Complete the form below to determine whether the W&M Online MS in Business Analytics is the right fit for you.
Learn the Language of Data
Develop your analytical fluency online with William & Mary.
Complete the form below to determine whether the W&M Online MS in Business Analytics is the right fit for you.
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:
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:
More details about the New Online Student Orientation can be found here.
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.
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.
How much rain will fall next month? How many cars will Toyota sell next year? Which candidate will win the election? Many future outcomes are similarly uncertain. The topic of probability can be described as finding mathematical representations that describe these types of uncertain circumstances. These mathematical constructs are typically called probability distributions, and this course includes discussion of many such distributions that are applicable in a variety of contexts. Evaluating these probabilities helps us think about what might happen in the future. Statistics can be described as interpreting data we have collected to infer the underlying truths. For example, suppose that a poll of voters favors Candidate A to Candidate B by a 52 percent to 48 percent margin. Given that data, we might then, in the vein of statistics, evaluate the likelihood that the entire population of voters actually does favor Candidate A. This course covers many such techniques of inference.
The R language is widely used in business analytics for statistical analysis. This course covers R programming concepts that are required for base proficiency in its application to business analytics problems.
Python is a general purpose programming language that is used widely in business analytics. This course focuses on the skills required for a base level of competency in Python, including (i) understanding data types, (ii) using and manipulating strings, lists, tuples, and dictionaries, (iii) using base Python functions, (iv) conditional statements, (v) loops, (vi) file input and output, (vii) list comprehension, (viii) defining functions, (ix) using lambda functions, (x) a brief introduction to the numpy package, and (xi) a brief introduction to the pandas package.
Linear Algebra is essential for a deep understanding of business analytics methodologies. In particular, it is often useful to represent data and models as vectors and matrices. This course introduces students to the vector and matrix constructs as well as various mathematical operations with these entities. The course covers vector and matrix multiplication, and matrix attributes including the concept of orthogonality, linear dependence and independence, rank, basis, span, symmetry, and positive definiteness. The course also covers methods applied to matrices such as Gaussian elimination, eigenvector and eigenvalue analysis, and various methods for matric decomposition.
Intermediate 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. Course topics include: probability theory (important probability distributions, sampling from distributions and interaction of multiple stochastic processes), statistical analysis (descriptive/inferential/predictive statistics, multivariate statistics and time series models), and modeling (modeling concepts, Monte Carlo simulation and decision analytics).
This series of courses provides a foundation for successfully executing business analytics projects. Students, first, become familiar with a process framework for business analytics through a case example, which also illustrates typical challenges in business analytics projects. Students also learn techniques for the effective visualization and communication of the results of business analytics projects. These courses also address issues regarding data confidentiality, protection of intellectual property, and ethics that arise in business analytics. Lastly, these courses teach an important non-traditional method for data acquisition, i.e. web scraping, whose use is becoming more commonplace.
This course will provide coverage of the most fundamental issues and topics related to the development and use of relational databases and database systems. Organizations store data in two types of databases: operational and analytical. We will spend half of the class covering operational database topics including database requirements, entity relationship modeling, relational database modeling constraints, update anomalies, normalization, SQL, the database front end, and data quality. In the second half of the class, we will focus on analytical database topics which include coverage of data warehousing concepts, dimensional modeling (star schemas), data warehouse/data mart modeling approaches, the extract-transform-load (ETL) process, online analytical processing (OLAP)/business intelligence (BI) functionalities, and the data warehouse/data mart front end.
This is the first 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. A family of analytical methods, collectively referred to as "Machine Learning" methods, has grown out of the artificial intelligence community and has become commonplace in many of the world's leading analytics competitors. This first course focuses on the basics of machine learning, regression techniques, classification techniques and how to avoid over-fitting predictive models. The R language is used extensively in this course.
This course builds on the Intermediate Probability and Statistics course, and it provides a deeper understanding of stochastic processes. Topics include advanced predictive methods and time series analysis.
Optimization is an analytics methodology designed to yield the best solution to a given problem. Maximal and minimal solutions are achieved by applying algorithms with mathematically proven properties. After a thorough examination of assumptions and a sensitivity analysis of the optimal solution, the most favorable option is recommended. Students are exposed to theory and applications of optimization including linear programming, non-linear programming, discrete optimization, and specialized networks. Included in this course is discussion about the difficulties of accurate representing real-world processes with a mathematical model.
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 are requiring 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.
Most business problems are too large or too complex to be solved optimally, where the strict meaning 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.
After completing this course, students will demonstrate an ability to visualize and interpret spatial data, articulate best practices in data visualization, develop and interpret a wide range of charts and graphs in Tableau, customize visualizations for specific business contexts, and verbally communicate persuasive, data-driven business insights supported by visualizations.
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 machines, principal components analysis, and the analysis of unstructured data via unsupervised machine learning techniques. The R language is used extensively in this course.
The theme of these courses is "Natural models and Artificial Intelligence." The courses consider natural models of intelligence and their artificial equivalents. The course 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.
Students work in teams in these three courses to complete a comprehensive analytics project, from start to finish, which requires applying and synthesizing the knowledge gained in the other courses. Students will be challenged to determine which of the methodologies they have learned in the program are the best to accomplish the goals of the project. These courses end with each team presenting their project results.
Be sure to submit your admissions materials before the July 30 application deadline. Contact an Admissions Advisor at 877-212-7180 with any questions you may have.
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.
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.