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C8352 - SOFTWARE FOR DATA ANALYSIS

Academic Year 2026/2027

                
                        Docente:
                        Edoardo Redivo
                    
                        Credits:
                        4
                    
                        Language:
                        English
                    
                        Teaching Mode:
                        In-person learning (entirely or partially)
                        
                            Campus:
                            Bologna
                        
                            Corso:
                            Second cycle degree programme (LM) in
                            Statistical Sciences (cod. 6810)

                                    Course Timetable
                                
from Sep 14, 2026 to Oct 22, 2026

Learning outcomes

By the end of the course, students will be able to: • use statistical software tools to import, clean, transform and analyze data; • implement standard workflows for data exploration, modeling and reporting in a reproducible way; • apply software functionalities to case studies in data analysis; • present analytical results clearly and effectively, through appropriate summaries, tables and data visualizations.

Course contents

This course page is preliminary and may be updated before the course.

Data-analysis workflows: scripts, file paths, project organization, and reproducibility.
Comparative overview of R and Python for statistical computing.
Basic data structures in R and Python: vectors, lists, data frames, arrays, and missing values.
Data import and data cleaning.
Data manipulation with tidyverse in R and pandas/polars in Python.
Data visualization with ggplot2.
Reproducible reporting with Quarto/Jupyter.

Readings/Bibliography

R for Data Science, 2nd edition, H. Wickham, M. Çetinkaya-Rundel, and G. Grolemund, 2023.
https://r4ds.hadley.nz/

Advanced R, 2nd edition, H. Wickham, 2019.
https://adv-r.hadley.nz/

An Introduction to Statistical Learning with Applications in Python, 1st edition, G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor, 2023.
https://www.statlearning.com/

Teaching methods

Lectures, coding demonstrations, and hands-on computer lab sessions.

To participate in computer lab sessions, students must complete Modules 1 and 2 of health and safety training, available as online courses.

Assessment methods

Assessment will consist of a pass/fail written exam designed to test understanding of the main course concepts, including data-analysis workflows, data manipulation and visualization, and comprehension of code in both R and Python. The exam will consist of multiple-choice questions and short open-ended questions.

Teaching tools

Slides, notes, and scripts that will be made available on Virtuale.

Office hours

See the website of Edoardo Redivo