GRAPH MINING

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

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General Module

Course Sheet

Course code:

ECBD012L

Academic Year of enrolment:

2020

Year of supply:

2021/2022

Type of Course:

Related/Supplementary

Degree:

Course of 2nd Cycle Degree in ECONOMICS AND BUSINESS ANALYTICS

Disciplinary Sector:

Computer Science

Is part of:

BIG DATA

Location:

PESCARA

Credits:

Programme Year:

Professor and Collaborators:

MOSCARDELLI Luca

Cycle:

First semester

Hours of classroom activity:

Individual study hours:

102

Prerequisites:

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

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.

Contents

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

Extended Syllabus

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)

Recommended Bibliography

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/

Teaching Methods

Lectures.
Practice and exercises in the computer lab.

Evaluation methods

Verification of learning:

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.

Mutuations

COMPUTER LANGUAGES FOR STATISTICS

Course degree in: ECONOMICS AND COMMERCE

Training in:

Other courses

Course year: 1

DATA SCIENCE IN ECONOMICS
9 Credits - Characterising
ECONOMICS OF DIGITAL MARKETS
9 Credits - Characterising
ECONOMICS OF GLOBAL MARKETS
9 Credits - Characterising
ECONOMY OF INNOVATION
6 Credits - Characterising
MICROECONOMICS FOR DIGITAL MARKETS
6 Credits - Characterising
STRATEGIC MANAGEMENT
9 Credits - Characterising
CYBERSECURITY
6 Credits - Related/Supplementary
IT LAW
6 Credits - Related/Supplementary
ELECTIVE COURSE
9 Credits - Elective Course

Course year: 2

BIG DATA
9 Credits
BUSINESS INFORMATION SYSTEMS
6 Credits - Characterising
DATA SCIENCE IN ECONOMICS LABORATORY
6 Credits - Characterising
DEEP NEURAL NETWORKS
6 Credits - Characterising
ECONOMIC INNOVATION LAW
6 Credits - Characterising
STATISTICAL LEARNING
6 Credits - Characterising
WEB MARKETING
6 Credits - Characterising
FINAL EXAMINATION
20 Credits - Language/Final Examination
INTERNSHIP
7 Credits - For stages and internships

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GRAPH MINING