The main objective of the course is to train students in the analysis, processing and management of large volumes of data, as well as in the understanding and application of distributed data technologies such as blockchain. It is intended that the student acquires a comprehensive and critical view of the challenges and opportunities offered by big data and blockchain in real environments. Specific objectives: - Understand the fundamentals of big data processing, including distributed architectures, scalable storage and parallel processing. - Become familiar with the key technologies and tools of the Big Data ecosystem. - Analyse the concept of blockchain, its cryptographic principles and its application beyond cryptocurrencies (digital identity, traceability, smart contracts, etc.). - Design and develop solutions that integrate massive data flows and chained data structures, evaluating their performance, scalability and security.

BLOCK 1. MASSIVE DATA INTEGRATION. 1.1. Integration of data sources. 1.2. Big Data for data integration and analysis. 1.3. Main applications. BLOCK 2. BLOCKCHAIN. 2.1. Origin of Blockchain. 2.2. Blockchain operation. 2.3. Consensus algorithm. 2.4. Types of Blockchain. 2.5. Main applications.

TRAINING ACTIVITIES AF1 - Theoretical class [6.6 hours with 100% attendance, 0.20 ECTS]. AF2 - Practical classes [5 hours with 100% attendance, 0.19 ECTS]. AF4 - Laboratory practices [5 hours with 100% attendance, 0.20 ECTS]. AF5 - Tutorials [5.83 hours with 25% of attendance, 0.19 ECTS]. AF6 - Group work [30.5 hours with 0% attendance, 1.02 ECTS]. AF7 - Individual student work [30.5 hours with 0% attendance, 1.02 ECTS]. AF8 - Partial and final exams [6.66 hours with 100% attendance, 0.20 ECTS]. TEACHING METHODOLOGIES MD1 - Class lectures by the professor with the 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 professor 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. .... posed by the teacher individually or in groups. MD4 - Presentation and discussion in class, under the moderation of the professor, of topics related to the content of the subject, as well as of practical cases. case studies. MD5 - Preparation of papers and reports individually or in groups.