Salta al contenuto principale della pagina

BUSINESS ANALYTICS PROJECT

CODE 101801
ACADEMIC YEAR 2021/2022
CREDITS
  • 6 cfu during the 1st year of 10852 COMPUTER SCIENCE (LM-18) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR INF/01
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • 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