Class Visual and Data Journalism

Visual and data journalism is a cross-disciplinary field that is indispensable to the contemporary media landscape, driving new forms of investigation, rigour and public service within newsrooms. This Course Module (CM) addresses the digital demands of the labour market, equipping students with critical and technical skills in the fields of data literacy and digital storytelling. Its relevance lies in equipping future communication professionals with skills in quantitative analysis and investigative rigour, using open-source software and artificial intelligence code-assistance tools. The course develops the ability to transform complex databases into narratives of informational value, culminating in the publication of multimedia and interactive reports tailored to digital platforms.

Not applicable

1. Fundamentals and Critical Reading 1.1. The role of data journalism and investigative journalism in newsrooms 1.2. Data mindset, hypotheses, computational thinking and news value 1.3. Analysis of opinion polls, sampling, margin of error and biases 1.4. Statistical literacy and errors of representation in the media 2. Data Access and Processing with R 2.1. Access to information, open data and public repositories 2.2. APIs and web scraping: a conceptual overview 2.3. RStudio: reading, manipulating and cleaning data 2.4. Applied descriptive statistics 3. Visualisation, storytelling and AI 3.1. Information design, accessibility and interactive graphics/maps (low/no-code) 3.2. AI as a coding and analysis assistant 3.3. Triangulation, data verification and ethics 4. Multimedia Production 4.1. Journalistic narrative and the integration of interactive visuals on the web 4.2. Project-based learning (PBL) in the newsroom

The course module adopts an active, problem-based learning (PBL) approach, combining the flipped classroom model - in which students explore theoretical concepts in advance - with collaborative work in a multimedia writing context. The teaching of coding and visualisation is based on the hands-on coding model, through which students carry out data analysis in R and build interactive visualisations during laboratory sessions. Critical reflection is fostered through the presentation and analysis of leading international case studies, culminating in a peer assessment process that includes public discussion of the work produced and the use of peer assessment mechanisms to evaluate individual contributions to the practical group project.

Bournegru, L., Chambers, L., & Gray, J. (Eds.). (2012). The Data Journalism Handbook 1. European Journalism Centre and O'Reilly Media. Cairo, A. (2019). How Charts Lie: Getting Smarter about Visual Information. W. W. Norton & Company Garcia, R., Rosa, M. J. V., & Barbosa, L. (2017). Que número é este? Um guia sobre estatísticas para jornalistas. PORDATA - Fundação Francisco Manuel dos Santos. Gray, J., & Bournegru, L. (Eds.). (2021). The Data Journalism Handbook 2. Towards a Critical Data Practice. Amsterdam University Press. Porlezza, C. (2024). The datafication of digital journalism: A history of everlasting challenges between ethical issues and regulation. Journalism, 25(5), 1167-1185. https://doi.org/10.1177/14648849231190232 Wickham, H., & Grolemund, G. (2023). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (2nd ed.). O'Reilly Media. https://pt.r4ds.hadley.nz