Checking date: 22/07/2025 14:04:47


Course: 2025/2026

Mobile Applications
(20537)
Bachelor in Data Science and Engineering (Plan: 566 - Estudio: 350)


Coordinating teacher:

Department assigned to the subject: Telematic Engineering Department

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Learning Outcomes
K3: To know fundamental contents in their area of study starting from the basis of general secondary education and reaching a level proper of advanced textbooks, including also some aspects of the forefront of their field of study. K4: Knowledge of basic scientific and technical subjects that qualify for the learning of new methods and technologies, as well as providing a great versatility to adapt to new situations, in the field of data storage, management and processing. S3: Ability to solve technological, computer, mathematical and statistical problems that may arise in data engineering and science, applying knowledge of mathematics, probability and statistics, programming, databases, and languages, grammars and automata. S13: Apply fundamental knowledge of network architectures. S16: Ability to synthesize the conclusions obtained from the analyses carried out and present them clearly and convincingly both in writing and orally to both specialized and non-specialized audiences C3: Ability to solve problems with initiative, decision making, creativity, and to communicate and transmit knowledge, skills and abilities, understanding the ethical, social and professional responsibility of the data processing activity. Leadership capacity, innovation and entrepreneurial spirit C5: Be able to analyze and synthesize basic problems related to engineering and data science, elaborate, defend and efficiently communicate solutions individually and professionally, applying the knowledge, skills, tools and strategies acquired or developed in their area of study.
Description of contents: programme
1. Introduction and evolution of distributed computing 2. Distributed Systems: models of computing distribution 3. Cloud computing: virtualization, deployment architectures, security and location aspects, challenges, opportunities, use cases 4. Cloud communications: access to instances and services, organization of communications between instances, representation, capture and extraction of data, and network architectural considerations 5. Big Data: Big Data systems architecture, hardware and network characteristics according to usage, anatomy of a data center, modern distributed storage systems, modern batch processing systems.
Assessment System

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