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.

(OPMT 605) - 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.

(OPMT 621) - 3 credits

Develop the quantitative and analytical skills needed to resolve practical business problems. Learn to analyze and solve management problems using spreadsheet-based methods aimed to clarify objectives, develop alternatives, address trade-offs and conduct a defensible quantitative analysis. Topics include spreadsheet modeling, linear programming, transportation modeling, decision analysis, project management and simulation. You’ll also get an introduction to building decision support models using Visual Basic Applications (VBA).

(SEIS 632) - 3 credits

Get an introduction to concepts and techniques used in the field of data analytics and visualization. Communicate insights discovered from the data using data visualization. Learn about predictive analytics, pattern discovery and best practices for creating effective data visualizations to develop proficiency in using analytics tools.

(SEIS 630) - 3 credits

This course will examine database management system concepts, database design and implementation. Learn how to capture the requirements of a database design using Entity Relationship (ER) conceptual data modeling, and explore system performance improvement with logical database design (Normalization) and indexing strategies. Work with a database using relational algebra and Structured Query Language (SQL). For your project, use Oracle and SQL Server to design a database and complete an application using SQL.

(SEIS 737) - 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

Managerial Writing and Presentations (BCOM 536):

(BCOM 536) - 3 credits

Get a framework for understanding managerial communication and a general model for employing skills. Focus on best practices for formal written and spoken communication in the workplace. Provide writing samples and classroom performances incorporating what you’ve learned from assigned reading and lectures. Respond to feedback from guest experts, your peers and your instructor.

Storytelling: Influencing Organization Decisions

(MGMT 708) - 3 credits

Refine your storytelling capabilities and study the principles that effectively link storytelling to influencing business outcomes. Explore the meaning of information and its effect on organizational strategy and culture. Learn how to build a structured thinking process or informational dashboard to tell a compelling story. Gain skills in confidently understanding and using information to influence outcomes.

Technical Communication

(SEIS 605) - 3 credits

Learn how to communicate more effectively in the workplace using the fundamentals of written and oral communication as practiced by IT professionals. Understand the importance of product descriptions, instructions, informative and persuasive oral presentations, the role of graphics, and teamwork on projects. Explore managerial strategies and tactics, such as planning and evaluation, that are critical for meeting an intended audience's needs. You’ll also study communication issues related to business analysis and project management.

(OPMT 625) – 1.5 credits

This course provides an introduction to the management of business operations. It focuses on the strategic role of the operations function in the survival and success of manufacturing and service organizations. You will explore a variety of strategic issues related to the design of operational systems and their connection with other functional and business strategies. The course will provide a multi-functional perspective on challenges and opportunities in managing operations, emphasizing the use of state-of-the-art concepts and quantitative methods for making critical choices in a dynamic business environment. Prerequisite: OPMT 600 or SEIS631

(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 625

(OPMT 730) – 1.5 credits

This course familiarizes students with the principles and practice of quality management through the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) structure, a well-established methodology to improve business processes, reduce variability and ensure quality. This course utilizes established quality tools and statistical analysis to identify, investigate, and improve any part of the organization that is facing quality concerns. This course will provide a conceptual foundation of quality theory and provide the necessary tools to monitor and improve quality within an organization. Prerequisite: OPMT 625

(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 625

(SEIS732) – 3 credits

In order to build and maintain a successful data warehouse, it is important to understand all of its components and how they fit together. This course will cover data warehouse and data mart lifecycle phases while focusing on infrastructure, design and management issues. The course project will provide an opportunity to for hands-on experience with some of the available tools and technologies.

