Summer School of Mathematics for Economic and Social Sciences
Data Mining and Machine Learning for Business and Organizations
11 September 2017 - 15 September 2017Planned Activities
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