ECONOMIA E IMPRESAData ScienceAnno accademico 2023/2024

9793926 - DATA BASE AND BIG DATA ANALYTICS
Modulo BIG DATA ANALYTICS

Docente: Orazio TOMARCHIO

Risultati di apprendimento attesi

This module covers the fundamental concepts of management and design of a business intelligence system. Topics include data models for building a data warehouse; ETL (extract, transform and load) functionalities; OLAP analysis; basic data mining; reporting and interactive dashboards, evolution of BI architectures on large datasets. The module covers techniques and algorithms for data visualization and exploratory analysis based on principles and techniques from graphic design, perceptual psychology and cognitive science. It is targeted to using visualization in their data analytics work. The learning objectives are as follows:

Knowledge and understanding

Applying knowledge and understanding

Making judgements

Communication skills

Learning skills

Modalità di svolgimento dell'insegnamento

The main teaching methods are as follows:

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Prerequisiti richiesti

Frequenza lezioni

Strongly recommended. Attending and actively participating in the classroom activities will contribute positively towards the overall assessment of the oral exam.

Contenuti del corso

1. Introduction to Business Intelligence and Big Data Analytics 

2. Data models for data warehouse 

3. BI Architecture 

4. Data Visualization 

Testi di riferimento

  1. [GoRi] Golfarelli, Rizzi. Data Warehouse Design: Modern Principles and Methodologies, McGraw Hill
  2. [Dash] Steve Wexler, Jeffrey Shaffer, Andy Cotgreave. The Big Book Dashboards: Visualizing Your Data Using Real-World Business Scenarios. Wiley (2017)
  3. [Few1] Stephen Few. Show Me the Numbers: Designing Tables and Graphs to Enlighten, 2nd edition, Analytics Press (2012)
  4. [Few2] Stephen Few. Information Dashboard Design: Displaying Data for At-a-Glance Monitoring, 2nd edition, O’Reilly Media (2013)
  5. [Notes] Instructor’s notes (published on Studium and/or the Microsoft Teams platform)

Programmazione del corso

 ArgomentiRiferimenti testi
1Introduction to Big Data Analytics.[Notes]
2Business intelligence: introduction, fundamental concepts and architectures[Notes][GoRi] Chap. 1 
3The structure and evolution of BI and Big Data analytics systems[Notes] 
4Data models for data warehouse: conceptual modeling and design[GoRi] Chap. 2-6 
5Multi-dimensional data model[GoRi] Chap. 5 
6Data models for data warehouse: logical modeling and design[GoRi] Chap. 8-9
7ETL (extract, transform and load) process[GoRi] Chap. 10[Notes] 
8OLAP analysis and query[GoRi] Chap. 7[Notes] 
9Introduction to Data Visualization. Visual Perception and Preattentive Attributes[Dash] Chap. 1[Few2] Chap. 4 
10Charts and standard views: relevance, appropriateness and best practices[Few1] 
11Use of colors in data visualization[Dash] Chap. 1
12Advanced and innovative tools for data visualization: the Tableau platform[Notes] 
13Dashboard design principles. Exploratory vs. Explanatory dashboards.[Few2] 
14Data visualization: infographics and storytelling[Few2] 

Verifica dell'apprendimento

Modalità di verifica dell'apprendimento

The final exam consists of

  1. a project work aiming at assessing the capabilities in developing a BI system including the analysis and the visualization of relevant information,
  2. an oral exam that will consist of the discussion of the project work.

Assessment criteria include: depth of analysis, adequacy, quality and correctness of the proposed solutions to the project work, ability to justify and critically evaluate the adopted solutions, clarity.

The vote on the Big Data Analytics module will account for 50% of the total grade for the entire course.

Learning assessment may also be carried out on line, should the conditions require it.

Esempi di domande e/o esercizi frequenti

Examples of questions and exercises are available on the Studium platform and/or the Microsoft Teams platform

English version