Educational guide
IDENTIFYING DATA							2024_25
Subject	CLOUD COMPUTING AND BIG DATA					Code	01744002
Study programme	1744 - MASTER UNIVERSITARIO EN INDUSTRIA 4.0
Descriptors	Credit.	Type	Year	Period
	3	Compulsory	First	First
Language	Castellano
Prerequisites
Department	ING.MECANICA,INFORMAT.AEROESP.
Coordinador	GUERRERO HIGUERAS , ANGEL MANUEL				E-mail	agueh@unileon.es mcong@unileon.es
Lecturers	CONDE GONZALEZ , MIGUEL ANGEL GUERRERO HIGUERAS , ANGEL MANUEL
Web	http://
General description
Tribunales de Revisión	Tribunal titular Cargo Departamento Profesor Presidente ING.MECANICA,INFORMAT.AEROESP. MATELLAN OLIVERA , VICENTE Secretario ING.MECANICA,INFORMAT.AEROESP. PANIZO ALONSO , LUIS Vocal ING.MECANICA,INFORMAT.AEROESP. SANCHEZ GONZALEZ , LIDIA Tribunal suplente Cargo Departamento Profesor Presidente ING.MECANICA,INFORMAT.AEROESP. FERNANDEZ LLAMAS , CAMINO Secretario ING.MECANICA,INFORMAT.AEROESP. RODRIGUEZ DE SOTO , ADOLFO Vocal ING.MECANICA,INFORMAT.AEROESP. CASTEJON LIMAS , MANUEL

Competencies

Type A	Code	Competences Specific
	A18631
	A18637
Type B	Code	Competences Transversal
	B5714
	B5715
	B5716
Type C	Code	Competences Nuclear
	C1
	C4

Learning aims

	Competences
Know the basics of Big Data and cloud computing: architectures, services and applications.	A18631	B5714 B5715	C1
Know the fundamentals of developing data capture, storage, intelligent analysis and visualization applications using Big Data tools.	A18631 A18637	B5715	C4
Know how to detect possible applications of cloud computing in the context of Industry 4.0.	A18631 A18637	B5716

Contents

Topic	Sub-topic
Block I. CLOUD COMPUTING INFRASTRUCTURES AND SERVICES	Topic 1. INTRODUCTION TO CLOUD COMPUTING Basic concepts about cloud computing, main risks and challenges. Topic 2. DATA CENTER Concept of data center and its main characteristics Topic 3. VIRTUALIZATION Concept of virtualization, virtualization models and virtualization in the cloud. Theme 4. CLOUD COMPUTING IN INDUSTRY Main applications of cloud computing in the industry, best solutions and main problems to be addressed.
Block II. CLOUD COMPUTING APPLICATIONS	Topic 1. INTERNET AS A SERVICE (IAAS) Description of the concept, how it can be deployed, main advantages and aspects to take into account. Topic 2. PLATFORMS AS A SERVICE (PAAS) Description of the concept, how it can be implemented, main advantages and aspects to take into account. Topic 3. CLOUD ARCHITECTURE AND USE OF CONTAINERS Cloud architecture and use of containers Approaches to cloud architectures and their implementation.
Block III. BIG DATA AND DATA SCIENCE	Topic 1. BIG DATA Description of the concept of Big Data, what it implies, its characteristics and how it is possible to exploit the information. Topic 2. DATA SCIENCE Description of the Data Science concept, what it implies, possible applications and main tools. Topic 3. NON-SQL DATA BASES Concept, advantages, main tools for accessing, querying and presenting information. Topic 4. BIGDATA AND INDUSTRY 4.0 Applications and considerations for the application of BIGDATA in the context of Industry 4.0.
Block IV. DATA CAPTURE, STORAGE, ANALYSIS AND VISUALIZATION.	Topic 1. INFORMATION ACQUISITION Main procedures to access information in heterogeneous contexts typical of cloud environments. Topic 2. DATA PROCESSING MODELS Main data processing strategies for subsequent analysis. Topic 3. ALGORITHM DESIGN FOR BIG DATA Possible algorithms for the exploitation of information from the processed data. Topic 4. INFORMATION VISUALIZATION TECHNIQUES Different alternatives for information representation, from traditional techniques to advanced methods. Topic 5. AVAILABLE TOOLS Tools to centralize the process and facilitate decision making.

Planning

Methodologies  ::  Tests
	Class hours	Hours outside the classroom	Total hours
Laboratory practicals	15	17.5	32.5
Lecture	9	10.5	19.5
Oral tests	0.5	6	6.5
Objective multiple-choice tests	1.5	15	16.5
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies

Methodologies   ::  

	Description
Laboratory practicals	Practicals to be done in the computer lab about different aspects of cloud computing and big data.
Lecture	Exposition of the contents of the course.

Personalized attention

Lecture Laboratory practicals Oral tests Objective multiple-choice tests

Description Resolution of doubts arising during the student's personal study.

Assessment

	Description	Qualification
Oral tests	Exhibition and/or delivery of work-projects and/or internship reports.	15%-50%
Objective multiple-choice tests	Partial and/or final objective tests	20%-45%
Other comments and second call

Sources of information

Access to Recommended Bibliography in the Catalog ULE

Basic	VK Jain, Big Data and Hadoop, Khanna Publishing, Luis Joyanes, Big Data, Análisis de grandes volúmenes de datos en organizaciones, Alfaomega, Viktor Mayer-Schönberger, Kenneth Cukier, Big data: La revolución de los datos masivos, Turner, Kris Jamsa, Cloud Computing: SaaS, PaaS, IaaS, Virtualization, Business Models, Mobile, Security and More, Jones & Bartlett Publishers, Dan C. Marinescu, Cloud Computing: Theory and Practice, Morgan Kaufmann,
Complementary

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