Unit DATA journalism
- Course
- Communication studies
- Study-unit Code
- A006362
- Location
- PERUGIA
- Curriculum
- In all curricula
- Teacher
- Maria Giovanna Ranalli
- Teachers
-
- Maria Giovanna Ranalli
- Hours
- 56 ore - Maria Giovanna Ranalli
- CFU
- 8
- Course Regulation
- Coorte 2026
- Offered
- 2026/27
- Learning activities
- Affine/integrativa
- Area
- Attività formative affini o integrative
- Sector
- STAT-01/A
- Type of study-unit
- Opzionale (Optional)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- English
- Contents
- Data sources: how to interpret data, reconstruct and evaluate data quality.
Data analysis: how to extract and synthesize information from micro- and macro-level data. Tables and graphs, analysis of the evolution of macro-phenomena over time.
Computer lab for data research and data visualization.
Focus on crime statistics, electoral and public opinion surveys. - Reference texts
- Measuring Crime – Sharon Lohr, CRC Press, 2019
Scientific reports and articles provided by the Instructor - Educational objectives
- The course focuses on the reading, analysis, interpretation, and visualization of data from a variety of sources. It also examines the ways in which information and journalism make use of these forms of communication. The course provides the basic tools needed to critically assess the quality of information derived from surveys, polls, and big data. It also equips students with fundamental computational tools for independently analyzing data, extracting the necessary information, and effectively constructing, visualizing, and communicating quantitative information.
By the end of the course, students will be able to understand and interpret data used in journalistic and communication contexts; recognize the strengths and limitations of these tools; integrate data and visualizations into journalistic and argumentative writing; and adopt appropriate methods of data visualization and presentation. - Prerequisites
- All the required technical tools of data analysis will be introduced in the course. The knowledge of some simple tools of mathematics, such as proportions, percentages and the Cartesian plane are useful prerequisites, although non essential.
- Teaching methods
- Face-to-face lectures and computer lab sessions. The slides shown during the lectures along with practical cases analyzed and the data used in class and in the lab are made available on the Unistudium website of the course.
- Other information
- Teaching materials and detailed information are available on the course website on the Unistudium platform
- Learning verification modality
- The assessment method differs for attending students and non-attending students.
For attending students, a final course assessment is required. The assessment consists of the presentation and discussion of a data journalism project selected in agreement with the instructor. The project is designed to cover both theoretical issues and more technical aspects, through practical examples of real-world data analysis and visualization. The former are intended to evaluate students’ knowledge of analytical methods and tools, while the latter assess their ability to select and apply these methods and tools to solve concrete problems.
The assessment is considered passed with a score of 16 or higher. Given the computer-lab activities offered to attending students, the final evaluation also includes a number of homework assignments. The final grade is calculated by combining the results of these assessments with the following weights: Homework: 50%, final project: 50%
For non-attending students, there is an oral examination aimed at assessing their knowledge of analytical methods and tools, as well as their ability to select and use these methodologies and tools to solve practical problems.
For information on support services for students with disabilities and/or specific learning disorders (SLD), please visit the relevant page on the University of Perugia website. - Extended program
- Different data sources – focus on official statistics and opinion polls; how data are collected in official surveys and in electoral and public opinion polls. Probability and non-probability surveys, web surveys, online panels. Evaluating the quality of data.
Data analysis.Tables. Analysis of phenomena over time.
Data visualization – Datawrapper, Flourish, Excel.
Case studies: crime statistics, corruption indicators, electoral and public opinion polls in the United States and the United Kingdom, the experience of The Crunch newsletter (The Guardian), socio-economic indicators from official sources. - Obiettivi Agenda 2030 per lo sviluppo sostenibile