Data science per le organizzazioni
A.Y. 2025/2026
Learning objectives
Undefined
Expected learning outcomes
Undefined
Lesson period: First trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
Course syllabus
1. Introduction to data science
2. Open Data, Open Access, Open Source
3. R language and RStudio
4. Data Wrangling operations
5. Data import and main transformation operations
6. Date, strings, and missing values operations
7. Groups and aggregation operations
8. Functions and multicolumn operations
9. Join operation between data frames
10. Operations on lists
The study will be strongly focused on exercising with case studies from Open Data publicly available, in addition to more didactic exercises from books and teaching material used for the course. Data and exercises from Open Data will be both discussed during classes and left as autonomous homework. Completing numerous exercises is indispensable for the required preparation.
2. Open Data, Open Access, Open Source
3. R language and RStudio
4. Data Wrangling operations
5. Data import and main transformation operations
6. Date, strings, and missing values operations
7. Groups and aggregation operations
8. Functions and multicolumn operations
9. Join operation between data frames
10. Operations on lists
The study will be strongly focused on exercising with case studies from Open Data publicly available, in addition to more didactic exercises from books and teaching material used for the course. Data and exercises from Open Data will be both discussed during classes and left as autonomous homework. Completing numerous exercises is indispensable for the required preparation.
Prerequisites for admission
English reading and understanding: basic knowledge needed for tools, data, and part of the documentation.
Basic usage of a personal computer and of the internet (e.g., file and directory creation and management, rules for file naming, program installation, browser and online search usage, etc.).
Basic usage of a personal computer and of the internet (e.g., file and directory creation and management, rules for file naming, program installation, browser and online search usage, etc.).
Teaching methods
Classes are in person and include several practical examples. For this reason, it could be useful to bring a laptop in order to follow examples and exercises discussed during classes.
Some additional exercises will be taught by the course tutor. Note that these are extra hours in addition to 40 hours of the course, they will not include new contents with respect to the official program, and therefore they are not mandatory for the exam preparation. However, they are a useful learning support for several students.
Some additional exercises will be taught by the course tutor. Note that these are extra hours in addition to 40 hours of the course, they will not include new contents with respect to the official program, and therefore they are not mandatory for the exam preparation. However, they are a useful learning support for several students.
Teaching Resources
TEXTBOOK
FONDAMENTI DI DATA SCIENCE - Python, R e OpenData
Marco Cremonini, Egea Editore, Giugno 2023. ISBN/EAN: 9788823823501
https://www.egeaeditore.it/ita/prodotti/ict-e-sistemi-informativi/fondamenti-di-data-science.aspx
Of this book, we will use sections dedicated to the R language.
This textbook will be used also for the Data Visualization per il Management course in the 2nd year (ex-Organizzazioni e Società Digitali).
FONDAMENTI DI DATA SCIENCE - Python, R e OpenData
Marco Cremonini, Egea Editore, Giugno 2023. ISBN/EAN: 9788823823501
https://www.egeaeditore.it/ita/prodotti/ict-e-sistemi-informativi/fondamenti-di-data-science.aspx
Of this book, we will use sections dedicated to the R language.
This textbook will be used also for the Data Visualization per il Management course in the 2nd year (ex-Organizzazioni e Società Digitali).
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.
Professor(s)