This course introduces advanced statistical methods of building models for decision-makers, with a primary focus on modeling techniques such as logistic regression and discriminant analysis. Students will explore the application of statistical models through examples in business, finance, market research, and healthcare management.
Learning Outcomes: Ability to use SPSS to solve problems and visualize data and apply statistical concepts and techniques to solve real-world business cases
Databases and Big Data Management
This course provides an understanding of the business value of big data, the importance of effective management of big data, and the development of technical competencies using leading-edge platforms for managing and manipulating structured and unstructured big data.
Learning Outcomes: Ability to use data management platforms such as Hadoop and MySQL to store, retrieve, modify, and process data
This course introduces the basic principles and techniques of applied mathematical modeling for managerial decision-making. The course emphasizes skills in model formulation, assumptions and limitations, and interpretation of results, with some discussion of mathematical theory. Students learn about models widely used in diverse industries and functional areas, including finance, operations, and marketing.
Learning Outcomes: Ability to identify patterns in problem-solving, develop models based on organizational problems, identify solutions, test results, and effectively analyze and communicate findings
This course teaches students critical skills for succeeding in today’s data-intensive world, including business case studies, data analysis, and management recommendations. Students learn how to utilize database systems and analytics software and how to make trustworthy predictions using traditional statistics and machine learning methods.
Learning Outcomes: Ability to use tools like Python and SPSS to develop data-based predictions for businesses and organizations
This course is designed to develop a student’s ability to model and analyze real systems using event simulation. It will apply computer modeling and simulation approaches for studying complex systems, with emphasis on using general-purpose programming tools. Areas covered include system structure, system analysis, model construction, data collection, and computer simulation.
Learning Outcomes: Ability to collect data, formulate an appropriate simulation model for a system, implement the model as a computer program, and evaluate the output of the model
Applying Analytics to Achieve Business Impact
In this course, students will explore how the ability to use data has driven rapid, precise, and profitable decisions for Fortune 500 companies. Students will be equipped to identify, evaluate, and capture business analytic opportunities to create value within their own organizations.
Communications with Data
This course prepares students to communicate data insights, and it involves a combination of three key elements: data, visuals, and narrative. Students will get hands-on experience in collecting and organizing data in Tableau, and communicating a specific position or point of view from data through a combination of data-driven visuals and a carefully crafted storytelling technique.
Learning Outcomes: Ability to use Tableau to collect data and translate it into a compelling and influential data story
This course covers the use of information technology and systems that enable and enhance marketing strategies and tactics. This course prepares managers to face the challenges of various information systems, data collection methodology, and organization; the process of mining valuable information from the data; and ethical situations created by data collection and information use.
Programming for Business Analytics
This course introduces students to programming using Python, with a focus on learning how to develop algorithms. Current Topics (Python) will function primarily as a programming course, and students will practice applying their programming skills to analytics problems in examples, homework, and projects.
Learning Outcomes: Ability to apply Python programming skills to solve analytical programs for businesses and organizations
Multiple Attribute Decision Analysis
This course examines a major class of problems in decision analysis: one-time decisions where a group of alternatives must be compared on the basis of multiple (and possibly competing) goals and objectives. Students will consider the social and environmental consequences of their firms’ actions, and how the ability to solve multi-attribute-decision problems has become more important than ever.
Learning Outcomes: Ability to consider social, ethical, and environmental implications while making decisions, and choose a solution that will bring about the best possible outcome
Social Media Analytics
This course focuses on the difference between knowing what stats mean and knowing which stats are meaningful. Students are first able to identify which metrics are important for decision making and focus on these rather than “vanity” metrics. This class also equips students to make critical decisions regarding trade-offs in terms of what is most important to decision makers.
Business Process Analysis and Innovation
In this course, students will be introduced to key concepts and approaches to business process analysis and improvement. The main focus of this course is both understanding and designing business processes that accomplish specific desired outcomes. Students will learn how to identify, document, model, assess, and improve core business processes.
This course includes a review of the fundamental theory of decision analysis and options as well as an introduction to numerical techniques for solving dynamic programming problems, such as binomial lattices and trees. The course also provides hands-on experience with software tools used for the numerical analysis of problems using these ideas.
This course offers a comprehensive introduction to the fundamentals of healthcare research methodologies, including research design, data collection, and applied statistics. In addition, this course introduces students to basic operations research/management (OR/OM) techniques and demonstrates how those tools can also be applied in health service management.
Information Security Data Analytics
This course equips business managers to recognize and address the key risks to business information systems and data. Utilizing data analytics across different dimensions is critical for effectively providing information security analytics. An essential element of a risk-based approach is the use of user-behavior analytics (UBA) to compare and contrast threats against normal behavior.
Business Analytics and Intelligence
This course introduces techniques to transform data into business intelligence and to use analytics to create business value. Students learn to develop solutions to real-world problems through a combination of readings, case studies, applied projects, technology demonstrations, guest lectures, and assignments to analyze and interpret real data.