Organisations and Digital Societies
A.Y. 2022/2023
Learning objectives
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)
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)
Expected learning outcomes
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.
Lesson period: First trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
Course syllabus
1. Introduction to Data Science with Python
2. Jupyter Notebook and Markdown language for interactive documents
3. Data structures, data frames and multi-index
4. Data Transformation: main libraries and advanced usage
5. Finding and using national and international Open Data : socioeconomics data, environmental, mobility, commerce and industry, energy, cultural events, etc.
6. Data Visualization: graphical organization, extended gallery, and interactive choropleth maps
During the course, several exercises with Open Data, of increasing complexity, should be completed in order to acquire the skills necessary to analyze real case studies. For a successful preparation, it is required to also work autonomously on several exercises.
2. Jupyter Notebook and Markdown language for interactive documents
3. Data structures, data frames and multi-index
4. Data Transformation: main libraries and advanced usage
5. Finding and using national and international Open Data : socioeconomics data, environmental, mobility, commerce and industry, energy, cultural events, etc.
6. Data Visualization: graphical organization, extended gallery, and interactive choropleth maps
During the course, several exercises with Open Data, of increasing complexity, should be completed in order to acquire the skills necessary to analyze real case studies. For a successful preparation, it is required to also work autonomously on several exercises.
Prerequisites for admission
English reading and understanding: basic knowledge.
To be familiar with personal computer usage and with the internet.
Recommended to have attended to the Tecnologie Digitali per le Organizzazioni course.
To be familiar with personal computer usage and with the internet.
Recommended to have attended to the Tecnologie Digitali per le Organizzazioni course.
Teaching methods
Classes are in person and it is recommended to bring a laptop in order to follow examples and exercises discussed during classes.
Teaching Resources
Course books, web sites, and software are freely available online (open access, open source) at no cost. Links are provided during the first class.
They are almost entirely in English.
They are almost entirely in English.
Assessment methods and Criteria
The exam is exclusively in written form with practical exercises requiring to use a personal computer and softwares employed during the course.
No intermediate exams are provided.
The evaluation will consider to what extent computational logic has been understood, the familiarity achieved with data analysis principles, and usage of software employed during classes.
No intermediate exams are provided.
The evaluation will consider to what extent computational logic has been understood, the familiarity achieved with data analysis principles, and usage of software employed during classes.
Educational website(s)
Professor(s)