The world most valuable resource is data, not oil. Hence, it is not surprising that organizations increasingly look to have in their rank’s experts in the sciences of data who are able to identify the correct sources of data, raise relevant business-oriented questions, and extract useful knowledge and insights for the organization. In that sense, the Data Science for Hospitality & Tourism Course unit explores different techniques to perform a descriptive analysis of data using python, as well as an understanding of how data science benefits from Big Data and the different storing technologies.

Classes will involve a mix of lectures and practical exercises. Moreover, the course will have a strong active learning component, as such students are expected to actively participate in the class and read the recommended materials prior to each class. A short introduction to Python will be delivered in the first weeks of the course to enable students to explore and practice many of the theoretical concepts taught in the classes on their own.

Bibliography

• VanderPlas, Jake. Python data science handbook: essential tools for working with data. “ O’Reilly Media, Inc.”, 2016
• McKinney, Wes. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. “ O’Reilly Media, Inc.”, 2012
• Grus, Joel. Data science from scratch: first principles with python. “ O’Reilly Media, Inc.”, 2015