This course examines issues and functions that business managers face within the context of day-to-day operations and long-term planning of the organization. Topics include the tools used in business operations, the global dimensions of business, financial statement analysis, capital budgeting, ethical behavior, and economic analysis for decision-makers.

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 gives emphasis to understanding how the predictive analytic approach flows, as well as the process of analysis starting with a problem, and through effective analytics approach that is cohesive and integrating of various statistical analysis tools for predicting behavior of variables in a modeled relationship. (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 covers the elements of research, data collection, sampling, and data management including creating a broad range of quantitative and qualitative data and applying research methodologies while navigating rights and ownership and other ethical components. This course integrates quantitative and qualitative data management to prepare learners to understand how to inventory, store, and backup data. As well as how to create useable data to use, share, ethically reuse data, and covers aspects of digitally preserve data for the future. Students will also learn to create and data management plan. (Prerequisite: BUSN662)

This course is designed to lead students through various projects and business problem scenarios to enable them to apply concepts learned to quantify elements of alternative elimination, assess data pertinent to the overall decision-making process, and to gain and understanding of the different business functions and how data is used differently in each area.

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.