Organisations and Digital Societies

The course is the natural complement and extension of the Digital Technologies for Organizations course and it refers to the general area of Data Analysis for the Social Sciences, as well.
It has three general objectives:
1) Familiarize students with changing technology for data analysis and visualization, and the contextual usage of more than just one (R and Python);
2) Extend the usage of open data: data provided by public organizations and associations both Italian and international, institutes of Statistics both Italian (ISTST) and international (Eurostat, etc.), and other data in the public domain; requiring analysis and transformation operations of medium or medium-high complexity;
3) Improve data visualization practice and theory through an extended graph gallery and the study of theoretical principles and professional examples.
More specific objectives are:
1) Data analysis with Python: lists, arrays, dataframes, multiindex, and pivoting;
2) Adoption of Jupyter Notebook/Lab for documents containing narrative text, executable code, and results (data or plots);
3) Use of Github as a personal repository and versioning system;
4) Data visualization and dynamic maps for georeferenced datasets: Seaborn library and annotated choropleth maps (folium and geopandas libraries)

A student should demonstrate to have acquired a good knowledge of analysis methods and to have become familiar with open source tools for data analysis and visualization. Learning outcomes should also demonstrate that the student's preparation is not limited to a sufficient usage of technologies, but she/he has understood critical aspects of a data analysis, the appropriate way of conducting a data analysis, and she/he is able to produce well-motivated evaluations of both open data analyses and the graphical representation of results.