Advance your journey at William & Mary
Explore our core curriculum and capstone experience
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
|Pre-requisites (Flexible and Affordable Prerequisite Completion Available)|
|Probability & Statistics
R and Python Programming
(See Admissions for flexible and affordable online and in-person option for completing prerequisites)
|Database Management (3 Credits | 12 Weeks)||Big Data (3 Credits | 12 Weeks)||
|Stochastic Modeling (3 Credits | 12 Weeks)||Data Visualization
(1.5 Credits | 6 Weeks)
(1.5 Credits | 6 Weeks)
|Machine Learning 1 (3 Credits | 12 Weeks)||Machine Learning 2 (3 Credits | 12 Weeks)|
|Optimization (3 Credits | 12 Weeks)|| Artificial Intelligence - Neural Networks, Genetic Algorithms
(3 Credits | 12 Weeks)
Comprehensive SkillsYou 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:
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
Unique Academic Experiences
The MSBA program offers several business analytics boot camps in the summer for deposited MSBA students. These boot camps include linear algebra, R and Python programming.
We have designed these boot camps as a cost-effective way for deposited students to meet any or all of these prerequisites prior to the start of MSBA orientation. It is required that students who enroll have computers with a Windows Operating System environment. Please see our laptop requirements to ensure that your computer will work properly during the boot camp and your classes.
Each boot camp will be taught by W&M faculty and will provide a competency test and certificate of completion. Each weekly session costs $250. Students must be admitted and deposited into the program in order to participate in these boot camps. Registration information will be sent to deposited students in the spring.
- Linear Algebra - Linear algebra (LA) is a branch of mathematics that studies systems of linear equations and the properties of matrices. The concepts of LA are critical for learning the advanced skills required in the business analytics program. The following topics will be covered: definitions of vectors and matrices, vector/matrix operations, matrix spaces, and matrix-vector properties/definitions, Gaussian elimination, diagonalization, vector projections, linear transformations, Eigenvectors/Eigenvalues, matrix decomposition and permutation matrices.
- R Programming - R is a free, open-source programming language and software environment for statistical computing and graphics. R is an integrated suite of software facilities for data manipulation, calculation, and graphical display. It also includes conditionals, loops and user-defined recursive functions. This course will teach the basics of R that are critical for learning the advanced skills required for business analytics.
- Python Programming - Python is a widely used high-level, open-source, general-purpose, interpreted, dynamic programming language whose design philosophy emphasizes code readability. It supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. This course will teach the basics of Python that are critical for learning the advanced skills required for business analytics.
Please contact MSBA@mason.wm.edu if you have any questions about our boot camps.
The MSBA Capstone is an intensive assignment that tests your ability to be an effective analyst. Part of being an effective analyst is to think critically about a situation in the context of how much time and what resources are available. Unlike most programs, our approach ensures you have all the required tools in your toolbox before commencing the project. You'll apply the skills learned throughout the MSBA program to a real-world scenario in a controlled environment, allowing you to honestly assess the knowledge you've gained to successfully solve a complex business problem in Business Analytics
Advanced Communication Skills
Advanced Communication SkillsTeams generate an innovative solution and persuasively convey it using data visualization techniques and communication skills
Combined MBA/MSBA Degree
"William & Mary's Raymond A. Mason School of Business is excited to announce that our MBA/MSBA combined degree is now a two-year STEM-designated program.
This course of study allows you to merge the business acumen and leadership from the MBA curriculum with the highly technical skills from the MSBA to solve complex problems in today's workplace.
Candidates enrolling in the new STEM-designated combined degree may be eligible for an extension of their post-study Optional Practical Training (OPT) visa up to 3 years.
Candidates must apply to the MBA and MSBA programs separately to pursue the combined degree.
Current students may apply for the combined degree after completing the first semester in the MBA or MSBA program.
For additional information or to set up a pre-interview appointment, please contact email@example.com or 757-221-2944.
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Own Your Journey
Whichever program you choose, the fact remains that you will receive a quality education from one of the nation’s leading public universities, expand your perspective through our diverse student population, and join our Tribe of alumni who are making an impact in businesses around the globe.