Social data analysis and visualization

• Access and assess types of available on-line data for data visualization.
• Use state-of-the-art tools to filter, clean, and organize large, complex datasets
• Apply standard tools from high-level programming languages (e.g. Python, MatLab, R) to evaluate data visualization methods for exploration of single variable data, including dot and jitter plots, histograms, kernel density estimates, distribution functions, and more.
• Assess and apply data visualization methods for data exploration of multiple variable data, including estimating functional relationships (e.g. by smoothing noise, visualizing residuals, using log, semilog-plots, and simple regressions).
• Use visualization techniques to evaluate and identify limitations of summary statistics, based e.g. on Simpson’s paradox, and Anscombe’s quartet.
• Use basic principles of displaying visual information (e.g. Tufte’s six principles of graphical integrity) to create explanatory visualizations.
• Apply specialized visualization software (e.g. JavaScrip’s D3 library or Python libraries) in order to build custom visualizations designed to explain insights from a dataset to an audience.
• Analyze cases of narrative data visualization to extract the underlying principles used to construct this type of visualization.
• Build a narrative data-visualization.

The course is based on mastering tools for analyzing data sets generated from online social interactions. The course is structured around short lectures combined with exercises, as well as a high degree of independent project work

02101/02100, The course involves work with high level programming languages (e.g. Python), so practical programming experience is recommended (e.g. in Python/Java/JavaScript/C/C++)

Lectures, exercises and final project