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BUSINESS ANALYTICS PROJECT

CODE 101801
ACADEMIC YEAR 2021/2022
CREDITS 6 credits during the 1st year of 10852 COMPUTER SCIENCE (LM-18) GENOVA
SCIENTIFIC DISCIPLINARY SECTOR INF/01
LANGUAGE English
TEACHING LOCATION GENOVA (COMPUTER SCIENCE )
SEMESTER 2° Semester
TEACHING MATERIALS AULAWEB

OVERVIEW

The course provides a hands-on approach to predictive analytics. In.a few interactive lectures we learn the basic steps of any predictive analytics project: 1 understanding the problem, 2 identifying the machine learning tools to be used, 3 reviewing pros and cons of the available techniques, 4. looking for an appropriate data set, 5. running the algos and commenting the obtained results. Each student is then encouraged to find a project of suitable size and work on it. 

AIMS AND CONTENT

LEARNING OUTCOMES

Learning the key elements of conceptual and notational tools for business modelling and the ability of approaching data mining as a process - including the business understanding, data understanding, exploratory data analysis, modeling, evaluation, and deployment phases -, and of employing a wide range of mining techniques for data analysis.

PREREQUISITES

A firm grasp of the machine learning basics

RECOMMENDED READING/BIBLIOGRAPHY

Predictive Analytics (Eric Siegel, Wiley 2016)

TEACHERS AND EXAM BOARD

Exam Board

ALESSANDRO VERRI (President)

EXAMS

EXAM DESCRIPTION

Project discussion

Exam schedule

Date Time Location Type Notes
26/01/2022 09:00 GENOVA Esame su appuntamento Esame su appuntamento, contattare i docenti
27/06/2022 09:00 GENOVA Esame su appuntamento Esame su appuntamento, contattare i docenti
14/09/2022 09:00 GENOVA Esame su appuntamento Esame su appuntamento, contattare i docenti
25/01/2023 09:00 GENOVA Esame su appuntamento Esame su appuntamento, contattare i docenti