1. Graphs and general concepts
1.1 Mathematical definition and examples
1.2 Graph Theory, historical introduction
1.3 Weighted and directed graphs
1.4 Bipartite graphs
1.5 Adjacency matrix
1.6 Degree, average degree, degree distributions
1.7 Topological concepts in graphs (distance, shortest path, diameter)
1.8 Practical example
1.9 Centrality metrics
1.10 Cliques, motifs, clustering and communities
1.11 Types of networks: random networks, small world, scale-free
1.12 Hubs and preferential attachment
2. Social Networks
2.1 Definition and context
2.2 Local and global properties of social networks
2.3 Difference between social networks and other networks
2.4 Social mechanisms
2.5 Applications of social networks: fraud detection, recommendation systems, product adoption, churn, etc.
3. Graph analysis / Social Network Analysis
3.1 Overview of software/libraries for SNA
3.2 Introduction to the igraph library
3.3 Introduction to the networkX library in Python
3.4 Practical example
3.5 Create a graph
3.6 Analyze a graph
3.7 Simulate a graph
3.8 Test a graph
4. Practical examples of graph analysis
4.1 Link prediction: application to friend recommendation
4.2 Epidemic models in networks
4.3 Build, analyze and visualize information networks: the case of Twitter and its API
4.4 Analysis and visualization of dynamic networks
5. Introduction to data visualization
5.1 Data types and sources
5.2 Main tools to visualize data. Introduction to Tableau, ggplot and D3
5.3 Data reduction techniques
5.4 Static visualization of data
5.5 Visualization of one-dimensional data
5.6 Visualization of multi-dimensional data
5.7 Geo-spatial data
5.8 Content (text) visualization
5.9 Time-series and predictive model visualization
5.10 Graph visualization
5.11 Dynamic data visualization
5.12 Visualizaton of transport data (world-wide flights)
5.13 Visualization of large social networks from Twitter
5.14 Visualization of movie ratings