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Learn the Language of Data

Develop your analytical fluency online with William & Mary.

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From Data to Insight: Behind Our Curriculum

Designed to maximize a student's skill set in data science, William & Mary's Online Master of Science in Business Analytics program challenges students to immerse themselves in the language of data to develop their analytical fluency in a business context. Our 32-credit curriculum revolves around four dynamic pillars that together ensure our students graduate with the competitive advantage in a field with high growth and opportunity.

Woven into each course throughout the program, the following disciplines represent the pillars of our pedagogical philosophy:

  • Business Acumen
  • Math Modeling
  • Computing Technologies
  • Communicating With Impact

At the intersection of these four pillars are the talents and tools every data scientist needs for a successful and profitable career in business analytics, and our curriculum prepares you for just that. Learn more about why our degree focuses on these pillars and the workplace skills they help you develop here.

Our 32-credit curriculum can be broken down into the following three types of courses:

  • 2 pre-program courses worth 0 credits
  • 4 prerequisite courses**
  • 8 core courses, worth a total of 32 credits

    **Note: BUAD 502A, 502B, 502C and 502D are prerequisites for the remainder of the program. Students may be able to satisfy these prerequisites with courses from other sources, and they should inquire about their eligibility during the admission and onboarding process if they wish to do so.

Pre-Program Courses (0 credits)

W&M OMSBA Orientation (0 credits)

More details about the New Online Student Orientation can be found here.

Excel Boot Camp (0 credits)

Completion of this course ensures that students have sufficient skills in Excel. This non-credit course will be available in Canvas throughout the program for students' reference as needed.

Prerequisite Courses (8 credits)

At the discretion of the institution, students with documented expertise in mathematics, statistics, computer science, business and economics, and other quantitative subjects can waive the four prerequisite courses, completing the program in as few as 68 weeks. Students who are required to take the prerequisites are still able to graduate in as few as 75 weeks. Speak with an Admissions Advisor at 877-212-7180 to learn more.

BUAD 502A Probability and Statistics I (3 credits)

In the area of probability, this course covers the concepts of discrete and continuous probability distributions as well as conditional probability. It also covers basic statistics, which can be thought of as a set of tools for interpreting data. These include descriptive statistics, which permit us to describe basic characteristics of data, including the computation of means, standard deviations and ranges of a data set. This course also covers inferential statistics, which are methods for uncovering deeper insights from the data, such as hypothesis testing. Finally, the course considers data visualization as an integral part of data analysis.

BUAD 502B R Programming (1 credit)

This course provides a set of programming skills using the R programming language, which is widely used in business analytics for statistical computations.

BUAD 502C Python Programming (1 credit)

This course provides a foundation of Python programming skills for business analytics including knowledge of Python data types, facilitating repeated execution through the application of loops, using conditional statements, programming the input and output of data, the use of Python packages, and the construction of functions.

BUAD 502D Linear Algebra for Business Analytics (3 credits)

This course provides a set of linear algebra tools for performing business analytics including vector-matrix multiplication, Gaussian elimination, computing determinants, computing matrix rank, computing matrix column and row spaces, performing eigenanalysis, and performing principal components analysis.

Core MSBA Courses (32 credits)

Competing Through Business Analytics (4 credits)

This course will include a survey of the state-of-the-art in business analytics, examining companies that have used business analytics for competitive advantage and how they have done so. This course will teach business acumen and how the field of analytics fits within the context of business. Topics will include business metrics as used for performance measurement and incentives, communicating with impact, visualization, and the functions of a company—how they interact, what data they have, and their development and deployment of algorithms. The course will also include a survey of opportunities for problem solving using business analytics in operations, supply chain, human resources, finance and marketing, as well as an introduction to the tools that are covered in the remainder of this program.

Probability and Statistics for Business Analytics (4 credits)

Probability and Statistics 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 and the interaction of multiple stochastic processes; regression; statistical analysis—descriptive/inferential/predictive statistics, multivariate statistics and time series models; and modeling—modeling concepts, Monte Carlo simulation and decision analytics. Students will also be introduced to a variety of statistical modeling packages.

