Dear Data

The flow of data science
1. Introduction [markdown, markup]
2. A hasty tour inside R [images, markdown, markup]
3. dplyr: a grammar of data manipulation [images, markdown, markup]
4. ggplot2: a grammar of data visualization [markdown, markup]
5. Data Challenge: The Elo method in chess [markdown, markup]
6. Modelling: find a good explanation for your data [markdown, markup]
7. Demo – Life expectancy change over time [markdown, markup]
8. Data Challenge – Life expectancy change over time for each continent [markdown, markup]
9. Communication [markup]
Network Science
- Basics
  1. Introduction [images, markdown, markup]
  2. igraph: network analysis [markdown, markup]
  3. tidygraph and ggraph: tidy network analysis and visualization [markdown, markup]
  4. Graph basics [images, markdown, markup]
- Local analysis
  1. Centrality: usual suspects [markdown, markup]
  2. Centrality: recursive methods [graphs, markdown, markup]
  3. Data Challenge: A dolphin social network [markdown, markup]
  4. Power measures [markdown, markup]
  5. Data Challenge: Power and centrality in the European natural gas market [markdown, markup]
  6. Signed networks [markdown, markup]
  7. Similarity and heterogeneity [markdown, markup]
  8. Data Challenge: Interdisciplinarity and autarchy in science [markdown, markup]
- Group analysis
  1. Community detection and clustering [markdown, markup]
  2. Data Challenge: Cluster Italian soccer teams in performance classes [markdown, markup]
  3. Data Challenge: Detect the most dangerous terrorists of Madrid train bombing [markdown, markup]
- Global analysis
  1. Network models [markdown, markup]
  2. Connectivity and resilience [markdown, markup]
  3. Data Challenge: Attack the Madrid train bombing terror network [markdown, markup]
  4. Geodesic distances and small worlds [markdown, markup]
  5. Power laws and scale-free networks [markdown, markup]
  6. Assortativity [markdown, markup]
  7. Motifs [markdown, markup]
  8. Epidemics on networks [markdown, markup]