Competences
MSc Degree in Computational Biology
General Competences
| General Competence | Competence |
| GC 1 | To possess the appropriate skills, which are the technological and scientific core of Computational Biology that will enable the development of original ideas in this field. |
| GC 2 | To get familiarised with the work and method of Computational Biology in actual practice, acquiring the ability to independently design applications and experiments and to describe, quantify, analyse and evaluate the results in a critical way. |
| GC 3 | To know how to apply the acquired skills and problem-solving capacity in new or unfamiliar environments within broader or multidisciplinary contexts related to the Computational Biology field. |
| GC 4 | To know how to communicate both the bases of their work strands in the Computational Biology field and the results and conclusions obtained to specialised and non-specialised audiences sending an unambiguous message. |
| GC 5 | To know how to integrate skills in the Computational Biology field, draw conclusions, hypotheses or work strands based on available information and form a precise opinion of the social responsibilities and ethics related to their knowledge application. |
| GC 6 | To have the learning skills to keep working through self-directed learning as the primary mode, to better adapt to the fast evolution occurring in the Computational Biology field. |
Specific Competences
| Specific Competence | Competence |
| SC 1 | Understand the molecular bases and the most common standard experimental techniques in omics research (genomics, transcriptomics, proteomics, metabolomics, interactomics, etc.). |
| SC 2 | Use operating systems, programs and tools commonly used in computational biology, as well as manage high-performance computing platforms, programming languages and bioinformatics analysis. |
| SC 3 | Analyse and interpret bioinformatics data from omics technologies and present bioinformatics solutions to problems derived from research with mentioned data. |
| SC 4 | Work with different databases (including big data), know their structures and ontologies, apply statistics to their analysis, using representation and visualisation tools. |
| SC 5 | Use computational biology tools for genomic analysis, such as comparative genomics and evolutionary biology. |
| SC 6 | Identify the bioinformatics needs of research centres and companies in the biotechnology and biomedicine sectors. |
| SC 7 | Apply knowledge to scientific-technological work in Computational Biology, Bioinformatics and big data. |
| SC 8 | Ability to integrate technologies and systems of Artificial Intelligence with a general nature and in broader and multidisciplinary contexts. |
| SC 9 | Ability to interpret the supervised and unsupervised classification models obtained by applying Machine Learning techniques to a set of data. |
| SC 10 | Understand reusable knowledge representation techniques and reasoning models in centralized and distributed environments to solve intelligent behaviour problems. |
Transversal competences
| Transversal competence | Competence |
| TC 1 | Ability to professionally apply the acquired knowledge, taking into account the work impact in a global and societal context. |
| TC 2 | Ability to apply the scientific method for problem-solving effectively and creatively. |
| TC 3 | Professional and Bioethics commitment and respect to respect environmental sustainability. |
| TC 4 | Ability to address every type of audiences in English language, both orally and writing. |
| TC 5 | Ability to organize and write technical documents, plan experiments and, on the whole, professional works. |
| TC 6 | Ability to lead and work in multidisciplinary and multicultural teams in an international context. |
| TC 7 | Ability to manage information and communication technologies in a professional context. |
| TC 8 | Analyse and summarise relevant data and handle problems from distinct perspectives. |