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Discover how you can use the language of data to improve corporate decision-making with the online MS in Business Analytics from W&M.
Dynamic Content Pillars
William & Mary faculty designed the Online MSBA program with a singular goal in mind: to give you an unmatched competitive advantage in the workplace. Our program blends the technical rigor you expect from a graduate analytics program at a top-ranked university with the strategic business insights you need to communicate productively with stakeholders.
As Faculty Director Joe Wilck says, “It is not enough to just know business analytics methodologies. You and your organization need problem solvers who can take data from chaos to clarity.”
Our expert faculty—terminal degree holders who have worked in cybersecurity, information technology, telecommunications, healthcare, marketing, manufacturing, oil and gas, and the Department of Defense—weave the theme of business intelligence and analytics throughout the program. You will learn about the value of big data and the ethics of working with algorithms as well as how to manage teams, communicate solutions and steer data-driven decision-making.
The concept of business intelligence and analytics is introduced prominently in our core course BUAD 5112 Competing Through Business Analytics. In this course, you will learn how to:
This course, and the program as a whole, will challenge you to synthesize your technical and strategic learnings so you can champion the importance of business analytics at any company.
“In Competing Through Business Analytics, we developed essential skills—the ability to visualize data and to use it to persuade clients—that would serve us in class, our eventual jobs and, more pertinently, our job interviews.”
William & Mary's Online MSBA program is designed to maximize your computing and modeling skills while honing your ability to apply data science to strategic business planning. The program curriculum revolves around four dynamic pillars, and mastery of these pillars helps ensure that you graduate with a tangible advantage in a high-demand field, where opportunities are growing across industries and around the globe.
Explore the learning outcomes of these four pillars:
These skills will guide you in asking the right questions, building the right models and using those models to perform the right analyses. The resulting insights, when appropriately interpreted and communicated, will give you the means to transform business outcomes by telling the right story, in the right way.
“It is not enough to just know business analytics methodologies. Organizations need problem solvers who can take data from chaos to clarity. The William & Mary Online MSBA program prepares individuals to do just that.”
See just how many career opportunities you could unlock with the Online MSBA—you might be surprised by the possibilities that await a tested data science professional.
At the intersection of our four pillars—business acumen, math modeling, computing technologies and communicating with impact—are the talents and tools every data science professional may need for a successful and profitable career in business analytics. The Online MSBA curriculum prepares you to master exactly these.
Our 32-credit curriculum can be broken down into the following three types of courses:
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.
This course provides a set of programming skills using the R programming language, which is widely used in business analytics for statistical computations.
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.
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.
An interactive virtual seminar that helps set students up for success in this rigorous program. Led by your Student Success Coordinator, your OMSBA Orientation will cover an array of topics, including:
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.
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 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.
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.
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.
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 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.
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.
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.
*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.
**Students who are required to complete the prerequisite courses can graduate in as few as 18 months.
Take a look at what’s ahead in the Online MSBA program—download our program details guide, your one-stop document for admissions requirements, courses and more.
Today’s corporate leaders are increasingly challenged to find qualified employees who understand data science and have proven experience applying its key business functions. Those who could fill this need must be familiar with unstructured data dumps, machine learning, data visualization and current analytics tools, but they must also be able to interpret the information acquired through these means and effectively communicate its implication(s) for the organization's bottom line.
By weaving our curriculum's four key pillars of business acumen, math modeling, computing technologies and communicating with impact into each of our Online MSBA courses, we blend the essential elements of business expertise with a robust portfolio of data science offerings. The result is a dynamic program with real-world applicability that ensures our students graduate with a thorough understanding of how to effectively apply their findings and communicate their insights in a succinct, coherent manner.
“What brought me back to school was kind of looking at the changing landscape, professionally. I didn’t want to be pigeonholed into computer science or statistics, strictly one of those two, nor did I feel like the general approach of an MBA was right for me either. I like the intersection of the two, which is what led me to business analytics. There are a lot of possibilities.”
Data science and analytics jobs posted in 20151
Median base salary for data scientists2
Percent of data science roles requiring a master's degree3
job openings expected by 2020 for U.S. data professionals4
William & Mary’s Online MSBA program welcomed its first cohort in spring 2018, and we quickly discovered that our student demographics mirrored the personal and professional diversity that is common in the fields of technology, data analytics and programming. While some of our current students have highly technical backgrounds, others come from financial, educational and even military ones. They represent varied sociocultural contexts and reside in geographic locations across the country. It is telling, then, that our program has managed to meet each of these driven individuals where they are, and that they’ve found it able to help them advance their technical and managerial development.
Take a look at the story told by our cohort demographics, and consider what you could learn not only from our Online MSBA curriculum and faculty but also from such a diverse cohort of peers.
NOTE: Statistics are based on cumulative self-reported W&M Online MSBA student profile data collected from the spring 2018 cohort.
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1. Retrieved on August 22, 2018, from burning-glass.com/infographic-data-science-jobs-by-the-numbers/
2. Retrieved on August 22, 2018, from glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm
3. Retrieved on August 22, 2018, from pwc.com/us/en/library/data-science-and-analytics.html
4. Retrieved on August 22, 2018, from forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-soar-28-by-2020/