Objective 1
To develop the knowledge and skills to select the most suitable storage and management solution for both structured and non-structured data for a given problem. To develop knowledge of acquisition, extraction, manipulation and data-transformation processes in different environments.
Objective 2
To acquire skills in the use of Data Science’s main architectures and technical tools.
Objective 3
To develop knowledge of statistical techniques and machine learning methods to perform descriptive and predictive data analysis.
Objective 4
To provide students with the resources they need to be creative when addressing scientific and technological issues in Data Science.
Objective 5
To implement the knowledge they have learned to build a Data Science Project based on a real work environment.
Objective 6
To acquire advanced training and specialized and multi-disciplinary knowledge to address research issues in Data Science.
General Competences:
The general competences are included in the first category. These are shared by all Master’s degrees in Spain (by Royal Decree), or are proposed by the Universidad Politécnica de Madrid, or are included in the standard EURO-INF, which defines the competencies required for a degree to be accredited as an MSc in Computer Science.
Specific Competences in Research:
The second category includes competences concerning the research orientation of the degree proposed or shared by all research-oriented Master’s degrees offered by the School of Computer Science, and which are different from those shared by the professionally-oriented Master’s degrees.
Specific Competences in Data Science:
And finally, the third category includes the specific competences in Data Science that set the proposed Master’s degree apart from other research Master’s degrees at the School of Computer Science.
General Competences:
Specific Competences
in Research:Specific Competences
in Data Science:
CG1: To acquire scientific knowledge, enabling the student to be original in the development or application of new ideas within the context of research.
CG2: To apply acquired knowledge and problem-solving skills in new, unfamiliar or broader contexts related to their field of study.
CG3: To demonstrate the ability to integrate knowledge and handle complexity, and to formulate judgements with incomplete or limited information, that include reflecting on social and ethical responsibilities related to the application of their knowledge and judgement.
CG4: To demonstrate the ability to use diverse methods to clearly and unambiguously communicate their conclusions, and the knowledge and rationale underpinning them, to specialist and non-specialist audiences.
CG5: To be able to plan self-learning and improve personal performance as a foundation for lifelong learning and ongoing professional development.
CG6: To specify and complete informatics tasks that are complex, incompletely defined, or unfamiliar.
CG7: To apply state-of-the-art or innovative methods in problem-solving, possibly involving the use of other disciplines.
CG8: To demonstrate that they can think creatively to develop new and original designs, approaches, methods, etc.
CG9: To apply and integrate knowledge and understanding of other informatics disciplines in support of study in their own specialist area(s).
CG10: To describe and explain applicable techniques and methods for their particular area of study and identify their limitations.
CG11: To gain awareness and understanding of informatics to build models, as well as complex information systems and processes.
CG12: To be able to contribute to the further development of informatics.
CG13: To appreciate the skills required to work with, and lead, a team that may be composed of people from different disciplines and different levels of qualification.
CG14: To communicate effectively, both verbally and through a variety of communications media, to a variety of different audiences, and preferably also in a second language.
CG15: To demonstrate knowledge of the main principles in project management, risk management and change management, as well as to be able to implement project-management and risk-reduction methodologies.
CGI1: To acquire advanced scientific knowledge in informatics, enabling the student to be original in the development or application of new ideas within the context of research (EUR-ACE®).
CGI2: To demonstrate the ability to identify, locate and obtain required data in a research work, and the ability to design and conduct experimental or analytical investigations, critically evaluate data, and draw conclusions (EUR-ACE®).
CGI3: To undertake literature searches and reviews using databases and other sources of information as a necessary step in developing any research work.
CGI4: To demonstrate the ability to read and understand research publications and studies within their subject matter, as well as to recognize their scientific value.
CGI5: To acquire necessary knowledge on research-funding programs and technology-transfer mechanisms, as well as on current intellectual property right law.
CECD1: To know the processes of data capture, extraction, handling and transformation in different environments.
CECD2: To know and properly select the most suitable storage solution for a given problem, for both structured and non-structured data.
CECD3: To demonstrate the ability to use big-data processing tools for both batch and online processing.
CECD4: To demonstrate the ability to apply the most adequate visualization technique for the analysis and exploration of data in a given scenario and to communicate the results of the analysis.
CECD5: To demonstrate the ability to apply advanced statistical techniques to model, analyze and predict.
CECD6: To demonstrate the ability to apply data-mining techniques for classifying, modelling, segmentation and prediction from a data set.
CECD7: To demonstrate the ability to build intelligent data-based models.
CECD8: To demonstrate the ability to design and manage Data Science projects.
CECD9: To demonstrate the ability to apply ethical and legal frameworks in the data profession.
CEC10: To demonstrate the ability to use Data Science in a given environment.