* the student should have learned at least one software for the analysis and visualization of data especially for networks and text
* the student should be able to interpret experimental results and draw conclusions relevant to the domain of discourse.
* the student should be able to communicate effectively the results of an experimental analysis.
In the course we will address advanced topics in data analysis and data visualization of data. In particular the topics covered include:
– Network science: centrality and power, similarity, community, resilience, distances and small worlds, power laws and scale-free networks
– Text analysis: frequency of words and documents, sentiment analysis, n-grams and co-appearance of terms, topic modeling
Mark Newman. Networks: An Introduction. Oxford University Press, 2010.
Networks, crowds and markets, David Easley and Jon Kleinberg