Online Master of Science in Applied Business Analytics (MS)

This foundational course equips students with the essential knowledge and tools to interpret, analyze, and leverage data in the business landscape. Students will explore key analytical principles, learn about a diverse range of tools and techniques, and understand their application in real-world business scenarios. From data collection to insightful interpretation, students will gain a holistic perspective of the analytics domain.

This course covers data mining using the R programming language. It offers hands on experience approach through a learn-by-doing-it strategy. It further integrates data mining topics with applied business analytics to address real world data mining cases. It continues the examination of the role of “Data Mining in R”, and review statistics techniques in prescriptive analytics, and some predictive analytics. Additionally, some standard techniques and excel functions will be also covered.

This course covers the elements of text mining techniques used to complement data mining methods but for unstructured text. The essential transformation techniques where text is prepared and handled to a form in which it can be mined is discussed and explained. Additionally, some standard techniques and excel functions will be also covered. (Prerequisite: BUSN662)

This course equips students with the skills to forecast business outcomes based on historical data and sophisticated modeling techniques. Students will explore a gamut of predictive models from regression analyses to state-of-the-art machine learning algorithms. Class participants will practice building, validating, and deploying models to address pressing business challenges and to ensure proactive strategic initiatives. Emphasizing both theory and application, this course is essential for students aspiring to learn how to harness the predictive power of data to shape business trajectories. (Prerequisite: BUSN662)

This course primarily covers handling elements of the influence on a business performance that can be constraints for achieving certain results. The course discusses optimization methodologies to support management choices. Students will learn about applying linear programming principals to harness a precise objective considering a set of business constraints. Students will use spreadsheet software to implement and solve these linear programming problems. (Prerequisite: BUSN662)

This course offers a comprehensive overview of the methodologies, tools, and best practices required to store, organize, and maintain high-quality datasets. Students will explore relational databases, data warehousing, and the essentials of data governance and security. Through hands-on exercises, participants will design and implement database structures, ensuring data integrity and accessibility. This course may be useful for students seeking a mastery of the backend of analytics. (Prerequisite: BUSN662)

This course centers on leveraging analytics at a grand scale, ensuring students are well-equipped to drive strategic initiatives across large corporations and multifaceted institutions. Students will acquire a knowledge of holistic data strategies, systems integrations, and the nuances of analytics deployment in diverse enterprise environments. Class participants will participate in real-world case studies, simulating the challenges and opportunities of analytics in expansive organizational settings.

Preparation for the Applied Business Analytics Project/Capstone begins on day one of a student's graduate program of study. The theories, research methods, analytical skills, analytical tools, and substantive knowledge obtained through their master's curriculum provide the basis for a major data analysis project. Students will work closely with the assigned faculty member to develop the subject matter of their project. The experiential or practical component of the class aims to apply learning in an aspect of interest related to the degree and concentrations of the student’s areas of specialization. It is understood to be a supervised project where data is collected from or about an approved organization. The selection of an organization or site for the project must relate to the content of the student’s course work and/or analytics concentration. Goals of the applied project will be submitted by the student using an application for approval to the faculty member and/or Program Director. The organization will serve as an opportunity to experience the practice of business or big data analytics. This option will act as a capstone of the student’s program and is to be completed in the student’s final semester.