Degree in Data Science

(CG05) Analysis and synthesis capability in the preparation of reports and in the defence of ideas.
(CG06) Ability to access and manage information in different formats for subsequent analysis in order to obtain knowledge from data.
(CG07) Ability to autonomously make decisions and to properly and originally elaborate reasoned arguments, in order to obtain reasonable and contrastable hypotheses.
(CT01) To be able to access (bibliographical) information tools and appropriately use them in the development of their daily tasks.
(CT02) To be able to complete technical, scientific, social and human training in general, and to organise self-learning with a high degree of autonomy.
(CT03) Ability to defend your own work with rigor and arguments and to expose it in an adequate and accurate way with the use of the necessary means.
(CT04) To be responsible for ones own professional development and specialisation, applying the acquired knowledge in the identification of career opportunities and sources of employment.
(CT05) Ability to evaluate the advantages and disadvantages of different methodological and / or technological alternatives in different fields of application.
(CE01) Ability to solve the mathematical problems that can be posed in data science and be able to apply knowledge on: linear algebra, differential and integral calculus and numerical methods and optimisation.
(CE02) To methodologically know and apply the programming techniques and the algorithms necessary for the efficient processing of information and the computer resolution of problems that use large volumes of data.
(CE03) Ability to solve classification, modelling, segmentation and prediction problems from a set of data.
(CE04) To know and use the different models of data storage and database management systems using programming languages for the definition, query and handling of data.
(CE05) To understand the most relevant fields of application of data science and understand how data science is used to base and perform decision-making based on data
(CE06) Ability to represent and visualise data sets for the extraction of knowledge.
(CE09) To methodologically know and apply the concepts and techniques of probability and statistics necessary for the extraction of useful knowledge from data analysis.
(CE10) Ability to digitally process signals and extract information from them.
(CE11) Ability to design and implement data acquisition, its integration, transformation, selection, verification of its quality and veracity from different sources, taking into account its character, heterogeneity and variability.
(CE12) Ability to design and start solutions based on data analysis in the field of medicine and business, taking into account the specific requirements of this type of use cases.
(CE13) To know how to design, apply and evaluate data science algorithms for the resolution of complex problems.
(CE14) To understand and apply the ethical, legal and normative aspects related to data treatment and the application of the obtained knowledge.
(CE15) Ability to model and analyse the uncertainty in data-based studies, as well as to know how to interpret and contextualise the results obtained.
(CE16) Ability to develop an original exercise, to do it individually, as well as to present it and defend it before a university board, on the field of data science, in which the acquired competences are synthesised and integrated in the lessons.
(CG01) Knowledge of basic subjects and technologies that enable students to learn new methods and technologies, and to provide them with versatility to adapt to new situations.
(CG02) Ability to solve problems with initiative and creativity and to communicate and transmit knowledge, abilities and skills, which should include the ethical and professional responsibility of the activity of a data scientist.
(CG03) Capability to elaborate models, calculations, reports, to plan tasks and other works analogous to the specific field of data science.
(CG04) Ability to work in a multidisciplinary group in a multilingual environment and to communicate, orally and in writing, knowledge, procedures, results and ideas related to data science.
(CE07) Ability to model dependency between a response variable and several explanatory variables, in complex data sets, using machine learning techniques, interpreting the results obtained.
(CE08) Ability to understand, select and use the infrastructure and the techniques used to handle mass data, according to criteria of efficiency, scalability, security, error tolerance and adaptation to the production environment.
(CB5) Students must have developed the learning skills needed to undertake further study with a high degree of autonomy.
(CB4) Students must be able to communicate information, ideas, problems and solutions to both expert and lay audiences.
(CB3) Students must have the ability to gather and interpret relevant data (usually in their field of study) to make judgements that take relevant social, scientific or ethical issues into consideration.
(CB2) Students must be able to apply their knowledge to their work or vocation in a professional manner and have acquired the competences required for the preparation and defence of arguments and for problem solving in their field of study.
(CB1) Students must have acquired knowledge and understanding in a specific field of study, on the basis of general secondary education and at a level that includes mainly knowledge drawn from advanced textbooks, but also some cutting-edge knowledge in their field of study.