Competencies and objectives
Course context for academic year 2026-27
Tecnología en Bases de Datos es una asignatura que introduce técnicas de bases de datos fundamentales para el desarrollo eficaz y eficiente del software de gestión así como de otras áreas, en concreto orientadas a sistemas de almacenamiento de grandes volúmenes de información. Por ello en esta asignatura se plantea conocer las tecnologías de bases de datos relacionales y no relacionales de forma que los estudiantes sean capaces de determinar ante un sistema de información concreto cuál es la solución óptima para el almacenamiento de la información y dar soluciones concretas a estas problemáticas enfrentándose al diseño y gestión de la base de datos correspondiente.
Learning outcomes / Course competencies (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2026-27
Skills/Skills
- HAB1 : Know how to apply the knowledge acquired to solve real problems in new or multidisciplinary environments related to their area of study, and possess the necessary learning skills to continue studying in a self-directed and autonomous manner.
- HAB2 : Apply the knowledge acquired in data science to solve real problems, developing effectively in multidisciplinary and international contexts, managing available information and resources, and demonstrating computer and information skills specific to the field.
Conocimientos/Contenidos
- CON1 : To possess and understand the knowledge that enables originality in the development and application of ideas within a research context, always integrating the social, ethical and legal responsibilities associated with the application of knowledge in data science.
- CON2 : To be able to manage, plan, design, develop, implement and maintain products, applications and services related to data science, ensuring service quality and complying with current regulations, as well as taking into account technical, economic and efficiency aspects.
- CON3 : To use data science techniques to uncover new relationships and provide insights into research problems or organisational processes, and to support decision-making
- CON4 : Acquire the skills, strategies, and procedures to act ethically and responsibly in the search for solutions that promote sustainable development, gender equality, non-discrimination, inclusion, justice, peace, and social equity.
Skills/Competences
- CC1 : Be able to integrate knowledge and make judgements in complex contexts with incomplete or limited information, considering social and ethical implications, clearly communicate conclusions and reasoning to specialist and non-specialist audiences, and develop self-directed and autonomous learning in topics related to data science
- CC2 : Being able to adapt to the changing environments typical of data science, promoting teamwork, creativity, critical thinking and entrepreneurial spirit, as well as understanding and applying the technical and scientific advances of the discipline.
- CC3 : Be able to use engineering principles and modern computer technologies to research, design, implement, and develop experiments, processes, instruments, systems, and infrastructures throughout the entire data lifecycle in data science.
- CC4 : Be able to lead projects and teams in Data Science, combining effective oral and written communication skills.
- CC5 : Adopt values of sustainability and social equity, supporting equality between women and men, the elimination of all discriminatory practices, social inclusion, justice, peace, and democratic and human rights principles and values.
- CC6 : Achieve a critical understanding of the complexity of socio-environmental challenges and problems, including the roots of gender inequalities, situations of discrimination and social exclusion, threats to peace and the achievement of human rights, as well as environmental challenges.
Learning outcomes (Training objectives)
No data
Specific objectives stated by the academic staff for academic year 2026-27
No data
General
Code:
43453
Lecturer responsible:
Saquete Boró, María Estela
Credits ECTS:
6,00
Theoretical credits:
1,20
Practical credits:
1,20
Distance-base hours:
3,60
Departments involved
-
Dept:
Software and Computing Systems
Area: Languages and Computing Systems
Theoretical credits: 1,2
Practical credits: 1,2
This Dept. is responsible for the course.
This Dept. is responsible for the final mark record.
Study programmes where this course is taught
-
UNIVERSITY MASTER'S DEGREE IN DATA SCIENCE
Course type: OPTIONAL (Year: 1)

