K1: Know the principles and values of democracy and sustainable development, in particular, respect for human rights and fundamental rights, gender equality and non-discrimination, the principles of universal accessibility and climate change. K3: Identify and analyze research methodologies and sources to develop academic work in the field of digital information management K4: Understand and apply the fundamental theories, instruments and techniques to manage information in digital media, covering its organization, control, communication and preservation K5: Know the fundamental theories, instruments and techniques for managing information in digital media, covering its organization, control, communication and preservation K7: Understand the fundamentals of statistics and quantitative analysis to interpret data, as well as the appropriate techniques for their collection and processing, understanding different structures, social contexts and user needs. S1: Plan and organize teamwork by making correct decisions based on available information and gathering data in digital environments. S2: Use the information by interpreting relevant data, avoiding plagiarism, and in accordance with the academic and professional conventions of the area of study, being able to evaluate the reliability and quality of said information. S3: Apply digital information management principles in different organizational environments, ensuring effective communication of processes and results to stakeholders. S5: Be able to design, manage, and operate with information through database systems, demonstrating skill in information retrieval and the use of query languages to meet complex information needs. S6: Be able to collect, process, cleanse and aggregate data by understanding the needs of users and organizations and how they need them. S7: Experiment with data visualization tools to represent information intuitively, properly presenting the results to different types of audiences. S8: Develop skills in the creation of digital content and multimedia editing by applying usability principles. S10: Apply statistical analysis techniques and metric studies to evaluate and measure the impact of data in digital environments. S12: Be able to advise on the definition of strategy and project management regarding tracking, indexing, content structuring, link building, etc C1: Know and be able to manage interpersonal skills on initiative, responsibility, conflict resolution, negotiation, among others, which are required in the professional field. C2: Be able to apply knowledge in a professional way in solving specific digital information management problems using the tools and techniques learned in the academic field C3: Demonstrate ability in the development and execution of digital content projects autonomously working in multidisciplinary teams. C4: Capacity for continuous autonomous learning that facilitates adaptation to new situations and the updating of knowledge in the field of digital information.

This course will introduce you to Data Science, its concept, applications, and future perspectives in the Social Sciences. In a globalized, ever-changing, increasingly accelerated and complex world, having professionals who are able to collect, analyze, and interpret the vast amount of existing heterogeneous data (Big Data) is absolutely crucial for decision making in the business, social, economic, and political areas. Data Science has been labeled 'the sexiest job of the 21st century' (Harvard Business Review, 2012), and in fact there is a growing demand of professionals trained in this discipline. In this course, students will approach the management and analysis of different types of data -including those from surveys, web-based and social media, business data, and research data, among others- by means of the latest techniques and tools for statistical learning. Contents: 1. Foundations of Data Science: concept, theories, and approaches. 2. Preliminary analysis/preparation of data: how to collect, clean, treat and combine data from different sources. 3. Data visualization: best practices in large data visualization and communication. 4. Predictive tools: applications of the main tools for statistical learning, regression and classification.

Theoretical knowledge acquisition (3 ECTS), through lectures, teaching materials prepared by the instructor, online tutorials, readings, and students' individual study. Acquisition of skills and abilities (3 ECTS) through the realization of web positioning projects, analytics and digital marketing plans, both individually and/or in groups. The methodology of this course involves learning as a process of construction, and teaching as a support. Thus the teaching-learning process will encourage continuous learning and collaborative students, facilitating the exchange of experience between them.