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Transversal Competences


  • CB6
    Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
  • CB7
    Ability to integrate knowledge and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
  • CB8
    Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
  • CB9
    Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.
  • CB10
    Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.


  • CT1
    Know and understand the organization of a company and the sciences that govern its activity; have the ability to understand labor standards and the relationships between planning, industrial and commercial strategies, quality and profit. Being aware of and understanding the mechanisms on which scientific research is based, as well as the mechanisms and instruments for transferring results among socio-economic agents involved in research, development and innovation processes.


  • CT2
    Capability to know and understand the complexity of economic and social typical phenomena of the welfare society; capability to relate welfare with globalization and sustainability; capability to use technique, technology, economics and sustainability in a balanced and compatible way.


  • CT3
    Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.


  • CT4
    Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.


  • CT5
    Achieving a level of spoken and written proficiency in a foreign language, preferably English, that meets the needs of the profession and the labour market.


  • CT6
    An awareness and understanding of sexual and gender inequalities in society in relation to the field of the degree, and the incorporation of different needs and preferences due to sex and gender when designing solutions and solving problems.

Generic Technical Competences


  • CG1
    Identify and apply the most appropriate data management methods and processes to manage the data life cycle, considering both structured and unstructured data
  • CG2
    Identify and apply methods of data analysis, knowledge extraction and visualization for data collected in disparate formats
  • CG3
    Define, design and implement complex systems that cover all phases in data science projects
  • CG4
    Design and implement data science projects in specific domains and in an innovative way

Technical Competences


  • CE1
    Develop efficient algorithms based on the knowledge and understanding of the computational complexity theory and considering the main data structures within the scope of data science
  • CE2
    Apply the fundamentals of data management and processing to a data science problem
  • CE3
    Apply data integration methods to solve data science problems in heterogeneous data environments
  • CE4
    Apply scalable storage and parallel data processing methods, including data streams, once the most appropriate methods for a data science problem have been identified
  • CE5
    Model, design, and implement complex data systems, including data visualization
  • CE6
    Design the Data Science process and apply scientific methodologies to obtain conclusions about populations and make decisions accordingly, from both structured and unstructured data and potentially stored in heterogeneous formats.
  • CE7
    Identify the limitations imposed by data quality in a data science problem and apply techniques to smooth their impact
  • CE8
    Extract information from structured and unstructured data by considering their multivariate nature.
  • CE9
    Apply appropriate methods for the analysis of non-traditional data formats, such as processes and graphs, within the scope of data science
  • CE10
    Identify machine learning and statistical modeling methods to use and apply them rigorously in order to solve a specific data science problem
  • CE11
    Analyze and extract knowledge from unstructured information using natural language processing techniques, text and image mining
  • CE12
    Apply data science in multidisciplinary projects to solve problems in new or poorly explored domains from a data science perspective that are economically viable, socially acceptable, and in accordance with current legislation
  • CE13
    Identify the main threats related to ethics and data privacy in a data science project (both in terms of data management and analysis) and develop and implement appropriate measures to mitigate these threats
  • CE14
    Execute, present and defend an original exercise carried out individually in front of an academic commission, consisting of an engineering project in the field of data science synthesizing the competences acquired in the studies