Digital Tecnologie for Organisations
A.Y. 2021/2022
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.
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
Lesson period: Open sessions
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
Course currently not available
INF/01 - INFORMATICS - University credits: 6
Lessons: 40 hours