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The use of quantitative or statistical techniques in managerial decision-making. Students with credit for BUS 553 may not take this course for further credit.

An introduction to the theories and practices of managing information technology. Uses case studies to analyze complex situations and develop skills necessary to select, deploy and use information systems. Students with credit for BUS 554 or BUS 621 or BUS 739 or BUS 756 may not take this course for further credit.

Introduces analytical knowledge that enables students to develop foundational knowledge of artificial intelligence (AI) and machine learning (ML), including the tools and methods of application and associated risks for use case identity across various business functions. Students with credit for BUS 774 under the title "AI in Business" may not take this course for further credit.

Builds on concepts introduced in BUS 706 to equip students with advanced skills in the use of data and models to make decisions. Focuses on stochastic, prospective, competitive, goal and productivity analytics. Prerequisite: BUS 706.

Data science tools like data wrangling, data analytics and visualization are introduced as they apply to areas such as marketing, human resources and operations management. Through a hands-on approach, students will learn various Python packages and Jupyter notebooks and how to use these tools to execute relevant data science techniques on complex data with a special focus on natural language processing, which is essential to understanding various textual information collected through various business processes.

Offers a comprehensive overview of advanced analytical and machine learning techniques used to predict customer behaviours such as patterns, responses to marketing initiatives, and customer loyalty.

Develops conceptual, analytics and decision-making skills related to generative AI use in marketing, with a focus on product development, design thinking and communication strategies.

Enterprise information systems, the relational database systems that underlie them, and creating value through competitive analytics. Develop an understanding of database querying and analytical applications to inspect, summarize, and transform data.

An exploration of financial and non-financial data using summary measures, predictive models for decision-making, and graphic visualizations.