Course Syllabus
Obiettivi
At the end of the module students will be able to select, design and query a database (relational or not) according to their application needs
Students will be able to use a NoSql database management system to acquire, memorize and query semi structured data
At the same time students will have competence related to analysis, evaluation and design of complex interactive infographics
Contenuti sintetici
Introduction to data management in big data context
data lifecycle
Variety: nosql models and architecture
Volume: data distribution and replication, hadoop architecture
Velocity: data architecture for capturing and elaborating near real time data
The data visualization module covers the essentials of visual design by which to design, and evaluate systems that enable the interactive analysis of data and the flexible optimization of reporting (both in an organizational domain and in data journalism).
Programma esteso
-
Introduction to big data (variety, volume and velocity )
-
Data life cycle
-
Variety
-
Introduction to NoSQL models
-
Cap Theorem
-
key value and columnar models
-
Document based system
-
Graph db
-
Data integration
-
Data quality
-
Volume
-
Data distribution
-
Replication
-
hadoop architecture
-
Data lake
-
Velocity
-
Lambda and Kappa architecture
-
ELK architecture
Data visualization
- Introduction to the Human Data Interaction (Definitions, main concepts and methodologies)
- Data Transformation into sources of knowledge through visual representation.
- Requirements and heuristics for high-quality visualizations: dos and donts.
- Charts and standard views: relevance and appropriateness.
- Advanced and innovative tools for data visualization and advanced quantitative analysis.
- The evaluation of the quality of visualizations and infographics.
o Qualitative assessment: expert and heuristic;
o Quantitative assessment: user tasks; inferential statistical techniques.
o Validated psychometric questionnaires and their analysis and understanding.
- Elements of visual semiotics and social semiotics.
Prerequisiti
knowledge of relational model
Modalità didattica
Lectures and exercises in the classroom and on virtual lab
Lectures with the support of slideware, discussion of practical cases through the forum, discussion of practical home-work projects.
Some self-assessment tests, not considered for the final evaluation will be provided
Materiale didattico
G. Harrison Next Generation Databases, Apress, 2015
A. Rezzani Big data analytics Apogeo 2017
Yau, N. (2011). Visualize this: the FlowingData guide to design, visualization, and statistics. John Wiley & Sons.
Ware, C. (2012). Information visualization: perception for design. Elsevier.
Scientific articles and class pack provided by the lecturers.
Periodo di erogazione dell'insegnamento
first semester
Modalità di verifica del profitto e valutazione
The exam is divided into two parts
Data Management (50% of the final evaluation): Written exam and a project related to the topic of the module
Data visualization(50% of the final evaluation): test and a project related to the topic of the module
Orario di ricevimento
Please send an e-mail to teachers to arrange an appointment
Sustainable Development Goals
Aims
At the end of the module students will be able to select, design and query a database (relational or not) according to their application needs
Students will be able to use a NoSql database management system to acquire, memorize and query semi structured data
At the end of the course students will have acquired skills in analysis, evaluation and, to a lesser extent, development of complex and interactive infographics.
Contents
Introduction to data management in big data context
data lifecycle
Variety: nosql models and architecture
Volume: data distribution and replication, hadoop architecture
Velocity: data architecture for capturing and elaborating near real time data
Detailed program
-
Introduction to big data (variety, volume and velocity )
-
Data life cycle
-
Variety
-
Introduction to NoSQL models
-
Cap Theorem
-
key value and columnar models
-
Document based system
-
Graph db
-
Data integration
-
Data quality
-
Volume
-
Data distribution
-
Replication
-
hadoop architecture
-
Data lake
-
Velocity
-
Lambda and Kappa architecture
-
ELK architecture
Data visualization
- Introduction to the Human Data Interaction (Definitions, main concepts and methodologies)
- Data Transformation into sources of knowledge through visual representation.
- Requirements and heuristics for high-quality visualizations: dos and donts.
- Charts and standard views: relevance and appropriateness.
- Advanced and innovative tools for data visualization and advanced quantitative analysis.
- The evaluation of the quality of visualizations and infographics.
o Qualitative assessment: expert and heuristic;
o Quantitative assessment: user tasks; inferential statistical techniques.
o Validated psychometric questionnaires and their analysis and understanding.
- Elements of visual semiotics and social semiotics.
Prerequisites
knowledge of relational model
Teaching form
Lectures and exercises in the classroom and on virtual lab
Lectures with the support of slideware, discussion of practical cases through the forum, discussion of practical home-work projects.
Someelf-assessment tests, not considered for the final evaluation will be provided
Textbook and teaching resource
G. Harrison Next Generation Databases, Apress, 2015
A. Rezzani Big data analytics Apogeo 2017
Yau, N. (2011). Visualize this: the FlowingData guide to design, visualization, and statistics. John Wiley & Sons.
Ware, C. (2012). Information visualization: perception for design. Elsevier.
Scientific articles and class pack provided by the lecturers.
Semester
first semester
Assessment method
The exam is divided into two parts
Data Management (50% of the final evaluation): Written exam and a project related to the topic of the module
Data visualization(50% of the final evaluation): test and a project related to the topic of the module
Office hours
Please send an e-mail to teachers to arrange an appointment
