Data Analysis and Visualization (18934)

To solve business problems using data analysis, statistical models and other quantitative methods.

1. Introduction and Predictive Analytics. a. A process model for Data Mining ¿ CRISP-DM. b. Predictive Modeling Tools. 2. Advanced Data Visualization and Data Wrangling. 3. Classification methods in Business Analytics. 4. Text Mining for Business Analytics. 5. Real Case Studies: market basket analysis, Response Modeling in Direct Marketing, Predicting Bank Loan Default, etc.

Theory (10 hours) Practices (5 hours) Office Hours (5 hours) Group Work and Individual Work TEACHING METHODOLOGIES In-class lectures by the teacher with the support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students' learning. - Resolution of practical cases, problems, etc., posed by the teacher individually or in groups. - Preparation of individual work and reports. USE OF ARTIFICIAL INTELLIGENCE - The use of AI tools such as Chat GPT is allowed in the programming area. Chat GPT works in this case as an advanced content search engine, so that it facilitates and accelerates the process of becoming familiar with the course programming tools.