Program Structure

An overview of the program structure of the Master's in Business Analytics (M-BAN).

Advance Your Analytics Skillset

Solve challenging business problems leveraging the power of popular analytics tools, such as:

GAMS, KNIME, Microsoft Excel and Power BI Desktop, Minitab, Python, R, SAS, SQL, Tableau, and Weka.

This 30-credit master program provides students with the opportunity to master Business Analytics concepts, approaches, tools, critical thinking, and decision-making skills for application as a professional in a range of industries, consulting, and government careers.

Our M-BAN courses are taught by distinguished professors with world-renowned research expertise and practical knowledge and experience. These award-winning teachers, researchers, consultants, and authors specialize in bringing complex business concepts to life.

The program starts in the fall and spans two academic semesters followed by two hybrid courses in the summer. Each semester builds upon the required semester before it, creating a laddered educational experience that helps every student succeed, regardless of background or experience.

Fall Semester

Duration: 16 weeks | Professor: Wael Jabr, Ph.D.

Use various database designs to acquire the information needed to make effective business decisions. Successful students will be able to design, create, and implement a relational database and be able to write SQL statements to obtain information from a database. In addition, students will investigate the next generation approaches for storing, manipulating, and managing web data in unstructured formats.
Duration: 16 weeks | Professor: Chris Solo, Ph.D.

Examine the use of descriptive analytics tools and techniques throughout a wide range of business scenarios and problems, focusing on the question, "What happened?"
Duration: 16 weeks | Professor: Arthur Jones, Ph.D.

Designed for recent graduates with little to no professional experience. This course expands upon the data visualization concepts covered in BAN 830 by exploring a variety of advanced data visualization techniques focused on "big data" sets derived from marketing, finance, accounting, supply chain management, and other business-related scenarios. Using the latest data visualization software applications, students will focus on the development of dashboards and scorecards useful for translating structured and unstructured business performance data into decision-ready knowledge.
Duration: 16 weeks | Professor: Rashmi Sharma, Ph.D.

Gain the foundational programming skills needed to leverage the power of leading-edge general-purpose programming languages to acquire, clean, manipulate, query, visualize, and analyze large data sets typical of a variety of business environments. With a focus on developing solutions to business data problems, students will become conversant with a variety of software applications in the context of financial, marketing, supply chain management, and other data-rich business scenarios.

Spring Semester

Duration: 7 weeks | Professor: Forrest Briscoe, Ph.D.

The objective of the ethical leadership course is to raise awareness of the key role played as a manager and leader in creating and maintaining responsible business conduct in work groups and organizations. The course is also intended to enhance the student's ability to deal with the complexities of ethical decision making in today's dynamic business environment by clarifying and applying personal values.
Duration: 16 weeks | Professor: Nancy Mahon, Ph.D.

One of the most important skills MBAs develop in business school is the ability to demonstrate the value of their experiences. This course provides students with targeted opportunities to develop this skill as they clearly, forcefully, and professionally represent ideas, opinions, and solutions. Students will participate in various oral, written, and graphic projects during the course.
Duration: 16 weeks | Professor: Russell Barton, Ph.D.

Explore the use of predictive analytics tools and techniques throughout a wide range of business scenarios and problems. Initially focusing on the application of traditional predictive analytics techniques to answer the question, "What will happen in the future?," the course provides opportunities for students to apply regression and forecasting techniques to data from various business contexts to inform business decisions.
Duration: 16 weeks | Professor: Maryam Zokaeinikoo, Ph.D

Apply a variety of data mining tools and techniques for use in detecting and exploiting patterns and relationships in large structured and unstructured data sets derived from a variety of business scenarios. Students will explore the use of cluster analysis, classification, association, and cause-and-effect modeling techniques to explore and reduce data, classify new data elements, identify natural associations among variables, create rules for target marketing or buying recommendations, and describe relationships among data that motivate business performance.
An elective course (500 or 800 level) can be chosen from a list of approved courses maintained by the graduate program office. The list of elective courses may change over time based on feedback from students and industry.

Summer Semester

Duration: 13 weeks | Professor: Terry Harrison, Ph.D.

Explore the use of prescriptive analytics methods in a variety of business contexts. In the early part of the course, the focus is on the tools and methods of prescriptive analytics. As the course progresses, the emphasis shifts to the effective integration and implementation of prescriptive analytics in supply-side decision-making processes such as supply chain management, service management, operations, logistics, and transportation. The application areas within business will reflect the interests of the instructor and will evolve as new areas of theory and practice develop.
Duration: 13 weeks | Professor: Nathaniel Bastian, Ph.D.

Understand the project life cycle from business problem framing to model lifecycle management. This capstone course sets analytics problem solving in a real-world context, including communication to non-statistically trained executives. Key topical areas are derived from the common activities of the business analyst and include business problem framing, analytics problem framing, data sourcing, cleaning and integration, analysis methodology selection, model building, model deployment and model lifecycle management including benefit assessment.