Residential MSBA Academics

This prerequisite can be met by the following:

• W&M BUAD 231, BUAD 512A, MATH 106, MATH 351, ECON 307, PSYC 301, SOCL 353, KINE 394
  • BUAD 231 is offered as a residential summer course
  • BUAD 512A is offered as a online MSBA prerequisite course
• Virginia Community College System - MTH 240
• If you plan to take or have taken an alternative college level course in Statistics within the last 5 years, please submit the class syllabus for curriculum approval before enrolling. If a student has taken AP Statistics and/or Probability, an additional higher-level college-level course must be completed.

You must either:

• Hold an undergraduate degree in business or have completed a business major or minor
• Complete the approved courses through Coursera or

• Introduction to Corporate Finance
• Introduction to Financial Accounting
• Introduction to Marketing
• Introduction to Operations Management

All admitted students are automatically registered for the Business Analytics Toolkit Bootcamp, a comprehensive two-week program that consolidates prerequisite material and is held between our MSBA Orientation. The combined cost for orientation and bootcamp is $1,350, and both are required components of the MSBA program taught by William & Mary faculty.

The Business Analytics Toolkit Bootcamp is a required, in-person experience that begins in August and provides an intensive introduction to the programming and mathematical foundations needed for success in the MSBA program. Students build skills in R, Python, and core quantitative methods through hands-on practice with data, coding exercises, and applied problem solving. By the end of the bootcamp, students are prepared to being MSBA coursework at the intermediate level with confidence.

Students who complete the bootcamp in full will earn participation points that reduce the weight of the competency exam. Students who miss any portion of the bootcamp will instead be required to demonstrate proficiency by achieving at least an 80% on the exam. The competency exam will directly follow bootcamp.

Before the required MSBA Summer Boot Camp, you may choose to complete optional, self-paced DataCamp courses for additional programming practice. These courses are not required but can help you feel more confident going into boot camp and starting your MSBA coursework. Please keep in mind you will need some datacamp modules for other courses. For instance, you need to show completion of the Python Fundamental course for CTAI, your first course in the program.

• R-Programming Fundamentals (Skill Track)
• Data Visualization with R (Skill Track)
• Statistics Fundamentals with R (Skill Track)
• Data Manipulation with R (Skill Track)
• Python Fundamentals (Skill Track)
• Data Manipulation with Pandas in Python (Course)
• Introduction to SQL (Skill Track)

BUAD 5012 | 3 credits

This course explores how organizations can compete and innovate through business analytics and artificial intelligence. Students will learn to apply data-driven models, generative AI, ethical frameworks, and modern data acquisition techniques to real-world business problems. Emphasis is placed on augmented analytics, human-centered data storytelling, and developing both technical and organizational readiness for AI adoption. Practical skills in software tools are integrated throughout the course.

BUAD 5032 | 3 credits

Decision Modeling explores stochastic process modeling through probability theory (distributions, sampling, process interactions), statistical analysis (descriptive, inferential, predictive, multivariate time series models), and modeling techniques (Monte Carlo simulation, decision analytics), with practical exposure to statistical modeling packages.

BUAD 5272 | 3 credits

This course introduces the fundamental concepts, methods, and tools for designing, developing, and leveraging databases in data-driven organizations. Students will explore how organizations manage and use two primary types of databases: operational (supporting day-to-day transactions) and analytical (supporting decision-making and strategy). Throughout the course, students will also learn how to integrate and complement their work with AI-driven tools to enhance database design, coding efficiency, and analytical insights.

BUAD 5072 | 3 credits

This course provides a rigorous and hands-on introduction to regression and classification, two of the most widely used techniques in business analytics. These methods are essential for turning data into insight. They help organizations forecast sales, predict customer churn, assess credit risk, optimize pricing, detect fraud, and improve operational efficiency. Students will learn to build, interpret, and evaluate predictive models that support better decision making across a range of business applications.

BUAD 5022 | 3 credits

Optimization is an analytics methodology found in all business analytics programs at the master's level. This course will provide knowledge in optimization and analytics that are the foundations of analytics methodology including the theory and application of optimization techniques such as linear programming, integer programming, and mixed-integer programming.

BUAD 5722 | 3 credits

This course teaches students to collect, process, and analyze large-scale data using distributed computing technologies and cloud platforms. Students gain hands-on experience with industry-standard big data frameworks, covering the complete data pipeline from ingestion through visualization. Core topics include distributed computing architectures, batch and stream processing, cloud-based storage and retrieval, programmatic data collection via APIs, and deploying open-source Large Language Models (LLMs) for data analysis.

BUAD 5042 | 1.5 credits

This course introduces students to computational business modeling, which includes algorithmic approaches for solving complex business problems. By focusing on methods such as hill climbing, simulated annealing, and genetic algorithms, students learn how to design, implement, and evaluate heuristics in Python to generate efficient, practical solutions that approximate the best possible outcomes and create competitive advantage.

BUAD 5732 | 1.5 credits

This course introduces principles and techniques for data visualization for business. Effective visuals communicate information to maximize readability, comprehension, and understanding. Information visualization principles are drawn from the fields of statistics, perception, graphic and information design, and data mining. Students will learn visual representation techniques that increase the understanding of complex data and models. Human information processing and encoding of visual and textual information will be discussed in terms of selecting appropriate method for displaying of appropriate data, both quantitative and qualitative. Topics include charts, tables, graphics, effective presentations, and dashboard design. Examples will be used from a variety of industries.

BUAD 5082 | 3 credits

This is the second of two courses designed to equip learners with fundamental knowledge and hands-on skills in Machine Learning. Building on concepts from Foundations of ML of Business, it extends regression models to nonlinear models in supervised learning and introduces essential tools in unsupervised learning. Emphasis is placed on cultivating a thoughtful and strategic approach to leveraging data for improved business decision-making and performance. Through hands-on computer programming, students will explore the algorithms that drive these methods, gaining both a solid technical understanding and the ability to apply them effectively in real-world contexts.

BUAD 5742 | 3 credits

This course covers deep learning models for regression, classification, vision, language and recommendations, and an introduction to reinforcement learning. Students develop intuition for the mathematical underpinnings of modern AI architectures, hands-on experience in training AI models, and technical skills in the evaluation and management of AI model risks.

BUAD 5792 | 3 credits

Companies and organizations partner with student teams to address real-world business challenges. These projects involve analyzing large and complex datasets and require students to select and apply appropriate analytical techniques. Emphasis is placed on translating analysis into actionable insights and persuasively communicating recommendations through effective data visualization and professional presentation skills.