Course Syllabus
Obiettivi
Il corso intende fornire gli strumenti (metodologici e tecnici) per la comprensione e la realizzazione di soluzioni di BI - incluso il ciclo di vita del dato KDD - in contesti applicativi reali, individuando e definendo i criteri per la valutazione dei processi realizzati
Contenuti sintetici
Introduction to BI and Big Data Analytics
BI Architectures
Knowledge Discovery in Databases – KDD
Programma esteso
1. Introduction to BI and Big Data Analytics
a. Goal and rationale of BI systems
b. The value of knowledge – digital economy and data driven decision making
c. The Structure and subsequent evolution of BI and Big Data Analytics systems
2. BI Architectures
a. The Evolution of BI Architectures (towards Big Data)
b. Decision Models on the basis of business functions
c. Definition, selection and metrics for computing directional indicators (KPI – CSF)
3. Knowledge Discovery in Databases – KDD
a. Phases, methodologies and the value for business purposes (Data as value)
b. Models for data quality evaluation – structured data vs (unstructured) Big data
c. Models for data management and analytics – relational vs schema free (i.e., graph db)
d. Models and techniques for data analysis – how to use data for fact-based decision making
e. Visualisation models for decision making – selecting the proper model for each stakeholder – data story telling and indicators
Prerequisiti
Nessuno
Modalità didattica
Lezioni frontali, seminari monotematici, esercitazioni, assegnamenti da svolgere a casa.
Materiale didattico
Lezioni con l'ausilio di slide, laboratorio e casi applicativi. Articoli scientifici di riferimento saranno forniti dal docente. Il Software utilizzato sarà open-source
Periodo di erogazione dell'insegnamento
I semestre
Modalità di verifica del profitto e valutazione
La verifica sarà composta da:
-- una prova scritta obbligatoria, volta a valutare le competenze dello studente in termini di (i) concetti e metodologie acquisite (ii) capacità nello scrivere/leggere codice e (iii) capacità nel sintetizzare fattori distintivi e critici delle tecnologie introdotti
-- un homework di gruppo facoltativa, volto a valutare le competenze dello studente in termini di (i) lavoro di gruppo; (ii) comprensione dei dati e definizione di un approccio risolutivo, (iii) discutere le soluzioni identificate e realizzate all'utente finale
Orario di ricevimento
su Appuntamento
Aims
The course would provide bot methodological an technical aspects needed to understand and realise BI solutions in real-life contexts, including the whole data lifecycle (KDD) and identifying criteria for the evaluation of the solution provided.
Contents
Introduction to BI and Big Data Analytics
BI Architectures
Knowledge Discovery in Databases – KDD
Detailed program
1. Introduction to BI and Big Data Analytics
a. Goal and rationale of BI systems
b. The value of knowledge – digital economy and data driven decision making
c. The Structure and subsequent evolution of BI and Big Data Analytics systems
2. BI Architectures
a. The Evolution of BI Architectures (towards Big Data)
b. Decision Models on the basis of business functions
c. Definition, selection and metrics for computing directional indicators (KPI – CSF)
3. Knowledge Discovery in Databases – KDD
a. Phases, methodologies and the value for business purposes (Data as value)
b. Models for data quality evaluation – structured data vs (unstructured) Big data
c. Models for data management and analytics – relational vs schema free (i.e., graph db)
d. Models and techniques for data analysis – how to use data for fact-based decision making
e. Visualisation models for decision making – selecting the proper model for each stakeholder – data story telling and indicators
Prerequisites
None
Teaching form
The course will be provided by means of lessons, seminars, and laboratory sessions and homework.
Textbook and teaching resource
Lectures with the support of slides, laboratory and real-life case studies. Scientific Papers and books indicated by the lecturer. The software used is either available as open-source
Semester
I semester
Assessment method
All exams will be performed online composed by:
-- a written examination (mandatory), aimed at assessing the competencies of the student in terms of (i) concepts and methodologies acquired (ii) abilities in writing/reading code and (iii) abilities in summarising pros/cons of the techniques introduced
-- an homework (optional), aimed at evaluating the competencies of the student in terms of (i) teamwork, (ii) understanding the data and define a way to approach the problem, (iii) discussing the solution identified and realised to the final user
Office hours
By Appointment
Staff
-
Lorenzo Malandri
-
Fabio Mercorio