Summer School of Mathematics for Economic and Social Sciences

Data Mining and Machine Learning for Business and Organizations

11 September 2017 - 15 September 2017

Syllabus

Clustering models for customer segmentation. Discussion of real cases. Hands-on project: segmentation of a base of anonymized customers from the retail industry. Clustering models for competitive intelligence.
Patterns and association rule mining for market basket analysis. Hands-on project: mining association rules from sales data of the retail industry.
Prediction models for promotion performance and churn analysis. Discussion of real cases. Hands-on project: churn prediction from a base of anonymized customers from the retail industry.
Analysis of human mobility patterns by mobility data mining from big data. Mining official data for understanding of human behavior.
Social network analysis for undestanding diffusion phenomena. Viral marketing. Application of data mining to geo-marketing. Analysis of innovators. Predictive models for fraud detection.

Textbooks

Gordon S. Linoff e Michael J. Berry. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley, 2011.
Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Addison Wesley, ISBN 0-321-32136-7, 2006
http://www-users.cs.umn.edu/~kumar/dmbook/index.php

Reading about the "data analyst" job