Topics include: differences between data warehouses and traditional database systems (OLTP), multidimensional analysis and design, building data warehouses using "cube" vs. RDBMS (Star schema, etc.), planning for data warehouses, extraction transformation and loading (ETL), online analytical processing (OLAP), data mining, quality and cleansing, common pitfalls to avoid when designing, implementing and maintaining data warehouse environments, and the impact of new technologies (data webhouse, clickstream, XML). Prerequisite: SEIS630

(SEIS 734) – 3 credits

To overcome data overloading problems, this course will discuss how to apply big data analytics to extract useful patterns from huge datasets and generate visual summary of data. This course will also demonstrate mining and analyzing big data on Amazon Cloud. Key topics of this course include: (1) mining association rule and market basket analysis, (2) classification and predictive analysis, (3) clustering and market segmentation, and (4) combining numeric analysis with text sentiment analysis. Real-world data will be used to illustrate the data mining concepts and their possible pitfalls. Sample case studies include: (1) predicting company’s credit ranking, (2) classifying cancer types from gene data, (3) analyzing sentiment from customer text reviews and numeric rankings, (4) finding associations among medical keywords from medical journal papers. Prerequisite: SEIS630

(SEIS 735) – 3 credits

This course will discuss processes in health care analytics, including data acquisition, storage, retrieval, management and analysis of health care data in heterogeneous formats (i.e. numeric health records, medical text and medical images). Major topics include: (1) analyzing patient records and identifying frequent medical sequences for treatment and prevention; (2) evaluating medical text and generating aggregated summary based on hierarchical medical concepts; (3) retrieving information from different types of medical images; (4) building clinic decision support systems to detect possible medical mistakes; and (5) comparing brain connectivity graphs from patients with different neurological conditions. Amazon Cloud will be used to analyze multi-million records of numeric and text data. Prerequisite: SEIS 632

(BLAW 635) – 1.5 credits

Explore the various legal, regulatory and ethical issues that relate to collecting, using, retaining and securing personal data. We will examine industry-specific data privacy requirements for several industries, including health care and financial and credit reporting. Other topics will include legal and ethical issues related to the use of data in social media and behavioral advertising, liability for information security risks, and the duty to comply with international, federal and state laws governing information security. You will develop the multi-disciplinary awareness and analytical thinking necessary to when considering technological, strategic, managerial and ethical issues related to big data, privacy and information security.

(MGMT 630) – 3 credits

This course provides you with an accurate understanding of the various components of the health care system – providers, consumers, payers and third-parties – and how they relate. You will learn about issues, motivations and incentives that influence all parts of the system, and gain an understanding of the political and social environments in which they operate.

(MKTG 729) – 3 credits

Marketing decisions are increasingly data driven. In this course, you will learn how to analyze marketing data to inform effective decision making. You will also develop a deeper understanding of current and emerging customer needs through the use of a broad range of marketing analytic techniques and gain hands-on experience with marketing data tools to develop useful customer insights to guide marketing decisions. Prerequisite: OPMT 600 or SEIS 631

(ETLS 551) – 3 credits

Strategic quality management is presented as a Driver--> System--> Results model. The DSR model provides a framework for better understanding your business and when and where to take action to improve results. The model is a tool that links company mission, strategic plans, competitive positioning, and customer focus as the DRIVER. People and processes form the SYSTEM that actually designs, produces, and delivers products and services. RESULTS include financial, customer, employee and process. The course also connects the DSR model to the Malcolm Baldrige Criteria for Performance Excellence, six sigma and lean improvement tools ISO 9000, and Quality Management Systems and tools such as Statistical Process Control (detailed training in tools such as SPC is not part of the class). In addition to developing an understanding of how to guide and manage quality strategically, the course also helps to identify and prioritize the "right questions to ask" to guide and manage tactically. Applying the course to real world situations should lead to improved results - financial, customer, employee and process.

(ETLS 640) – 3 credits

Lean Six Sigma is a seminar course designed for combining Six Sigma quality and lean speed. Guest speakers will be utilized to develop knowledge of the inter-relationship of these two concepts and how to develop plans for product and process improvements in development, production operations as well as service activities. Each student will create specific plans for their organizations using these concepts.

(BCOM 620) – 3 credits

Explore the literature of creativity, the study of creative persons and their contributions to society, and the process by which creative ideas are produced and communicated. Active participation in strategies for actualizing the creative potential of individuals and groups is an essential part of the course.

(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 607) – 3 credits

Businesses increasingly use teams to get work done at all levels of the organization, but often teams are not managed effectively. This course examines when teams are the right choice (and when they are not), how to be an effective team member and leader, and how to diagnose and solve common team problems. You will also examine how teams operate under special circumstances, e.g., cross-functional, temporary, global, and distributed (or virtual).