Digital tecnologie for organisations

A.Y. 2021/2022
Overall hours
Learning objectives
The aim of the course is twofold: 1) to familiarize student with widely used professional technologies for the organization, analysis, and visualization of structured data; 2) to introduce at the logic and usage of sequences of commands and control constructs (scripting) for data analysis.

More specific objectives are:

1) Introduce students to data analysis for the Social Science and to open source technologies;
2) Learn principles of data analysis with R: R languages, libraries, RStudio;
3) Familiarize with principles of computational logic through command-line tools;
4) Learn the main phases of a data analysis: data tidying and data transformation operations;
5) Introduce to data visualization and to the main graph types (scatterplot, lineplot, bar chart, histogram, boxplot, marginals with variants) by means of the ggplot2 library;
6) Introduce to open data usage through exercises with public domain dataset of medium-low complexity;
7) Use of open format and online books and technical documentation in English.
Expected learning outcomes
A student should be able to recognize the meaning and the expected effects of command sequences for data organization, analysis, and visualization. She/he should also be able to code scripts corresponding to data selection, transformation, and visualization regarding predefined dataset. The ability to recognize and fix syntax and semantic errors produced in the command language usage is also required. Finally, the student should be able to discuss how predefined dataset could be analyzed together with expected outcomes and possible applications of the considered technology.
Course syllabus and organization

Single session

Lesson period
First trimester
Specific information on the delivery modes of training activities for the academic year 2021/2022 will be provided over the coming months, based on the evolution of the public health situation.
Course syllabus
1. Introduction to technologies for data analysis for the Social Sciences, Management, and Organizations
2. Open Data, Open Access, Open Source
3. R language and RStudio program
4. Data analysis process: data import, tidy, transformation and visualization
5. Data Tidy: discussion, functions, examples, exercises
6. Data Transformation: discussion, functions, examples, exercises
7. Data Visualization: discussion, functions, examples, exercises
8. Advanced cases (tips) and final notes
Prerequisites for admission
English reading and understanding: basic knowledge
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.
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.
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 and without SEB. The remote conferencing tool will be either Microsoft Teams or Zoom, depending on technical issues experienced with them (in particular, Teams exhibited problems suggesting to not adopting it).
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.
INF/01 - INFORMATICS - University credits: 6
Lessons: 40 hours
Professor: Cremonini Marco