20538 - PREDICTIVE ANALYTICS FOR DATA DRIVEN DECISIONS MAKING
CLMG - M - IM - MM - AFC - CLEFIN-FINANCE - CLELI - ACME - DES-ESS - EMIT - GIO
Course taught in English
Go to class group/s: 31
The goal of the course is to improve the student's skills to manage and to take advantages of the huge availability of data nowadays produced by a great variety of sources. The contents of the course covers both technical aspects of data analytics and more interpretation related topics (how to translate the analytical outputs into meaningful business insights).
- Main characteristics of Big Data
- Introduction to Hadoop platform
- Models and statistical techniques applied to Big Data:
- Linear and logistic regression
- Regression and classification trees
- Factor analysis
- Cluster analysis
- Time series analysis
- Social networks analysis
- Machine learning alghorithm and neural networks
- Applications and real cases using open-source software (KNIME and R) in the following areas: internet of things, social & web content analysis, customer relationship management, fraud detection and operations.
Not attending students:
individual final exam in the computer lab (100% weight).
it will be based both on a group assignment (to be developed during the course and submitted before the end of the lessons; 40% of the final grade) and on an individual final exam in the computer lab (60% of the final grade), proposed in a reduced version compared to the full not-attending exam.
- M. Kuhn, K. Johnson, Applied predictive modeling, Springer, 2013.