Salta al contenuto principale della pagina

SOFTWARE ENGINEERING FOR DATA ANALYTICS

CODE 90532
ACADEMIC YEAR 2022/2023
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

    Recently (big) data, analytics, artificial intelligence, Internet of Thing pose new problems and open new perspectives in the field of the development of software systems. The teaching aims to illustrate these changements and to provide approaches/techniques/methods to support the development of these systems.

    The students will be encouraged to complement the presented material by independently deepen some topics, in the spirit of lifelong learning.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    The students will become aware of the new problems posed by the development of systems based on data and analytics, and will learn some approaches/techinques/tools to   support the development of such systems.

    AIMS AND LEARNING OUTCOMES

    Knowledge of the specific problems posed by the devlopment of systems based on (big) data, analytics, artificial intelligence, Internet of Thing (shortly smart systems).
    Competence for capturing and specifying the requirements of  smart systems.
    Comptence for using  the models to support the activities related with the development of smart systems.
    Knowledge of  state of the art approches for supporting specific sactivities related with the devolpment of smart systems.
     

    PREREQUISITES

    Basic knowledge of  software engineering.

    TEACHING METHODS

    Lessons and lab activities

    SYLLABUS/CONTENT

    Presentation of the problems posed by the development of systems based on (big) data, analytics, artificial intelligence, Internet of Thing (shortly smart systems).
    A method based on goals and UML for the capture and specification of the requirements of smart systems.
    Business process modelling
    DataOps/AnalyticsOps
    Declarative data analytics
    Model-driven approaches for data quality assurance.

    RECOMMENDED READING/BIBLIOGRAPHY

    material provided by the teacher

    TEACHERS AND EXAM BOARD

    Exam Board

    GIANNA REGGIO (President)

    MAURIZIO LEOTTA

    MAURA CERIOLI (President Substitute)

    FILIPPO RICCA (President Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    he exam consists of three parts:

    - application of an approach for the development of smart systems to a case study by a group of students

    - discussion of the results of the part above

    - oral presentation of an  approach for the development of smart systems

    ASSESSMENT METHODS

    The quality of the results of the project will allow to evaluate the students' comprehension  of the followed approach.

    The discussion of the project results will allow to determine the contributions of the single students.

    The oral presentation will allow to evaluate the students' communication and self-learning capabilities.

    Exam schedule

    Date Time Location Type Notes
    12/06/2023 09:00 GENOVA Esame su appuntamento
    11/09/2023 09:00 GENOVA Esame su appuntamento
    12/01/2024 09:00 GENOVA Esame su appuntamento