It is recommended to have completed the Mathematics, Statistics subjects and a good level in programming in R or Python

Basic Skills - Acquisition of knowledge and skills that provide with a background of creativity in the development and application of ideas, often within a research context. - Ability to apply acquired knowledge and to solve problems under novel or almost novel situations or within broader (multidisciplinary) contexts related with big data. - Acquisition of skills for learning in an autonomous and continuous manner. General Skills - Ability to apply the theoretical foundation of collect, storage, processing and presentation of information, especially for big data volumes. - Ability to identify the most suitable data analysis technique in each problem, and to apply it for obtaining the most appropriate solution to each one. - Ability to obtain practical and efficient solution for processing of big data volumes. - Skill to synthesize data analysis conclusions, and to communicate it clearly and convincingly in a bilingual environment. - Ability to generate new ideas and to anticipate new situations, within the context of data analysis and decision making. - Skill to working collaboratively and to collaborate with others autonomously. Specific Skills - Skill to design data processing systems, from the data gathering to statistical analysis and presentation of final results. - Ability to apply the basic principles of network science and apply them to the study of different data to model and forecast their behavior using features extracted from network science. - Ability to design effective visualizations of large data sets that can lead to the discover, interpretation and access to those datasets.. - Ability to identify the opportunity to apply network science and visualization techniques for solving real problems. Learning outcomes - Basic knowledge about network science techniques. - Understanding of basic network science techniques. - Making practical use of network science techniques in real problems - Basic knowledge of data visualization techniques - Ability to use visualization techniques to explain and solve real problems

1. Networks: general concepts and definitions 1.1 Network introduction - Network importance and examples - Historical background of network science - Network types and attributes 1.2 Network measures - Degree distributions and correlations - Transitivity and clustering coefficient - Connectedness and giant component Workshop 1: Gephi (network visualization) 2. Network communities 2.1 Centrality measures - Distances on networks, radius and diameter - Degree, closeness, harmonic and betweenness centralities - Eigenvector, Katz and PageRank centralities 2.2 Network mesoscale analysis - Cliques and network motifs - Modularity measure - Community detection algorithms Workshop 2: iGraph and graph visualization (R) 3. Network models 3.1 Random network models - Erdoös-Rényi (ER) random graph - Random Geometric Graph (RGG) - Configuration network models 3.2 Simple rule network models - Stochastic block model - Barabási-Albert (BA) scale-free network model - Watts-Strogatz (WS) small-world network model Workshop 3: Netlogo, community detection algorithms and network models (R) 4. Social Networks 4.1 Local and global properties of social networks - Examples of social networks and their properties - (Generalized) Friendship paradox - Six degrees of separation - Dunbar¿s numbers 4.2 Social mechanisms - Homophily - Triadic closure - Strength of relationships Workshop 4: Network analysis 5. Network dynamics and applications 5.1 Link prediction - Assortative, relational and proximity algorithms - Graph distance methods - Common neighbors methods - Preferential attachment - Katz score and hitting time - Community-based heuristics 5.2 Spreading processes - Susceptible-Infected (SI) model - Susceptible-Infected-Removed (SIR) model - Susceptible-Exposed-Infected-Removed (SEIR) model - More advanced models Workshop 5: Link prediction and spreading processes 6.1 Data visualization 6.1 Introduction to visualization - Types of visualizations - Examples of good visualizations - Examples of bad visualizations 6.2 Introduction to data and charts - Types of data - Types of charts - Visualization tools Workshop 6: Data visualization (ggplot) Workshop 7: GoogleVis, R shiny app and geolocalised data visualization

The course is imparted in specific rooms and laboratories for the Master Program. It will include: - Lectures for the presentation, development and analysis of the contents of the course. - Practical sessions for the resolution of individual problems and practical projects in the laboratory - Seminars for discussion with reduced groups of students