In this course, students will learn decision making in different service industries including education, healthcare, retail, and car sharing using analytics and modern computer languages (Python).

Students will learn how to design experiments effectively and apply appropriate hypothesis testing to confirm statistically significant variables, how to handle imbalanced data sets and develop appropriate predictive models to optimize planning, how to implement text mining and natural language processing to predict sentiment and emotion, and how to use simulations analysis to estimate waiting time in support of their management.

This content will be introduced to students with cases and data sets that permit students to experience the different tools and improve their confidence in using analytical tools appropriately in the context of real-world problems in the service industry.