Checking date: 17/05/2024

Course: 2024/2025

Big Data: Data Analysis Techniques
Master in Libraries, Archives and Digital Continuity (Plan: 500 - Estudio: 335)

Coordinating teacher: GARCIA ZORITA, JOSE CARLOS

Department assigned to the subject: Library and Information Sciences Department

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
- Database Design (340-17452). - Statistical Data Analysis (340-17467). - Data Visualisation (340-17468). - Metric Studies of Information (340-17461). - Data Science (340-17477). - Information Visualisation (335-17283).
BASIC COMPETENCES CB8 Learn how to design strategies for the analysis and exploitation of large volumes of data. CB9 Make proposals for data management for different contexts and organisations. GENERAL COMPETENCES CG2 Learn to identify work and research lines related to the analysis, cleansing and exploitation of data. CG6 Know the different business models related to Big data. CG8 Learn to identify the potential value and use of data. CG9 Learn how to adapt and develop methods and techniques according to the new knowledge and skills required for data management and analysis. LEARNING RESULTS - Knowledge of methods and techniques used for designing and assessing strategies of data management. The student after passing the subject must: - Apply techniques to elaborate studies and reports analysing data management in organisations. - Know and understand concepts and terms related to big data. - Design, plan and implement data cleansing and analysis processes. - Learn to identify strategic data and high impact in the organisation. - Know the different methods and techniques of data extraction, data cleansing, and linked data. - Acquire a good command using tools for data extraction, data cleansing, and linked data. The student, after passing the subject, must: - Know the limitations and technical capabilities required in Big Data. - Understand and understand the value of data from different sources. - Knowing tools that allow designing, preparing, analysing and managing large volumes of structured or unstructured information. - Use data analysis techniques to obtain valid conclusions for decision making. - Normalise, relate and enrich the information provided by different datasets. - Use different techniques and tools for data cleansing, data analysis and linking data.
Skills and learning outcomes
Description of contents: programme
Module 1.- Introduction to Big data. Basic fundamentals. Concepts 1.1. Big data in the context of records management. 1.2. New trends in data management. Big data and Smart data 1.3. Big data in the strategies of public and private organizations 1.4. Trends, policy, issues and initiatives Module 2.- Data analysis and filtering techniques. 2.1. Data quality audit 2.2. Structured and unstructured data testing tools. Introduction to Open Refine. 2.3. Unstructured data mining 2.4. Predictive models in data mining Module 3.- Data processing and open linked data 3.1. Linked data, interoperability and the semantic web standards 3.2. Database connectivity using WebService 3.3. Data Extraction. Open source solutions 3.4. Project planning and data Processing. Case study
Learning activities and methodology
* THE TRAINING ACTIVITIES ACORDING TO THE STUDY PLANIFICATION WILL BE: AF1 Individual work for the study of theoretical and practical materials developed and contributed by the teacher. AF2 Individual work for problem solving and case studies. AF3 Theoretical-practical classes. AF4 Tutorials. AF5 Group work. AF6 Active participation in forums enabled by the teacher in the virtual educational platform. AF7 Perform self-assessment test for content review. AF8 Synchronous online debates and colloquiums Type of activity Is it synchronous? Total hours Hours of synchronous interactivity No. In-person hours % In-person attendance Student AF1 NO 24,7 0 0 0 AF2 NO 22 0 0 0 AF3 SI 3 3 3 100 AF4 SI 3 3 0 0 AF5 NO 30 0 0 0 AF6 NO 1,3 0 0 0 AF7 SI 3 3 0 0 AF8 SI 3 3 0 0 Total 90 12 3 3,33% * TEACHING METHODOLOGIES: MD1 Presentations in the teacher's class with support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students' learning. MD2 Critical reading of texts recommended by the teacher of the subject: Press articles, reports, manuals and / or academic articles, either for later discussion in class, or to expand and consolidate the knowledge of the subject. MD3 Resolution of practical cases, problems, etc. Raised by the teacher individually or in a group. MD4 Exposition and discussion in class, under the moderation of the professor of subjects related to the content of the subject, as well as of practical cases. MD5 Preparation of individual and group work and reports. MD6 Reading of theoretical and practical teaching materials. TUTORIALS SCHEME The schedules of the tutorials, adjusted to the disposition by the University, can be consulted in the space of the course in the platform (Aula Global). They will include at least two sections, one for face-to-face and the other for online tutorials. In addition to these official tutorials, students can request and arrange with the teacher online or on-site tutorials outside those times.
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Calendar of Continuous assessment
Basic Bibliography
  • CABALLERO, Rafael y MARTIN, Enrique . Las bases del Big Data. Madrid: CATARATA. 2017
  • GÓMEZ GARCÍA, José Luis. Introducción al big data. Barcelona: UOC. 2015
  • JOYANES AGUILAR, Luis. Big data: análisis de grandes volúmenes de datos en organizaciones. Barcelona: Marcombo. 2013
  • LARA TORRALBO, Juan Alfonso. Minería de datos. Madrid: CEF. 2014
  • MARR, Bernard. Big Data. La utilización del Big Data, el análisis y los parámetros SMART para tomar mejores decisiones y aumentar el rendimiento. Madrid: TEEL. 2016
  • MARR, Bernard. Big data en la práctica. Madrid: TEEL. 2017
  • MAYER-SCHÖNBERGER, Viktor. Big data: la revolución de los datos masivos. Madrid: Turner. 2013
  • NETTLETON, David F. Data mining: fundamentos y metodologías. Barcelona: UOC. 2007
  • SCHMARZO, Bill. Big data: el poder de los datos. Madrid: Anaya Multimedia. 2014
  • SIEGEL, Eric. Analítica predictiva: predecir el futuro utilizando Big Data. Madrid: Anaya Multimedia. 2013

The course syllabus may change due academic events or other reasons.