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.

PREREQUISITES

Basic knowledge of probability, statistics, analysis and data representation.
Basic knowledge of Python or a similar programming language.

TEACHING METHODS

Lectures and computer assisted lab sessions.

SYLLABUS/CONTENT

Review of multivariate statistics and elements of decision theory
Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics
Supervised and unsupervised models
Association Pattern Mining
Cluster Analysis
Rule-based methods and decision trees
Kernel-based methods
Elemsnts of neural networks
Elements of methods for structured and semi-structured data
Mehods for model evaluation
Applications and case studies

RECOMMENDED READING/BIBLIOGRAPHY

Lecture notes provided during the course.

Further readings:

C.C.Aggarwal, Data mining: the textbook. Springer, 2015.

M.J.Zaki, M.Wagner Jr., Data Mining and Machine Learning: Fundamental Concepts and Algorithms. Cambridge University Press, 2019.

T.Hastie, R.Tibshirani, J.Friedman, The Elemsnts of Statistical Learning, Springer, 2009 (2nd Ed.)