CB1. Students have proven knowledge in an area of study that starts in secundary educatian and it is usually at a level that, although supported by advanced textbooks, also includes some aspects that imply knowledge coming from the forefront of his field of study
CB2. Students know how to apply their knowledge to their work or vocation in a professional manner and that they possess the skills that are usually demonstrated through the elaboration and defense of arguments and the resolution of problems within their area of study
CB3. Students have the ability to gather and interpret relevant data (usually within their area of study) to make judgments that include a reflection on relevant issues of social, scientific or ethical nature
CE17: Students have proven knowledge of security and privacy requirements in big data and the relevant technical, organizational and legal protection measures. Students have proven knowledge of cipher techniques and how to apply them to protect data. CE18: Students have the capacity to acquire basic and fundamental knowledge of network architectures.
CG1: Adequate knowledge and skills to analyse and synthesise basic problems related to engineering and data science, solve them and communicate them efficiently.
CG2: Adequate knowledge and skills to learn the next coming methods and technologies, and to adapt to new situations.
CG4: Ability to solve technological, computational, mathematical and statistical problems that may arise in engineering and data science.
CT1: Ability to communicate knowledge orally and in writing, before a specialised and non-specialised public.
RA1 Students should have acquired advanced knowledge and demonstrated an understanding of the theoretical and practical aspects and the methodology of work in the field of data science and engineering with a depth that reaches the forefront of knowledge
RA2 Students should be able to apply their knowledge and provide arguments to solve problems in complex scenarios that require new, creative ideas and innovation.
RA3 Students should have the ability to collect and interpret data and information on which to base their conclusions including, when necessary and relevant, reflection on social, scientific or ethical issues in the field of the data engineering