Business Analytics

This course covers key concepts related to predictive and prescriptive analytics by combining information technologies and statistical techniques to extract meaning from organizational data. The course includes hands-on work with data and software. Topics covered include data manipulation, decisions under uncertainty, and decision analytics tools (linear and nonlinear optimization). Students apply predictive and prescriptive analytics techniques in order to understand the business environment and guide business-related decisions. Fall even years. Prerequisite: BUSN 260 Business Analysis Tools; must be a junior or senior or have permission from the instructor.

Statistics for Business Analytics covers fundamental statistical concepts and terms relevant to business analytics, providing a comprehensive overview of the role of statistics in business decision-making. Students will learn to describe and explain statistical methods, interpret data using descriptive and inferential techniques, and apply these techniques to real-world business scenarios. Through the course, students will analyze business data to identify trends, patterns, and relationships, using time series analysis, correlation, and other statistical methods. The course introduces the development and interpretation of predictive models using regression analysis. Fall even years.

Data Visualization & Reporting introduces best practices in data visualization for more effective reporting of analytical results in a business context. Students learn analytical methods and technologies used to create dashboards and scorecards for more effective communication of patterns and relationships in data. In this course, effective design of data visualizations, choice of chart type, and the effective use of color and other design characteristics are covered. The course covers both the principles of data visualization, methods and tools used to visualize insights from data, and strategies for the effective communication and reporting of data-based insights. Spring odd years.

Predictive Modeling & Prescriptive Analytics provides students with skills applying more advanced predictive and prescriptive analytic techniques utilized in business analytics. In the predictive analytics domain, the focus is on the use of statistical and machine learning techniques to predict or forecast future outcomes. Topics include multiple regression analysis and more advanced hypothesis testing techniques. In the prescriptive analytics domain, the focus is on the use of data-driven models to prescribe the best action plan given a set of conditions or a problem situation. Topics will include spreadsheet modeling, optimization models, simulation, and an introduction to additional machine learning algorithms. The emphasis is on model formulation and interpretation of results. The course covers a wide range of predictive and prescriptive methods that are widely used across the various functional areas of business. Spring even years.

Data Mining provides an overview of the principles and techniques of data mining. Data mining is the science of discovering structures and making predictions in large, complex data sets. The course introduces the basic concepts, principles, methods, implementation techniques, and applications of data mining, with a focus on two major data mining functions, pattern discovery and cluster analysis. Topics covered include the data mining process, data preprocessing, data mining techniques, and data mining evaluation. Fall odd years.

Project Management for Improving Business Systems provides students with the essential skills and methods required to manage projects successfully within the organization. The curriculum aligns with the Project Management Institute's (PMI's) A Guide to the Project Management Body of Knowledge (PMBOK Guide) and offers a comprehensive overview of the four phases of project management: defining, planning, executing, and closing. Students will learn to evaluate and prioritize projects, identify and assess risks related to project-based improvement initiatives, allocate human and technical resources, and evaluate the quality of project deliverables against predefined standards. Fall.

Analytical Programming for Business provides a foundation in programming logic, techniques, and analytical methods. The course employs modern programming languages and tools, such as Python and R, to develop scalable programming solutions to business-related problems. This course focuses on the practical applications of programming such as interaction with data sources, data preparation, processing data with logical operations, analyzing the data to conclude insights, and visualizing and reporting the data. The student will also learn to debug code, manipulate data structures and prepare data for analysis, automate workflows, and apply foundational statistical, data visualization, and machine learning techniques. Fall.

Prerequisite: must be a junior or senior or have permission from the instructor.

Applied Problem-Solving for Business Analysis introduces business students to the use of quantitative tools and structured methods for effective data-based problem solving and decision analysis. The course develops skills necessary to formulate, evaluate, and communicate solutions based on evidence and data. It covers the four major stages of problem solving - problem identification and definition, developing theories and hypotheses relationships, analyzing data and evidence to validate or invalidate causal theories, and effectively communicating recommended solutions. The focus is on business process improvement and provides an overview of some of the common systematic approaches for business problem solving used in the investigation of relationships with data. Spring.