»Master of Science in Business Analytics

This course builds a foundation of machine learning and statistical models, focusing on knowing when and how these models can contribute to business objectives. Topics include uncertainty and bias-variance trade off, linear regression, classification models such as logistic regression, regularization models (Lasso and Ridge), decision trees and random forest, as well as interpretable machine learning and data management in SQL. Students will learn to apply these models in R or Python, but the course assumes no prior coding experience. 

Modern data storage systems provide a wealth of data, but accessing these systems requires specialized tools and an understanding of how they can impact a business. This course covers basic database skills around extracting, transforming, and loading data. Students will learn SQL for tabular data storage and will also  cover “NoSQL” storage variants.

This course will utilize data visualization techniques to build rich and vivid data-driven output for business consumption. This course will cover data visualization theories and focus on techniques using a variety of tools, including data dashboards (Tableau), modern plotting, advanced plotting and visualization (using libraries such as ggplot, seaborn, matplotlib, plotly, etc.). Additional focus will be placed on the managerial aspects of presenting data, including understanding audience receptivity to data and mapping to necessary business outcomes. It will also allow students to use advanced data visualization tools to translate information insights into understandable business outcomes.

Businesses rely on data stored in online repositories or “cloud” storage systems. This course covers the history and theory of these cloud systems and the basic mechanics of interacting with them. The course will cover Amazon Web Services (AWS), Google Computing Services (GCP), and Microsoft Azure. Students will learn to be proficient in the basics of one or more of these systems. This course will cover the fundamental issues in data and AI ethics, including responsible AI and the implications of ethics on business and technical processes. The evolving privacy landscape will be reviewed, including US and EU legislation implications on data science processes.

This course will explore the impact of AI and digital platforms on the economy, business, and society, using macroeconomic theory and empirical evidence. Students will routinely use Large Language Models (LLMs) as a collaborator in this course. The course will also cover future implications of AI and digital platforms, including ethical, social, and regulatory aspects.

This course is structured around a real-world orientated project in the student’s target industry using the breadth of skills the students have gained over the program. Students will spend the semester working on a tangible advanced data science project partnering with a firm in a related industry. The course will assist students by linking the principles, theories, and previous learnings around data and analytics to utilization in the business. The system will build from the methods and techniques typically used in analytics and focus on how the analysis output is interpreted and presented in the business world. The course will offer the opportunity to focus and refine their interest in one of the following areas: entertainment analytics and real estate.

• Submit the online application
• Resume
• Statement of Intent
• Official transcripts from undergraduate degree granting institution
  • Send electronically to admtranscript@chapman.edu
  • Send via mail to:
    Chapman University
    Office of Graduate Admissions
    One University Drive
    Orange, CA 92866
• Official GMAT or GRE score (institution code 4047), waiver available
• Letter of recommendation
• Interview
• International applicants must also submit the following:
  • Official TOEFL, IELTS, Duolingo English Test (DET), PTE or Cambridge English Advanced Exam test score
  • Please see our FAQs for more information on international admission requirements

• Statistics or Introductory Business Statistics
• Calculus