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BSAN 160: Business Analytics and Data Visualization
Credits: 4.0
Level: UG
The field of business analytics is concerned with managing and analyzing complex, high-dimensional data to support business decisions and improve firm performance. Data visualization is emerging as an essential complement to business analytics that greatly facilitates managerial understanding and decisions. This course introduces state-of-the-art techniques for data management, analysis and visualization in business contexts. It will emphasize practical challenges involving complex, real-world data and include several case studies and hands-on exercises with leading data analysis and visualization software.
BSAN 360: Programming for Data Analytics
Credits: 4.0
Level: UG
The mission of this course is to immerse students in the technical challenges associated with contemporary data analytics as applied to business processes and data-driven decision making. To achieve this mission, the course will introduce modules covering the state of the art in the areas of statistical programming as applied to data analysis for business problems.
BSAN 460: Business Analytics Senior Project
Credits: 4.0
Level: UG
The senior project serves as a capstone for business analytics majors. The course provides an opportunity for students to develop a project that draws on their skills in the areas of data management, mathematical modeling, and statistical analysis to support data driven decision-making processes. Students often choose a project in the area of a second major (marketing, finance, etc.) and thus the project provides deeper insight into organizational decision-making in a functional area of business.
BSAN 710: Business Analytics Capstone Project
Credits: 3.0
Level: GR
Contemporary business is embedded in a data-driven economy. Large datasets must be analyzed to understand the financial, economic, technological, societal, social, and environmental impacts of products, services, and policies. In this course, students execute a data-driven project in order to demonstrate their ability to meet this challenge. Projects draw on multiple quantitative skills: data management, mathematical modeling, and statistical analysis to support decision-making processes. Emphasis is also placed on ability to develop business insights from data and present findings to decision makers.
POM 642: Sustainable Supply Chain Management and Logistics
Credits: 3.0
Level: GR
This course presents management case studies on designing, evaluating, and improving supply chain operations with the goal of promoting environmental, social, and economic sustainability. Topics include product and process design for sustainability, cradle-to-cradle design, âgreenâ sourcing and procurement, reverse logistics and closed-loop supply chains, supply chain coordination for sustainability, end-of-life management, facilities location and design, sustainable transportation and logistics solutions.
STAT 630: Multivariate Analysis
Credits: 3.0
Level: GR
An introduction to multivariate statistics that focuses on the use of statistical methods for exploring and discovering information in large business datasets. Topics will be drawn from clustering and discriminate analysis for classification, principle components analysis for data exploration and variable reduction, factor analysis for indentifying latent variables, and other traditional multivariate topics.
BSAN 601: Business Analytics for Managers
Credits: 3.0
Level: GR
Business Analytics is an interactive process of analyzing and exploring enterprise data, in order to understand the past, make predictions about the future, and guide decision-making. In this course, students will learn how to use analytics in their decision-making. Analytics through the methodology of problem framing, model building, analysis, and communicating insights is explored.
BSAN 615: Data Visualization & Analytics
Credits: 3.0
Level: GR
Graphical methods enable data and information to be presented in an easily consumable, insightful, and actionable manner. Descriptive, diagnostic, predictive, and prescriptive analytics all benefit from data visualization, through the use of infographics, interactive visualizations, dashboards, and more recently, augmented analytics. This course will focus on three main facets of data visualization: strategy and implementation, Interpretation and insights, and communication and storytelling.
BSAN 605: Data Visualization for Managers
Credits: 3.0
Level: GR
For contemporary firms, data analytics are often an essential complement to human expertise and intuition in the decision-making process. To be successful, managers need to understand how data, models, and advanced computing can help meet the challenges of stronger competition, more sophisticated customers, and globalization. In this course students learn how firms can integrate data and tools to take advantage of business opportunities, improve organizational functions, and reduce risks in a data-oriented way. The course places particular emphasis on data visualization tools and methods that can be used to achieve better understanding of patterns, trends, and outliers in data. These often lead to valuable business insights. Managerial and leadership perspectives are highlighted throughout the course.
BSAN 261: Data Visualization for Business Analytics
Credits: 4.0
Level: UG
Data visualization enables business analysts to understand and present deeper and better insights into their businesses. These insights allow for greater managerial understanding and improvement in the quality of business decisions. This course is designed for students learning to use data visualization in business analytics, regardless of their technical or analytical backgrounds. Students will learn the fundamentals of data visualizations and the strengths and weaknesses of various visualizations. They will progress to learning to use data visualization software to conduct basic data analytics within an organization to support decision making, strategy formulation and operational improvements.
BSAN 620: Data-driven AI for Management Decisions
Credits: 3.0
Level: GR
This course is designed for MBA students to learn how data mining is used in business. Students learn how to ask meaningful questions of a data set, select appropriate analytical methods, interpret results, and evaluate the effectiveness of the analysis. The course focuses on business scenarios where data analysis is useful and uses real data sets or case studies. Students use the examples to (1) learn data management, data preprocessing, and data visualization, (2) become familiar with key data mining concepts such as classification, prediction, data reduction, model comparison, and anomaly detection and (3) understand the various types of AI that can aid in the data mining process, such as machine learning, generative AI, reinforcement learning, and rule-based systems.
POM I599: Independent Study in Production Operations Management
Credits: 0.0-12.0
Level: UG
Self-directed within the area of study requiring intermittent consultation with a designated instructor.