Residential MSBA Academics

Advance your journey at William & Mary

Our Approach

William & Mary's MS in Business Analytics program puts you at the cutting edge of business analytics in an environment that replicates the real business world. You'll gain keen insight from a diverse number of perspectives through critical thinking projects with your 5-person learning team. You will emerge with the analytic expertise to solve complex problems from multiple perspectives, making you the strategic asset companies want to hire.

We take a "book end" approach where business context is taught first, followed by intensive analytic methodology coursework - one third of which is in Machine Learning and Artificial Intelligence - and ending with a final business application capstone project where you will serve as a consultant for a real company.


STEM: Science, Technology, Engineering, Mathematics

STEM-Designated Program

William & Mary’s MSBA Program allows you to merge business acumen and leadership with highly technical skills to solve complex problems in today's workplace.

Candidates enrolling in the STEM-designated residential MSBA Program may be eligible for an extension of their post-study Optional Practical Training (OPT) visa up to 3 years. For additional information or to set-up a pre-interview appointment, please contact [[m|msba]] or 757-221-2953.

Nasiba Radjabova

Nasiba Radjabova '22

"William & Mary's program is only 10 months long. I like the idea that I could transform my career potential and enter the real business world in a short amount of time. Secondly, It offers the OPT-STEM designation which is good for international students."

Four Key Competencies

  • Business Acumen

  • Applied Mathematics

  • Computing Technologies

  • Communicating with Impact

Gio DeFrank

Gio DeFrank '22

"This program is very technical in nature and I was interested in pursuing a more technical path. William & Mary's goal is to create hands-on analysts or scientists who work directly with the data to understand its implications in a business context."

Core Curriculum

Core Curriculum

Our core curriculum ensures you will learn the technical skills of a data scientist and gain the strategic perspective of a business analyst.

  • Learn how to gather and structure the large volumes of data companies collect about their businesses
  • Master the requisite skills needed to wield Big Data
  • Identify the key data fields that effectively predict consumer behaviors
  • Effectively communicate the results of technical analyses using non-technical, managerial terms
  • Provide insight and relevance to affect strategic decision-making
Program Snapshot
program snapshot
Pre-requisites (Flexible and Affordable Pre-requisite Completion Available)
Probability & Statistics
Linear Algebra
R and Python Programming
Business Foundations
(See Admissions for flexible and affordable online and in-person option for completing prerequisites)
Fall Semester
Competing Through Business Analytics (3 credits | 3 weeks) Database Management (3 credits | 12 weeks)
Stochastic Modeling (3 credits | 12 weeks)
Machine Learning I (3 credits | 12 weeks)
Optimization (3 credits | 12 weeks)
Spring Semester
Big Data (3 credits | 12 weeks) Capstone Project (3 credits | 3 weeks)
Data Visualization (1.5 credits | 6 weeks) Heuristic Algorithms (1.5 credits | 6 weeks)
Machine Learning II (3 credits | 12 weeks)
Artificial Intelligence - Neural Networks, Genetic Algorithms (3 credits | 12 weeks)
Course Descriptions
course descriptions
Competing Through Business Analytics

BUAD 5012 | 3 credits

This intensive course will include a survey of the state-of-the-art in business analytics: A review of companies that have used business analytics for competitive advantage and how they have done it. These topics will be initiated with a panel discussion on the first day of class. This course will teach business acumen and how the field of analytics fits within the context of business. Topics will include subjects such as: understanding balance sheets and income statements, budgets, business metrics as used for performance measurement and incentives, communicating with impact, visualization, the functions of a company; how they interact, and what data they have, and project management techniques. The course will also include: Survey of opportunities for problem solving using business analytics in operations, supply chain, human resources, finance, and marketing, and also an introduction to the tools that are covered in this program.

Stochastic Modeling

BUAD 5032 | 3 credits

Stochastic Modeling is a foundation course in the study of business analytics. It provides an understanding of the principles associated with modeling of stochastic processes. The topics will include: probability theory (important probability distributions, sampling from distributions, interaction of multiple stochastic processes); statistical analysis (descriptive/inferential/predictive statistics, multivariate statistics, time series models); and modeling (modeling concepts, Monte Carlo simulation, decision analytics). Students will also be introduced to a variety of statistical modeling packages.

