MS in Business Analytics Curriculum

Statistical Methods for Decision Making

(OPMT 600) - 3 credits

Examine statistical and analytical methods including sampling concepts, regression analysis, hypothesis testing, forecasting, quality control, simulation and database management.

Foundations of Data Analysis (R-Environment)

(SEIS 631) - 3 credits

Get a broad introduction to the subject of data analysis, focusing on relevant methods for performing data collection, representation, transformation and data-driven decision making. You will also develop proficiency in the widely-used R language, used through the course.

(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.

(SEIS 632) - 3 credits

The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.

(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: OPMT 600 or SEIS 631.

(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.

Prerequisite: SEIS 630

(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.

Prerequisites: OPMT 600 or SEIS 631 and BUAN 600, BUAN 610, BUAN 620, SEIS 603, and 18 completed credits (total).

(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 603 and 631

(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 710) – 1.5 credits

Supply chain management focuses on the planning, coordination and control of the activities involved in procurement, transformation, and distribution of goods and services. This course develops a basic understanding of various systems and procedures used for managing the supply chain in manufacturing and service industries. The course will provide a multi-functional perspective on problems and opportunities in areas as including business forecasting, sales and operations planning, procurement and inventory management, production planning and control, distribution and logistics management; as well as conceptual and analytical framework for managing them. The course will focus on decisions that convert broad policy directives into specific actions in a dynamic business environment.

Prerequisite: OPMT 600

(OPMT 760) – 1.5 credits

This course is designed to explore the application of analytical tools in the service industry from the perspective of Operations Management. It prepares students for the new challenges in the service sector and suggests creative opportunities for applying analytics in different services. Outstanding service organizations are managed differently than their manufacturing counterparts. The results show not only in terms of measures of service performance, but also in the enthusiasm of the employees and degree of customer satisfaction. Service industries discussed in this course will include healthcare, airlines, hotels, restaurants, entertainment (Disney), sports, internet services and service supply chains. This course will build upon concepts from Operations Management, and will integrate material from strategy, marketing, technology and organizational issues. Since the service sector is the fastest-growing sector of the economy, special emphasis is given to application of analytics to keep pace with the industry growth.

Prerequisite: OPMT 610 or OPMT 625

(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.