MS in Business Analytics Curriculum

This course is designed to help you develop and polish essential career and professional development pieces. The idea is that, whether you are looking for a new job, aiming for a promotion, or happily working in your current position, you need certain items in your career toolkit. Those items include resumes, cover letters, elevator pitches, LinkedIn profiles and behavioral interviewing techniques. We'll use assigned readings, instructional videos, and other guidelines to establish criteria for meeting today's standards. Rubrics establish criteria for meeting expectations of each assignment and provide a framework for instructor feedback. Discussions are optional. Successful completion of the course is based on quality submission of assignments.

This course teaches students how to maximize spreadsheets as a data management tool using advanced functions and formulas. Students will learn how to efficiently manipulate, format, and automate data, and use spreadsheets to build customized reports, dashboards, amazing charts, pivot tables and macros. This course is applicable to all business disciplines. Prerequisites: basic spreadsheet knowledge. (3 Credits)

This course provides an introduction to Business Intelligence, including the processes, methodologies, infrastructure, and current practices used to transform business data into useful information and support business decision-making. Business Intelligence requires foundation knowledge in data storage and retrieval, thus this course will review logical data models for both database management systems and data warehouses. Students will learn to extract and manipulate data from these systems and assess security-related issues. Data mining, visualization, and statistical analysis along with reporting options such as management dashboards and balanced scorecards will be covered. (3 Credits)

This course provides an introduction to programming with a focus on business and analytics applications. The curriculum supports the development of skills in data extraction and manipulation as well as automating data analysis tasks. Students will become adept in the use of important libraries and will be able to use trusted open-source resources to support continued development of programming skills. No prior programming experience expected. (3 Credits)

This course provides an overview of statistical analysis and inference. The language and methods of statistics are used throughout the MBA curriculum, both in the classroom and in assigned readings. In addition, the language and methods of statistical analysis have permeated much of academic and professional writing as well as media reporting, both inside and beyond the business community. The goal is to present a broad introduction to statistical thinking, concepts, methods, and vocabulary. The course has an emphasis on business applications. (3 Credits)

This course provides an introduction to topics in managerial data mining, and related data retrieval and manipulation content. Models in regression, clustering, neural nets, classification, and association rule mining are applied to business data sets. In this managerially-focused course, students will learn how to use such models and interpret output. Students will also be introduced to a programming language for extraction and manipulation of data. Experiential exercises will be assigned using leading industry applications. (3 Credits)

This graduate course provides comprehensive coverage of project management, focusing on the methods and competencies needed to effectively manage projects. Upon successful completion of this course, students will be able to understand different project management techniques, terminology and trends; define project success measures and methods; improve quality, cost and efficiency with the project management process; and gain basic competency using project management software. (3 Credits)

An overview of the concepts and language of financial and managerial accounting that covers how accounting information can be used as an effective tool for communication, monitoring, and resource allocation. Topics include the principles and methodologies underlying financial statements and the inherent limitations of that information. Additional topics include behavior, cost analysis, and tools used to motivate and coordinate business activities. (4 Credits)

Basic concepts, principles, and practices involved in financial budgeting, planning for the cost of capital, and managing financial aspects of organizational sustainability and growth. Both theory and techniques applicable to financial problem solving.

This course provides an executive viewpoint of marketing concepts, such as branding and segmentation, for strategic and organizational decision-making. There is an emphasis on tools available for analysis and control of marketing activities, including the use of new media. Topics also include industrial life cycles, customer experience, and pricing strategy.

Provides an overview of different perspectives and approaches to leadership practice and related organizational behavior topics. Relying on a participative multi-learning approach, the course goes beyond traditional leadership practices to examine empowering leadership approaches for addressing contemporary organizational behavior challenges such as leading teams. The overall objective is to increase students' understanding of and ability to apply contemporary leadership and organizational behavior knowledge in work situations.

This course covers essential concepts and methods to foster data-driven decision-making to motivate business insight and innovation. Lessons cover topics in big data, algorithmic thinking, machine learning, data visualization, and communication. Projects spanning several weeks focus on business problems that can be solved through the application of cutting-edge Data Science techniques. Students develop proficiencies in the use of Python, R, and Tableau by completing regular exercises and projects using real data. No prior programming experience is expected.

Data Science for Business is a required course, plus complete 9 credits of electives from the courses listed below:

This course provides an introduction as well as hands-on experience in data visualization. Students will learn how to leverage data visualization for exploration and analysis. Students will be introduced to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making. Explore storytelling through a variety of mediums, including presentations and dashboards.

The course provides an introduction to business analytics within the context of the sport industry. It first examines the range of cross-disciplinary concepts and techniques that form the foundation for the field of business analytics. It then applies these concepts and techniques to both the administrative side of the sport business - like pricing decisions, stadium management, etc. - and to the on-field side. With the latter, topics include player performance measurement, in-game decision making, and player selection/team building.