Database Management and Visualization (4 credits)

This course covers fundamental topics related to the development and use of databases and database systems and best practices for data visualization. Organizations store data in two types of databases: operational and analytical. Operational database topics include database requirements, entity relationship modeling, relational modeling database constraints, update anomalies, normalization, Structured Query Language (SQL) and data quality. Analytical database topics include data warehousing concepts, dimensional modeling (star schemas), data warehouse/data mart modeling approaches, the extraction/transformation/load (ETL) process, online analytical processing (OLAP)/business intelligence (BI) functionalities and the data warehouse/data mart front end. Once data is cleaned and stored, data visualization is used to most effectively communicate information contained in the data. The course covers data visualization principles 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. Topics include charts, tables, graphics, effective presentations and dashboard design.

Machine Learning I (4 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. The programming language R is used extensively in labs and assignments in this class and subsequently reappears in other classes throughout the program.

Machine Learning II (4 credits)

This course is designed to provide students with a deep understanding of machine learning and big data, including more elaborate techniques that extend the coverage from Machine Learning I. 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 internet and the so-called "Internet of Things." Businesses today require a new set of technologies that are specifically designed to deal with these huge data sets. In this course, the Hadoop environment and Amazon Web Services (AWS) will be used to process large-scale data sets.

Optimization and Heuristics (4 credits)

Optimization is an analytics methodology designed to yield the best solution to a given problem. Students are exposed to theory and applications of optimization including linear programming, non-linear programming, discrete optimization and specialized networks. This course includes discussion about the difficulties of accurately representing real-world processes with a mathematical model. Most business problems are too large or too complex to be solved optimally, where the strict definition of "optimal" means finding the provably best solution. Finding a solution that approximates the optimal solution is, therefore, the predominant mode of problem solving found in industry: these are called heuristic solutions. Many companies gain a competitive advantage by constructing heuristics that either find better solutions than do their competitors or find solutions more quickly. This course focuses on achieving such results by programming custom algorithms, which are a sequence of steps taken to provide a solution to a problem.

Artificial Intelligence (4 credits)

The theme of this course is "natural models and artificial intelligence." The course considers natural models of intelligence and their artificial equivalents. It shows how viewing natural intelligence is an effective mindset and it describes the key analytics tools required for designing and executing some business processes competently. A majority of the course is devoted to the topic of neural networks, although other methods are included, such as genetic algorithms, simulated annealing and swarm intelligence.

Business Analytics Capstone Project (4 credits)

This experiential practicum course includes a comprehensive business analytics project that the student will complete from start to finish, integrating the skills that have been acquired from their previous coursework in the business analytics program. They will define and frame a complex problem, develop a systematic approach to solving it using analytics, identify methodologies that are suited to the problem, quickly prototype solutions with those methodologies to identify the best approach, and, ultimately, generate an innovative solution and persuasively convey that solution using data visualization techniques and communication skills.

Employees who both understand a company's business goals and understand the data to help them reach those goals will be highly desirable to recruit and hire in the technology-driven workforce.
— Brian Carlidge, Executive Director or Pre-Business and Pre-Graduate Programs for Kaplan Test Prep1
Apply By December 4

Be sure to submit your admissions materials before the December 4 application deadline. Contact an Admissions Advisor at 877-212-7180 with any questions you may have.

Discover the Raw Potential of Data

In recent years, open data science positions have increased by 136 percent, leaving an estimated 1.5 million unfilled roles by 2018.2 Put yourself in a position of great demand.

Redefine the Bottom Line

Do more for your organization with an understanding of how to read and interpret data for your organization’s bottom line. Get started by downloading our program’s brochure.

Important Dates

Application Deadline

December 4
Spring 2019 Term

Next Start

January 7
Spring 2019 Term

Priority Application Deadline

February 22
Summer 2019 Term


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  1. Retrieved on December 19, 2017, from
  2. Retrieved on October 10, 2017, from