Plan de estudios

COMPETENCES

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.
• CON5:In-depth knowledge and application of data analysis technologies and methods, including machine learning (supervised, unsupervised and reinforced), as well as predictive, prescriptive, descriptive and qualitative analytical techniques.
• CON6:Determine metrics for the evaluation and validation of data analysis.
• CON7:Identify and utilise effective visualisation and storytelling methods to create dashboards and data analysis reports.

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.
• HAB3:Identify, analyse, and utilise advanced techniques in data mining, text mining, and natural language processing.
• HAB4:Identify and use data analysis and graphical visualisation techniques for the analysis of organisational networks, customer relationships, and other tasks.
• HAB5:Analyse and apply advanced analytical and statistical methods for data preparation and processing.
• HAB6:Effectively use and manage big data infrastructures and services to support and make data-driven decisions.
• HAB7:Design and apply algorithms to solve real-world problems using modelling, optimisation and numerical calculation skills
• HAB8:Manage and apply computer and information tools for numerical calculation, optimisation, simulation, graphic visualisation, and other purposes to experiment and solve problems.
• HAB9:Design, develop, present and defend, individually before a university panel, a comprehensive data science project that synthesises the knowledge acquired during the Master's programme.

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.
• CC7:Design, analyse, and utilise efficient technologies and algorithms for modelling and simulating data systems.
• CC8:Design and use efficient algorithms to access and analyse large amounts of data, and learn how to use APIs to interconnect databases and heterogeneous data collections.
• CC9:Design and use systems for data collection (passive and active) aimed at testing hypotheses and solving problems, as well as employing metrics and techniques for validating and comparing machine learning algorithms.