OVERVIEW

This course provides a general introduction to the methods and tools of Business Analytics, with the goal of bringing students closer to data analysis in real-world contexts. Through an application-oriented approach, the course promotes an integrated view of data as a strategic resource for decision support in business, industry and management.

AIMS AND CONTENT

LEARNING OUTCOMES

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 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

All slides used during the lectures and other teaching materials will be available on Aulaweb. In general, the notes taken during the lectures and the materials provided on Aulaweb are sufficient for exam preparation.

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

LESSONS

LESSONS START

https://corsi.unige.it/en/corsi/11956/studenti-orario

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.

ASSESSMENT METHODS

The oral exam will focus on the discussion of the case study.

FURTHER INFORMATION

Please contact the professors for further information not included in the course description.