Educational guide

IDENTIFYING DATA

2024_25

Subject

DATA ANALYTICS ON THE WEB AND SOCIAL NETWORKS

Code

00717026

Study programme

0717 - GRADO INGENIERÍA DATOS INTELIGENCIA ARTIFICIAL

Descriptors

Credit.

Type

Year

Period

Compulsory

Third

Second

Language

Castellano

Prerequisites

Department

ING.ELECTR.DE SIST. Y AUTOMATI

Coordinador

BENITEZ ANDRADES , JOSE ALBERTO

E-mail

jbena@unileon.es
igarr@unileon.es

Lecturers

GARCÍA RODRÍGUEZ , ISAÍAS

BENITEZ ANDRADES , JOSE ALBERTO

Web

http://agora.unileon.es

General description

The student will gain knowledge about the services that build up the Internet network, and will be able to configure and manage different kinds of servers implementing these services. Different architectures and paradigms for service providing will also be studied, including those ones related to mobile services

Tribunales de Revisión

Tribunal titular
Cargo	Departamento	Profesor
Presidente	ING.ELECTR.DE SIST. Y AUTOMATI	FUERTES MARTINEZ , JUAN JOSE
Secretario	ING.ELECTR.DE SIST. Y AUTOMATI	PEREZ LOPEZ , DANIEL
Vocal	ING.ELECTR.DE SIST. Y AUTOMATI	PRADA MEDRANO , MIGUEL ANGEL

Tribunal suplente
Cargo	Departamento	Profesor
Presidente	ING.ELECTR.DE SIST. Y AUTOMATI	ALONSO CASTRO , SERAFIN
Secretario	ING.ELECTR.DE SIST. Y AUTOMATI	REGUERA ACEVEDO , PERFECTO
Vocal	ING.ELECTR.DE SIST. Y AUTOMATI	RIESCO PELAEZ , FELIX

Competencias

Code
A18980
B5800
B5802
B5806
B5808
C1	CMECES1 That students have demonstrated possession and understanding of knowledge in an area of study that is based on general secondary education, and is usually found at a level that, although supported by advanced textbooks, also includes some aspects that involve knowledge from the cutting edge of their field of study
C2	CMECES2 That students know how to apply their knowledge to their work or vocation in a professional manner and possess the skills that are usually demonstrated through the development and defense of arguments and the resolution of problems within their area of study.

Learning aims

	Competences
Applies techniques for extracting information from a website or social network	A18980	B5802 B5806 B5808	C1 C2
Knows tools for social network analysis and website analysis.	A18980	B5800 B5802 B5806 B5808	C1 C2
Develops a social network analysis.		B5800 B5802 B5806 B5808	C1 C2
Proposes the visualisation of the results to facilitate their comprehension.		B5800 B5806 B5808	C1 C2
Contrast the differences between a dynamic analysis and a static analysis of a social network.	A18980	B5800 B5802 B5806 B5808	C1 C2

Contents

Topic	Sub-topic
Topic I: Techniques and methods for accessing and extracting information on the Web and social networks	Topic 1. Web Scraping Topic 2. APIs for data mining Topic 3. Data Mining Strategies
Topic II: Metrics, analysis and visualisation of data on the Web	Topic 1. Sentiment Analysis Topic 2. Data Visualisation Topic 3. Platform Metrics
Topic III: Techniques for processing and analysing data in social networks.	Topic 1. Natural language processing techniques. Topic 2. Social Networks and Network Analysis Topic 3. Topic modelling.

Planning

Methodologies :: Tests
	Class hours	Hours outside the classroom	Total hours
Personal tuition	2	0	2

Laboratory practicals	20	26	46
Problem solving, classroom exercises	4	4	8
Assignments	11.5	11.5	23

Lecture	26	39	65

Mixed tests	6	0	6

(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies

Methodologies ::

	Description
Personal tuition	Time reserved for attending to and resolving students' doubts.
Laboratory practicals	Practical realisation of the contents dealt with in the lecture sessions.
Problem solving, classroom exercises	Formulation, analysis and resolution of exercises.
Assignments	Development of an assignment on a subject related to the course and proposed by the lecturer.
Lecture	Presentation of the contents of the subject.

Personalized attention

Personal tuition

Description

Individual or group doubt solving and face-to-face tutoring. Tutorials will be arranged in advance with the teacher.

Assessment

	Description	Qualification
Assignments	The quality of the report and the presentation of the work, if any, will be assessed. The completion of the work is compulsory to pass the course.	30%
Laboratory practicals	The correct completion of the practical work in the laboratory will be assessed.	20%
Mixed tests	The contents corresponding to the lectures, laboratory practicals and problem solving in the classroom will be assessed by means of several mixed tests (short questions, multiple choice, development, etc.) that will be distributed throughout the course.	50%
Others	In order to pass by continuous assessment, a minimum of 4 out of 10 must be obtained in each test. Inadequate attitude in the classroom, laboratory or evaluation tests will be penalised with 20% of the final grade.

Other comments and second call

Sources of information

Access to Recommended Bibliography in the Catalog ULE

Basic	Matthew A. Russell y Mikhail Klassen , Mining the Social Web , O'Reilly Media , 2018 (3ª edición) Panos Alexopoulos , Semantic Modeling for Data , O'Reilly Media , 2020 Dean Allemang y James Hendler , Semantic Web for the Working Ontologist , Morgan Kaufmann, 2020 (3ª edición)

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