LEARNING OUTCOMES

The course introduces the basic concepts of Business Intelligence (BI) with particular reference to aspects of Analytics/Data Mining and focusing on the use analytical methods and reporting to support business decisions. The students will acquire both the basic skills for the design of a BI system and the ability to critically evaluate the data analysis performed with Data Mining tools. During the course some seminars are planned that illustrate real cases of application of the BI in the enterprise.

TEACHING METHODS

Lectures and computer assisted lab sessions.

SYLLABUS/CONTENT

Introduction to Business Intelligence (BI): problems methods and tools.

Components of a BI system: ETL, Data Mart and Data Warehouses, On Line Analytical Processing (OLAP), Reports and Dashboards, Analytics and Data Mining.

Analysis and definition of Key Performance Indicators (KPIs).

Analytics: Basic concepts of statistical inference, Exploratory Data Analysis (EDA).

Tools and Techniques of Data Mining: decision trees and association rules, Naive Bayes, linear methods for classification and regression, Artificial Neural Networks, Support Vector Machine and kernel methods; Clustering methods; Quality Assessment of Data Mining techniques .

Business case studies.

RECOMMENDED READING/BIBLIOGRAPHY

Lecture notes provided during the course.

Further readings:

•          C.Vercellis, Business Intelligence: modelli matematici e sistemi per le decisioni, McGraw-Hill, 2006.

•          P.Giudici, Data Mining: metodi informatici, statistici e applicazioni, McGraw-Hill, 2003.

•          J.Han, M.Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2006 (2nd Ed.).