MS in Business Analytics Curriculum

(BUAN 640) – 3 credits

This course provides students with a basic understanding of the role of statistics in the gathering of data, the creation of information and its use in decision-making. Students will learn methods for summarizing data, both numerically and graphically, and for drawing conclusions from sample data. Statistical analyses will be carried out using the computer and statistical software. The focus of the course is on how statistical methods can be applied to business problems to improve outcomes; stress is placed on the design of statistical studies, collection of data, and the interpretation of results. The course will also focus on interpreting computer output and less on generating numbers through hand calculations.

Prerequisite: NONE.

(BUAN 600) - 3 credits

This course teaches students how to perform data analysis using spreadsheet-based methods to effectively and efficiently solve management problems. Students will learn how to effectively build, present and communicate advanced Excel spreadsheet models, forecasting models, optimization models and simulation models to drive managerial decision making. Students will also learn how to build interactive, data driven dashboards using Power BI to discover new insights and monitor key performance indicators.

Prerequisites: NONE

(BUAN 610) - 3 credits

This course will focus on developing the ability to understand the business needs for data insights, crafting those into an analytics problem statement, and developing a coherent and persuasive narrative of any data findings. Students will learn to create well-crafted data narratives and dashboards for business leaders while being able to translate insights into managerial decisions. Students will also be able to prepare raw data sets for their data narratives, executive summaries and technical memos. The Data Narratives course focuses on providing these fundamental data narrative and storytelling abilities while leveraging various tools to assist in the process.

Prerequisites: NONE

(BUAN 620) - 3 credits

This course covers the life cycle of data for analytics from the structure of relational and non-relational data stores, though the extraction, transformation, and loading (ETL) process, and into the analysis and presentation of data using data dashboards. Students will learn and practice acquiring, extracting, cleaning, and loading data from databases and other data stores. Students will learn to interpret and create data models, write and interpret the results of Structured Query Language (SQL), practice and apply industry ETL tools to solve business problems, and effectively communicate about data through the use of a dashboarding tool.

Prerequisites: NONE.

(BUAN 630) - 3 credits

In today's rapidly evolving business landscape, AI has emerged as a transformative force, reshaping industries, and redefining the rules of success. This course aims to bridge the gap between AI technology and business strategy, enabling students to integrate AI into their organizations and drive measurable results effectively. Through a combination of real-world case studies and hands-on projects, students will explore the various applications of AI in business, including customer experience enhancement, operational optimization, predictive analytics, and product innovation. The course will cover key AI technologies such as generative AI, machine learning, natural language processing, and computer vision, focusing on their practical implementation within a business context.

Prerequisite: None

(BUAN 650) - 3 credits

Gain a better understanding of data analysis for business research, emphasizing the interpretation of data rather than calculations. Develop techniques commonly used in business such as logistic regression, two-way analysis of variance and statistics for scale development. These skills are relevant for students involved in marketing research and survey development. Course deliverables will include a project, potentially based on a situation or analysis from your workplace or industry.

Prerequisite: BUAN 640.

(SEIS 603) - 3 credits

Learn the technical concepts of managing vast amounts of unstructured, semi-structured and structured data, collectively called “Big Data.” Learn why big data sets must be distributed and examine the issues that distribution introduces. Start by outlining the basic concepts on which distributed data sets are handled. Once that foundation is defined, move on to study the software tools that we use to work with big data sets for an in-depth analysis of the concepts.

(BUAN 799) - 3 credits

This application-focused course provides the opportunity for students to experience a real-time business analytics project. Under faculty guidance and mentoring, small teams of students will work together to implement the breadth of methods and skills developed throughout the MSBA program to manage all aspects of client and project management; develop the project deliverables including business problem analysis, data transformation and analysis; and presentation of the results at the client site. The course will begin with limited on-campus meetings, then transition to a flexible “directed study” format with regular required check-ins with the faculty leader, providing ample time for the team to complete the project work. Teams will use online collaboration software tools for communication and project coordination.

Prerequisite: 21 credits completed

(SEIS 763) - 3 credits

Machine Learning builds computational systems that learn from and adapt to the data presented to them. It has become one of the essential pillars in information technology today and provides a basis for several applications we use daily in diverse domains such as engineering, medicine, finance, and commerce. This course covers widely used supervised and unsupervised machine learning algorithms used in industry in technical depth, discussing both the theoretical underpinnings of machine learning techniques and providing hands-on experience in implementing them. Additionally, students will also learn to evaluate effectiveness and avoid common pitfalls in applying machine learning to a given problem.

