Due to the growing evolution and application of Artificial Intelligence techniques in various contexts, it is pertinent to develop skills that enable an ethical understanding of how such techniques can be used for the benefit of society.
General Objectives
This training aims to intersect the field of Social Sciences with the field of Data Science, with a view to developing skills in data mining that, in turn, promote improved productivity in industrial, social and technological organisations. In particular, mastering these skills enables professionals working in the field of Social Sciences to make data-driven decisions, rather than relying on experience and instinct to support decision-making processes. Initially, this training provides students with a general but detailed overview of the main descriptive, predictive and prescriptive techniques for extracting knowledge from data. This component serves as a basis for equipping students with the technical knowledge necessary to understand, model and implement decision-making support systems in the context of the Social Sciences. In a second stage, students will be exposed to real case studies, in which they will be challenged to critically solve problems based on the technical skills they have acquired. During the course, the presentation of theoretical concepts will be accompanied by tutorials, in which students will be able to numerically implement the techniques they have learned using appropriate software.
Recipients
It is aimed at business managers, marketing professionals and other professionals working in the social sciences (e.g. psychology, sociology, communication, management, economics, political science) who wish to take advantage of (descriptive/predictive/prescriptive) approaches to data analysis and knowledge extraction to optimise their decision-making processes in generalised organisational contexts. Graduates or undergraduates in non-STEM (Science, Technology, Engineering, Arts and Mathematics) fields are eligible to apply.
Terms of access
Graduates or undergraduates in non-STEM (Science, Technology, Engineering, Arts and Mathematics) fields are eligible to apply.
Selection and ranking criteria
Candidates will be selected and ranked according to the following criteria:
- Higher education not CTEAM;
- Academic record, considering the classification of academic degrees obtained (tiebreaker criterion).
The final classification of candidates is expressed on a numerical scale from 0 to 20 points.
Assessment
Trainees will be assessed on a scale of 0 to 20 in each of the modules that make up the training, using assessment tools to be defined by the teachers. These assessment tools must be consistent with the objectives and teaching methodologies to be developed in this training, and this consistency must be clearly stated in the description sheets for the various curricular units.
Fees and Charges
With regard to fees, applications and enrolment, the following applies:
- Application fee €50
- Enrolment fee €0
- Current UCP students receive a 50% discount on the application fee and tuition fees.
- Former UCP students receive a 10% discount on the application fee.
- Tuition fees €110 (equal to the value of the scholarship)
- UCP staff and lecturers are exempt from tuition fees and application fees.
Co-funded by the PRR - Recovery and Resilience Plan by the European Union
