residencies are designed to enhance learning on complex topics while giving you ample opportunities to engage with faculty, peers, alumni, and program team. You will take two courses during each residency, with the first residency held at the beginning of the program and the second midway through. Each residency week also includes career sessions, industry panels and speakers, networking events, and other community activities.