This course introduces the history and evolution of AI, its impact on various business sectors through real-world case studies, and practical tools to implement AI solutions for business problems. Students will also examine the ethical challenges of AI adoption in business, ensuring they are equipped to lead responsibly in an era of rapid technological change.
This course explores the application of data mining techniques to large organizational datasets to evaluate options, predict outcomes, and provide recommendations for optimal decision-making. Key topics include classification, regression, and clustering methods widely used in data analytics, with a focus on practical implementation through programming.
This course serves as a business student’s introduction to leveraging data for managerial decision-making. It covers foundational data analytics methods to solve decision problems across various applications and introduces the visualization tool, Tableau, for effectively analyzing and presenting large datasets.
This course is designed to acquaint students with principles and concepts of operations management (OM) as well as quantitative techniques and other tools or models used by OM professionals to improve their decision-making processes in both the service and manufacturing sectors. Course topics include productivity, forecasting, strategic capacity planning, product and service design, process selection, facility layout, quality control, inventory management, and linear programming.
This course emphasizes solving real-world business problems through practical, hands-on experience with modern spreadsheet applications and database management systems.
This course provides non-business students with an introduction to how data informs managerial decision-making. Using an example-based approach, the course addresses business decision problems from diverse perspectives. It adopts a unified framework that integrates concepts from various statistical models and emphasizes practical applications through real-world business examples.
This course focuses on applied statistical methodologies for time series analysis, emphasizing model development and accurate forecasting. Students will gain practical insights and hands-on experience with modeling tools to analyze real-world business time series data for forecasting and risk assessment.
This course focuses on statistical methods for solving business data challenges. Topics include basic probability theorems, hypothesis testing, analysis of variance, and linear regression models. Students will learn to formulate problems using statistical techniques and utilize computing software for analysis and computation.