Organisations and Digital Societies
A.Y. 2020/2021
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
Teaching
Classes will be provide online, both as synchronous and asynchronous modes. Zoom is the videoconferencing tool of choice. Videorecordings are available on the Ariel web site of the course.
Syllabus and teaching material
No variations have been introduced.
Exams and evaluation criteria.
The exam is exclusively in written form with practical exercises requiring to use a personal computer and softwares employed during the course.
Exams will be provided accordingly to university's dispositions regarding written exams. If it will be possible to organize them in presence (for all students or only part of them), they will take place in Unicloud rooms equipped with personal computers already configured as needed. For online exams, instructions already available will be followed regarding online written exams using Exam.net and without SEB. The remote conferencing tool will be Zoom.
No intermediate exams are provided, evaluation criteria are unchanged.
Classes will be provide online, both as synchronous and asynchronous modes. Zoom is the videoconferencing tool of choice. Videorecordings are available on the Ariel web site of the course.
Syllabus and teaching material
No variations have been introduced.
Exams and evaluation criteria.
The exam is exclusively in written form with practical exercises requiring to use a personal computer and softwares employed during the course.
Exams will be provided accordingly to university's dispositions regarding written exams. If it will be possible to organize them in presence (for all students or only part of them), they will take place in Unicloud rooms equipped with personal computers already configured as needed. For online exams, instructions already available will be followed regarding online written exams using Exam.net and without SEB. The remote conferencing tool will be Zoom.
No intermediate exams are provided, evaluation criteria are unchanged.
Course syllabus
1. Introduction to Python technologies for data analysis
2. Open Data, Open Access, Open Source
3. Jupyter Notebook as tools for executing scripts and presenting textual contents
4. Introduction to IPython
5. Data as arrays and matrices (NumPy library): discussion, functions, examples, exercises
6. Dataset usage and transformation (Pandas library): discussion, functions, examples, exercises
7. Data Visualization (Matplotlib and Seaborn libraries): discussion, functions, examples, exercises
8. Case studies and exercises with real open data: collecting data, definition of objectives of analysis, discussing the meaning of results
9. Advances of Data Visualization: principles of communication through plots produced by data analysis
10 Examples of data visualization: successes and failures
2. Open Data, Open Access, Open Source
3. Jupyter Notebook as tools for executing scripts and presenting textual contents
4. Introduction to IPython
5. Data as arrays and matrices (NumPy library): discussion, functions, examples, exercises
6. Dataset usage and transformation (Pandas library): discussion, functions, examples, exercises
7. Data Visualization (Matplotlib and Seaborn libraries): discussion, functions, examples, exercises
8. Case studies and exercises with real open data: collecting data, definition of objectives of analysis, discussing the meaning of results
9. Advances of Data Visualization: principles of communication through plots produced by data analysis
10 Examples of data visualization: successes and failures
Prerequisites for admission
English reading and understanding: basic knowledge
Knowledge of basic principles of computational logic and usage of technologies for data analysis
Suggested to have followed the course of Tecnologie Digitali per le Organizzazioni
Knowledge of basic principles of computational logic and usage of technologies for data analysis
Suggested to have followed the course of Tecnologie Digitali per le Organizzazioni
Teaching methods
Classes are in person and it is suggested 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, first, to what extent computational logic has been understood, the familiarity achieved with data analysis principles, and usage of software employed during classes. Secondly, it will be evaluated the knowledge of data visualization principles and communication through graphical results.
No intermediate exams are provided.
The evaluation will consider, first, to what extent computational logic has been understood, the familiarity achieved with data analysis principles, and usage of software employed during classes. Secondly, it will be evaluated the knowledge of data visualization principles and communication through graphical results.
Professor(s)