- Business Intelligence
- Summary
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.
** fino alla fine dell'emergenza sanitaria COVID-19, le lezioni si terranno in modalità mista: parziale presenza (se i numeri lo consentiranno) e lezioni in modalità sincrona. Tutte le lezioni saranno registrate e rese disponibili online in piattaforma**
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
-- una prova orale obbligatoria
-- un homework di gruppo
Orario di ricevimento
su Appuntamento
Aims
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.
** Due to the COVID-19 emergency, all the lessons will be provided in a mixed modality: partially on-site (if possible) and in a synchronous way, that is all lessons will be recorded and made available on the website **
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 or through academic license
Semester
I semester
Assessment method
-- an oral examination
-- a homework
Office hours
By Appointment