Prerequisite: SEIS 631 and 632 (can be taken concurrently)

(MKTG 729) – 3 credits

There is growing demand for marketers with the technical skills needed to make use of data to inform marketing decisions. Students will work hands-on with marketing data as they learn how to use the tools (mainly R, some Excel) and methods necessary to develop useful customer insights. Students will also learn what marketing questions – segmentation, customer lifetime value, etc. – these methods are meant to address. This course is quantitatively oriented, and some of the methods will be very technical. But these methods are means to an end: to better understand our customers in order to make informed marketing decisions.

Prerequisite: OPMT 600; Recommended: OPMT 605.

(ETLS 640) – 3 credits

Lean Six Sigma is a course designed to promote an understanding of two popular international methodologies – Lean and Six Sigma. A brief overview of the origin and definition of each will then be followed by an extensive review and understanding of concepts, principles and tools. Through lecture, group discussions, hands-on simulations, team exercises and guest speakers, students will develop knowledge of the inter-relationship of these two methodologies and how to implement for product and process improvement in all types of organizations and throughout all functional areas. Soft skills will also be covered such as working with cross-functional teams, driving organizational change and leading in a Lean Six Sigma culture. This course will not include the use of any statistical analysis tools. This course will provide a framework for students who plan to pursue Lean or Six Sigma certification.

(MKTG 774) – 1.5 credits

Digital Marketing continues to rise and has become core to marketplace success. This course provides an overview of how Digital Marketing can be engaged to significantly contribute to achievement of business goals and priorities. This course examines the concepts, strategies and applications related to Websites, Display Advertising, Search, Email, Social and Mobile Marketing with an explicit focus on how each area can be utilized to acquire and strengthen customer relationships across the customer life cycle.

Prerequisite: MKTG 625 or MKTG 600

(MKTG 778) – 1.5 credits

This course offers a hands-on understanding of how to set up, monitor and optimize the effectiveness of Digital Marketing campaigns in alignment with business goals and objectives. Students will learn to use of state of the art Digital Marketing Analytics tools such as Google Analytics and Adobe Analytics for daily analysis as well as prepare dashboards for sharing periodic results with executives, peers and staff.

Prerequisite: MKTG 774

(OPMT 750) – 3 credits

This course will provide an overview of the basic principles of supply chain management, giving students an understanding of supply chain processes from sourcing to finished goods and customers to suppliers, identifying the core supply chain processes. Students will learn the key mechanisms of how companies manage internal processes that control the flow of inventory in the supply chain.

(SEIS 764) - 3 credits

Artificial Intelligence has made significant strides in recent times and has become ubiquitous in the modern world, impacting our lives in different ways. By harnessing the power of deep neural networks, it is now possible to build real-world intelligent applications that outperform human precision in certain tasks. This course provides a broad coverage of AI techniques with a focus on industry application. Major topics covered in this course include: (1) how deep neural networks learn their intelligence, (2) self-learning from raw data, (3) common training problems and solutions, (4) transferring learning from existing AI systems, (5) training AI systems for machine visions with high accuracy, and (6) training time-series AI systems for recognizing sequential patterns. Students will have hands-on exercises for building efficient AI systems.

Prerequisite: SEIS 763

(MGMT 623) – 3 credits

As project managers, we face impossible schedules, unrealistic specifications and limited budgets. As leaders we face personnel issues, motivation requirements and organizational issues. This course will provide insight and practical examples of the areas of knowledge needed to practice effective project management in today's dynamic work environment. You will learn why similar pitfalls are often encountered with each new project as we examine the chaotic project life-cycle, the complexity people bring to projects and the reasons why our organizations continue to become more chaotic. You will examine the new phase development of project management and use numerous disciplines to create a more dynamic and flexible project management methodology, including: industrial behavior, psychology, human behavior, chaos and complexity, organizational behavior, and systems theory.

(MGMT 708) – 3 credits

This course will introduce students to principles that effectively link storytelling to influencing business outcomes ethically and effectively. Our business culture demands concise and meaningful communications that can both inform and influence decision makers. This course is designed to teach business professionals principles and skills that enhance their thinking about data and the use of a variety of communications channels to facilitate positive business decisions. Students will explore the meaning of information and its effect on organizational strategy and culture; be able to build a structured thinking process to tell a compelling story; and gain skills in confidently understanding and using information to influence outcomes.