General information

Academic year: 2023/2024

Level: Master

Type: Official Master's Degree

ECTS Credits: 60

Programme orientation Research

Length of programme (full time): 1 YEAR

Mode of delivery: Face-to-face

Level of qualification: Máster (MECES level 3 - EQF level 7)

Model of study: Full-time (42-60 ECTS per school year)

Work-based learning (Practicum): Yes

Language(s) of instruction Spanish

More info:

Programme coordinator


Field(s) of education and training (ISCED-F)

  • Software and applications development and analysis (0613)

Main focus

; Graduates of this master’s programme will be able to model and solve problems in both the academic and real world, for which a large volume of data is available, regardless of the sphere of knowledge involved. More specifically, graduates will have a solid understanding of advanced techniques of Data Science and Computational Intelligence, which they will use to address these problems. They will also be capable of designing, configuring, implementing, and using computational platforms and networks that provide the necessary characteristics (cost, computational capacity, velocity, storage, reliability, availability, and security) for big data processing. Finally, graduates will have an in-depth knowledge of these techniques in fields such as biomedicine and bioinformatics, optimisation and prediction, advanced control, and bio-inspired robotics.


Students that have completed the second cycle have the following competencies: – Have demonstrated knowledge and understanding that is founded upon a basis or opportunity for originality in developing and/or applying ideas, often within a research context. – Can apply their acquired knowledge and understanding, and problem solving abilities in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their field of study. – Have the ability to integrate knowledge and handle complexity, and formulate judgments with incomplete or limited information, but that include reflecting on social and ethical responsibilities linked to the application of their knowledge and judgments. – Can communicate their conclusions, and the knowledge and rationale underpinning these, to specialist and non-specialist audiences clearly and unambiguously. – Have the learning skills to allow them to continue to study in a manner that may be largely self-directed or autonomous.

Programme qualification

Name of title awarded in original language

Máster Universitario en Ciencia de Datos e Ingeniería de Computadores

Qualification requirements

60 minimum credits

Programme courses

Course NameYearPeriod
Advanced Data Mining 1st Year 2nd Semester
Big data 1 1st Year 2nd Semester
Big Data 2 1st Year 2nd Semester
Bio-Inspired Vision Systems 1st Year 2nd Semester
Computational Biology with Big Data Omics and Biomedical Engineering 1st Year 1st Semester
Computational Neuroscience and Neuroengineering 1st Year 1st Semester
Computer Vision 1st Year 2nd Semester
Data Mining: Pre-Processing and Classification 1st Year 1st Semester
Data Mining: Unsupervised Learning and Fault Detection 1st Year 1st Semester
Data Science Applications and Intelligent Technologies 1st Year 2nd Semester
Embedded Systems and Hw/Sw Co-Design 1st Year 1st Semester
Entrepreneurship and Knowledge Transfer 1st Year 2nd Semester
High Performance Architecture for Vision 1st Year 2nd Semester
High Performance Computing for Classification and Optimisation 1st Year 2nd Semester
High Performance Signal Processing in Biomedicine 1st Year 2nd Semester
Image Feature Extraction 1st Year 1st Semester
Information Retrieval and Recommendation Systems 1st Year 2nd Semester
Internet of Things 1st Year 1st Semester
Introduction to Computer Engineering Programming 1st Year 1st Semester
Introduction to Data Science 1st Year 1st Semester
Introduction to Information Science Programming 1st Year 1st Semester
Master's Dissertation 1st Year 2nd Semester
Mechatronics and Aerospace Systems 1st Year 2nd Semester
Mobile Robotics and Neurobiotics 1st Year 2nd Semester
Mobility Agreement with the Iberoamerican University Association for Postgraduate Studies (AUIP) 1st Year Yearly
Probabilistic Graphical Models 1st Year 2nd Semester
Process Mining 1st Year 2nd Semester
Research Methodology 1st Year 1st Semester
Safe Servers 1st Year 1st Semester
Social Media Mining 1st Year 2nd Semester
Soft Computing Techniques for Learning and Optimisation. Neural Networks and Metaheuristics, Evolutionary and Bio-Inspired Programming 1st Year 2nd Semester
Soft Computing: Fuzzy Sets and Systems 1st Year 1st Semester
System Modelling and Time Series Prediction 1st Year 2nd Semester
Time Series and Data Flow Mining 1st Year 2nd Semester
Web Server Engineering 1st Year 1st Semester


Specialisation name

– Computer and Network Engineering – Data Science and Intelligent Technologies

Admission information

Access to Master’s Degree programmes is granted to holders of:
A.1. A Spanish official university degree.
A.2. A degree issued by a Higher Education institution from another European Higher Education Area Member State which allows access to Master Degree’s programmes in that State.
A.3. A degree from a non-EHEA education system, upon verification by the Spanish University that the aforementioned degree accredits an equivalent education level to that of a Spanish university degree and allows access to postgraduate programmes in the issuing country.
A.4. A Spanish Bachelor in Advanced Artistic Education.
A.5. Official Spanish university degrees of Diplomado, Arquitecto Técnico, Ingeniero Técnico, Licenciado, Arquitecto, Ingeniero, Graduado or Máster Universitario.

Specific admission requirements

Access to this Master’s degree programme is granted to holders of a Spanish official university degree of Licenciado or Graduado (Bachelor’s degree), Ingeniero Superior and Ingeniero Técnico (Bachelor’s degree), or equivalent degrees from other education systems, in the fields of Computer Engineering, Telecommunications, Electronics, Physics, Mathematics, and Statistics. Access is also granted to graduates from other Engineering degree programmes who can demonstrate prior knowledge in computer science, communications and/or mathematics.

General regulations

Grading scale
In the Spanish university system, modules/courses are graded on a scale of 0 to 10 points with the following qualitative equivalence:
0-4,9: «suspenso»; 5-6,9: «aprobado»; 7-8,9: «notable»; 9-10: «sobresaliente». A special mention, «Matrícula de Honor» may be granted to up to 5% of the students in a group provided they have got a «sobresaliente». To pass a module/course is necessary to get at least 5 points.
In cases of recognition of ECTS, professional experience, cultural or sports activities, or student representation no grading will be recorded but, where appropriate, the word «Apto».


UGR Examination Regulations


More info on academic regulations at:

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