Database Management

BUAD 5272 | 3 credits

Internet-scale applications and modern business processes generate voluminous data pertaining to business vital signs, market phenomena, social networks that connect millions of users, and the habits of users and customers. Data produced in these settings hold the promise to significantly advance knowledge and provide business opportunity. This course covers fundamentals of database architecture, database management systems, database systems, principles and methodologies of database design, and techniques for database application development. The course also examines issues related to data organization, representation, access, storage, and processing. This includes topics such as metadata, data storage systems, self-descriptive data representations, semi-structured data models, semantic web, and large-scale data analysis.

Machine Learning I

BUAD 5072 | 3 credits

This course is designed to provide students with a deep understanding of the theory and practice of regression and classification, two of the most commonly used techniques in the data scientist’s toolkit. These predictive analytics techniques are important members of a family of analytics often referred to as machine learning techniques, and they are the basis for more elaborate machine learning techniques that will be covered in a sequential course called Machine Learning 2. An important part of this course will cover a powerful and ubiquitous software package called R, which is used extensively in labs and assignments in this class and subsequently reappears in other classes throughout the program.


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, mixed-integer programming, and stochastic programming.

Big Data

BUAD 5722 | 3 credits

The data storage and retrieval techniques that have served the information processing industry for decades have proven inadequate in the face of the huge collections of data presently being created by the web and the so-called “Internet of Things.” Businesses are requiring a new set of technologies that are specifically designed to deal with these huge data sets. In this course, MapReduce techniques will be taught which will include parallel processing and Hadoop, an open source framework that implements MapReduce on large-scale data sets. Other Big Data tools will be taught that provide SQL-like access to unstructured data: Pig and Hive. Finally, we will teach so-called NoSQL storage solutions such as HBase.

Heuristic Algorithms

BUAD 5042 | 1.5 credits

Most business problems are too large or too complex to solve optimally, where the strict meaning of “optimal” means finding the “probably” best solution to a problem. Satisficing, or finding a heuristic solution that approximates the optimal solution is, therefore the predominant mode of problem solving found in industry. Having the capability of designing and executing heuristics that more closely approach optimal solutions creates a competitive advantage for companies. This course focuses on such methodologies where quick but good solutions to complex problems are needed so that they can be acted upon in a timely manner. The type of heuristic covered in this course is the algorithm, which is a sequence of steps taken to provide a solution to a problem.

Data Visualization

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 the appropriate method for displaying of appropriate data, both quantitative and qualitative. Topics include charts, tables, graphics, effective presentations, and dashboard design. Cases will be used from a variety of industries.

Machine Learning II

BUAD 5082 | 3 credits

This is the second of two courses designed to equip students with the kinds of analytical skills used in the era of Big Data to reveal the hidden patterns in, and relationships among, data elements being created by internal transaction systems, social media and the Internet of Things. This second machine learning course covers many methodologies including various non-linear approaches, tree-based methods, support vector machine, principal components analysis, and the analysis of unstructured data via unsupervised machine learning techniques. The R language is used extensively in this course.

Artificial Intelligence

BUAD 5742 | 3 credits

This course provides competence in an essential set of tools that are not covered in other courses. Artificial Intelligence (AI) methods perform well in cases of large, complex problems, which is the focus of cutting-edge business analytics endeavors. This course covers AI methods such as genetic algorithms, neural networks, and fuzzy logic. AI comprises a set of essential analysis techniques for the modern data scientist who solves problems that encompass vast data sets and involve complex relationships.

Business Analytics Capstone Project

BUAD 5792 | 3 credits

This course is taught in the last two and a half weeks of the Business Analytics Program and requires students to complete a comprehensive business analytics project, from start to finish. The projects require that students apply the knowledge gained in the preceding courses. Students will identify the most appropriate techniques for their projects and then apply one methodology effectively. Projects are characterized as requiring the analysis of vast data and solving complex problems. Several projects hosted by businesses would be offered, with the goal of representing multiple functions and industries to suit students’ interests. They will define and frame a complex problem, develop a systematic approach to solving it using analytics, generate an innovative solution and persuasively convey that solution using data visualization techniques and communication skills. A unique faculty supervisor will be assigned to each business analytics capstone team (average 4-5 students per team).

You will learn state of the art models for clustering, regression, classification, and deep learning for vision and text mining. Plus, you will be well rounded with models for time series forecasting, resource optimization, logistics, and reporting at scale. Our program uses tools in every course including, but not limited to: Python, R, Gurobi, Alteryx, Tableau, MySQL, Keras, Spark, TensorFlow.

There will be comprehensive coverage in descriptive, predictive, and prescriptive analytics. Communication is reinforced strategically throughout the program, emphasizing storytelling and communication of results to decision makers. We are applying the heft of data science to business problems, using data to drive business forward and to create insight.