CLEBA

Language:

Italian
Textbooks:

Course slides.

Further readings:
Leskovec, Jure, Anand Rajaraman, and Jeffrey David Ullman.
Mining of massive datasets.
Cambridge University Press, 2014.
Online available for free: http://www.mmds.org/
Learning objectives:

The learning objectives can be associated with the following expected learning outcomes:
Knowledge and understanding
The course aims to provide the basic methodological and application knowledge of the main tools for the analysis of big amounts of data

Applying knowledge and understanding
At the end of the course, also because of the case studies addressed during the lab classes, the student will also be able to extract and manipulate data from the web, from files and from databases, including those of big size.
Prerequisite:

The knowledge of Basic Statistics is requested. Moreover, the knowledge of basic programming is suggested.
Teaching methods:

Lectures.
Practice and exercises in the computer lab.
Exam type:

Knowledge and understanding
The verification of the learning outcomes will be carried out through a written and oral examination (the latter being optional or potentially required by the teacher). The score of the exam is assigned by a mark expressed in 30ths and is based on both the written and oral examinations.
Applying knowledge and understanding
During the exam, students' ability to apply the knowledge given in the course is verified. In particular, student should be able to extract and manipulate data from the web, from files and from databases, including those of big size.
Sostenibilità:
Further information:

- Introduction to the Big Data phenomenon
- Big data methods
- Data mining
- Lab & tools

Introduction
• Introduction to the Big Data
Big data methods
• Programming frameworks: MapReduce/Hadoop, Spark
Data mining
• Association Analysis
• Clustering
• Graph Analytics (centrality measures, scale-free/Power-law graphs, small world phenomenon, uncertain graphs)
• Similarity and diversity search
Lab & tools
• tools and methodologies for collecting, processing, visualizing and analyzing large amounts of data (Big Data).
o extract unstructured data from web (import.io, kimono, etc.)
o explore and present static data (RAWGraphs, Gephi, illustrator, etc.)
o explore and build interactive data visualizations (Tableau Public, Carto)

Course data

Academic year:

2024/2025
Curriculum:
CFU:

6
Class hours:

48
Study hours:

102
Area:

INF/01 - INFORMATICA
Classification:

C - Affine/Integrativa
Evaluation:

Giudizio Finale
Professors:

Luca Moscardelli
Year:
Semester:

First
Joint courses:
Joint courses:

Computer languages for statistics (CLEC/M, economia e statistica, mutuo)
Modulo o canale di:

Big data (CLEBA)
Modules and Partitions: