OVERVIEW

The course illustrates the basic concepts of Business Analytics with particular reference to the approaches for statistical data modeling, diagnostic and predictive analytics, using methodologies based on machine learning for the solution of application problems and decision support in industrial, management and economics fields.

AIMS AND CONTENT

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.

AIMS AND LEARNING OUTCOMES

The student will acquire design skills of data analysis in industrial and management application fields. In particular, the student will be able to design a predictive analysis system and evaluate its performance.

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.)

TEACHERS AND EXAM BOARD

Exam Board

DAVIDE ANGUITA (President)

LUCA DEMETRIO

LUCA ONETO

ANTONIO EMANUELE CINA' (President Substitute)

LESSONS

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Oral examination. The student will develop autonomously (individually or in cooperation with other students) a case study, selected among those proposed as exam topics and using the methods discussed during the course. The oral examination will focus on the discussion of the case study.

Exam schedule