STATISTICS AND DATA ANALYSIS LAB

Academic year
2026/2027 Syllabus of previous years
Official course title
STATISTICS AND DATA ANALYSIS LAB
Course code
ET1012 (AF:698231 AR:418683)
Teaching language
English
Modality
On campus classes
ECTS credits
0
Degree level
Bachelor's Degree Programme
Academic Discipline
NN
Period
3rd Term
Course year
3
Where
VENEZIA
The laboratory is part of the study programme as an applied activity aimed at developing basic practical skills in the use of computational tools for statistics and data analysis. The course introduces students to R and Python, with a specific focus on data management, exploration and preliminary analysis, strengthening quantitative skills useful for further academic work.
By the end of the laboratory, students will be able to understand the basic logic of R and Python, use working environments for data analysis, import datasets in different formats, perform simple data cleaning and organization tasks, produce basic descriptive statistics and create initial graphical representations. Students will also become familiar with a reproducible approach to data analysis.
A basic knowledge of descriptive statistics is strongly recommended.
The laboratory introduces the use of R and Python for basic statistical analysis. Topics include: an overview of working environments, the logic of programming languages and scripts, importing data from common file formats, organizing and inspecting datasets, managing variables and observations, calculating descriptive statistics, producing basic tables and graphs, guided examples of exploratory analysis and an introduction to reproducible data analysis workflows.
The main reference materials are the slides, R and Python scripts, datasets and exercises provided by the instructor during the laboratory. For further study on using R to import, transform and visualize data, students may consult R for Data Science, 2nd edition, which is also available online. For data analysis in Python, with particular attention to pandas and exploratory computing tools, students may consult Python for Data Analysis, 3rd edition, also available as an open access HTML version. Additional quick-reference resources include the official pandas documentation and the Posit/RStudio cheat sheets.
No formal assessment or final exam is scheduled; attendance and classroom activities are intended exclusively as practical, applied and formative learning experiences.
not present

The instructor is responsible for ensuring the authenticity and originality of all examinations and coursework. In cases of suspected academic misconduct, an additional on-site assessment may be required during the exams, which may differ from the standard format.

No grading scale is provided, as the laboratory does not include an examination.
Introductory lectures, computer-based guided demonstrations, individual practical exercises and discussion of applied examples.
The laboratory is also intended to support students who plan to write a quantitative thesis, providing basic tools for data management, organization and preliminary analysis.
Definitive programme.
Last update of the programme: 12/06/2026