Salta al contenuto principale della pagina

BUSINESS ANALYTICS

CODE 60270
ACADEMIC YEAR 2022/2023
CREDITS
  • 6 cfu during the 2nd year of 8734 INGEGNERIA GESTIONALE (LM-31) - GENOVA
  • 6 cfu during the 1st year of 8734 INGEGNERIA GESTIONALE (LM-31) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR ING-INF/05
    LANGUAGE Italian (English on demand)
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 2° Semester
    TEACHING MATERIALS AULAWEB

    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.

    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

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    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

    Date Time Location Type Notes
    19/12/2022 09:30 GENOVA Orale
    10/01/2023 09:30 GENOVA Orale
    25/01/2023 09:30 GENOVA Orale
    13/02/2023 09:30 GENOVA Orale
    08/06/2023 09:30 GENOVA Orale
    05/07/2023 09:30 GENOVA Orale
    13/09/2023 09:30 GENOVA Orale