LEARNING OUTCOMES
RA1:Students should have acquired advanced knowledge and demonstrated an understanding of the theoretical and practical aspects and working methodology in the field of data science and engineering with a depth that reaches the forefront of knowledge.
RA2:Be capable of applying their knowledge and
problem-solving skills, through arguments or procedures developed and sustained by themselves, in complex or professional and specialized work settings that require the use of creative and innovative ideas
RA3:Have the ability to collect and interpret data and information on which to base their conclusions including, where appropriate and pertinent, reflection on issues of a social, scientific or ethical nature within their field of study
RA4:Be able to cope with complex situations or those that require the development of new solutions in the academic, work or professional field within their field of study
RA5:Know how to communicate to all types of audiences (specialized or not) in a clear and precise manner, knowledge, methodologies, ideas, problems and solutions within the scope of their field of study
RA6:Be able to identify their own training needs in their field of study and work or professional environment and organize their own learning with a high degree of autonomy in all types of contexts (structured or not).
BASIC COMPETENCES
CB3:Students have the ability to gather and interpret relevant data (usually within their field of study) in order to make judgements which include reflection on relevant social, scientific or ethical issues.
CB5:Students will have developed the learning skills necessary to undertake further study with a high degree of autonomy.
GENERAL COMPETENCES
CG4:Ability to solve technological, computer, mathematical and statistical problems that may arise in data engineering and science
CG6:Ability to synthesize the conclusions obtained from the analyses carried out and present them clearly and convincingly both in writing and orally
SPECIFIC COMPETENCES
CE2:Ability to correctly identify predictive problems corresponding to certain objectives and data and to use the basic results of regression analysis as the basis for prediction methods
CE13:Ability to apply and design machine learning methods in classification, regression and clustering problems and for supervised, unsupervised and reinforcement learning tasks