Data Journalism
Code: 104991Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Journalism | OB | 3 |
Contact lecturer
- Name :
- Santiago Giraldo Luque
- Email :
- santiago.giraldo@uab.cat
Teaching staff
- Alessandro Bernardi
- Mireia Camacho Corrales
Group languages
You can consult this information at the end of the document.
Prerequisites
The course is in the area of research in journalism and communication. Therefore, it requires the student to know in advance the handling of information sources. Students must also know the basic principles of journalistic writing and the structure of journalistic genres in different formats, as well as know how to use different tools for journalistic production in the digital environment.
Objectives
The general objective of the Data Journalism course is to develop students' criteria and skills for the world of data journalism through the understanding and execution of processes linked to the search, extraction, analysis and visualisation of data.
The course, which emphasises in open information, introduces different methods of data analysis, factchecking and processing that can be applied to everyday journalism practices such as developing stories, interpreting a database, contextualising information and the interactive presentation of news genres.
The course also has the following specific objectives:
1. To make an approach to the concepts of Big Data, Open Data and Data Journalism as trends and realities in the generation of information and as a path for the generation of added value to communicative processes.
2. To train students in the management of data collection, transformation, analysis, interpretation and presentation applications.
3. To provide students with practical tools for interpreting databases based on structured information.
4. To orientate participants' skills towards the management and exploration of databases and information within open data channels, as well as from their own database constructions.
5. Encourage students to use tools for searching, collecting, analysing and visualising data, using techniques currently employed by the media.
Learning outcomes
- CM14 (Design journalistic investigation projects based on data, and ensure their methodological rigour and news impact.) Design journalistic investigation projects based on data, and ensure their methodological rigour and news impact.
- KM15 (Identify the journalistic tools and techniques related to data and journalistic documentation, as well as their impact on news production.) Identify the journalistic tools and techniques related to data and journalistic documentation, as well as their impact on news production.
- SM15 (Use data collection and analysis techniques to develop rigorous, evidence-based journalistic investigations.) Use data collection and analysis techniques to develop rigorous, evidence-based journalistic investigations.
- SM16 (Assess the truthfulness and reliability of sources in the process of journalistic investigation and ensure the quality of the information.) Assess the truthfulness and reliability of sources in the process of journalistic investigation and ensure the quality of the information.
- SM17 (Develop fact-checking methodologies for news and include digital tools and principles of transparency.) Develop fact-checking methodologies for news and include digital tools and principles of transparency.
Contents
Unit 1. The data society: Introduction to the course in which the digital society is contextualised and the economic and political universe of the data society is presented.
Unit 2. Data Journalism: Presentation of the concept, history and foundations of data journalism in contemporary newsrooms. At the same time, the student is introduced to the processes and roles involved in a data journalism project, as well as to the new journalistic genres associated with data.
Unit 3. Data sources and data capture: Introduction to open data sources, the processes of accessing and requesting public information and transparency laws. Beginning the process of searching, downloading and storing different types of data (formats).
Unit 4. Data processing and analysis: Handling of data cleaning and analysis tools and functions to find journalistic stories in information.
Unit 5. Storytelling with data: Building the script of a journalistic story from data: What to show? How to show it? and with what resources and tools?
Unit 6. Data visualisation: Presentation of data visualisation tools for journalistic stories based on different representations and interaction possibilities.
Unit 7. Data mapping: Presentation of different tools and possibilities of cartographic representation of information for data-driven news stories.
Unit 8. Factcheck: Presentation of methods and practices of the information verification.
(*) A detailed schedule outlining the content of each session will be presented on the first day of the course and will be available on the course’s Virtual Campus, where students will find the various teaching materials deemed appropriate by the instructors and necessary information for effective course monitoring.
The content of this subject will be sensitive to aspects related to the gender perspective.
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Workshops | 50 | 2 | CM14, KM15, SM15, SM16, SM17 |
| Autonomous work: reading and following tutorials | 38 | 1.52 | CM14, KM15, SM15, SM16, SM17 |
| Theoretical sessions | 15 | 0.6 | KM15, SM15, SM17 |
| Laboratory | 30 | 1.2 | CM14, KM15, SM15, SM16, SM17 |
The structure of the course, which includes various practical activities, aims to foster the internalization of skills related to the four main processes involved in data journalism (data searching, extraction, analysis, and publication). At the same time, it seeks to develop critical self-awareness among students about the datafied society. The methodology is entirely practical. Through lab activities, workshops, and the final evaluation, both the theoretical component and the practical application of the content studied are assessed. The goal is to evaluate the learning progression through a range of different practical tasks.
The continuous assessment of the course, which includes ongoing practical work, allows for close monitoring of the student’s learning and progress. Likewise, knowledge is acquired progressively, with each step building upon the previous one and applied in subsequent exercises.
The Data Journalism course includes four types or categories of learning activities:
Theoretical classes: sessions in which the teaching staff introduces key concepts related to data journalism and the use of spreadsheets and other visualization tools.
Laboratory practice: individual or team work consisting of hands-on activities with a specific deliverable and time limit. Students must apply their knowledge, manage their time, and complete assignments during class hours and the allocated practical time, under the guidance of the instructor.
In-class practice: short individual or team tasks conducted in large theory groups to assess the acquisition of basic skills related to data extraction, cleaning, and analysis.
Project work: development of a final project based on challenge-based learning (CBL), which will be explained on the first day of class.
The instructors may inform students that, in order to ensure the proper functioning of the class and to maintain a respectful environment in the classroom, electronic devices or screens may not be used during sessions, except when otherwise indicated for specific teaching purposes.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Final exam and factcheck exercises | 20% | 1 | 0.04 | SM15, SM16, SM17 |
| Multimedia project | 20% | 6 | 0.24 | CM14, KM15, SM15, SM16, SM17 |
| Laboratory | 60% | 10 | 0.4 | CM14, KM15, SM15, SM16, SM17 |
The assessment activities are as follows:
Activity A: Practice 1, which accounts for 5% of the final grade
Activity B: Practice 2, which accounts for 10% of the final grade
Activity C: Practice 3, which accounts for 5% of the final grade
Activity D: Practice 4, which accounts for 20% of the final grade
Activity E: Practice 5, which accounts for 20% of the final grade
Activity F: Final exam and fact-checking exercises, which together account for 20% of the final grade
Activity G: Multimedia project, which accounts for 20% of the final grade
To pass the course, students must achieve a minimum passing grade (5.0) in both the set of practical activities (weighted average of Activities A, B, C, D, and E) and the final exam.
Reassessment: In the last two weeks of the course, students who have not passed will have the opportunity to take a resit exam, consisting of a theoretical test and a practical exercise. The mandatory condition to qualify for the resit is to have completed at least 2/3 of the total course assignments (Activities A–G) and to have obtained a final average grade equal to or higher than 3.5 (and lower than 5).
Single assessment
This course does not offer the single assessment system.
Plagiarism
The performance of any irregularity in an evaluation act (academic fraud, plagiarism or improper use of AI, unless this use is expressly authorized by the teaching guide), which may lead to a significant variation in the grade, assumes that this act will be graded with a 0. In the event that the teaching guide foresees that in order to pass the subject it is an essential requirement to have obtained a minimum grade in this evaluation act or that there are several irregularities in the evaluation acts of the same subject, the final grade of this subject is 0. Apart from this, a disciplinary process may be instructed to the student that incurs any of these irregularities.
Use of Artificial Intelligence
The use of Artificial Intelligence (AI) technologies is allowed in this course as part of the work process, provided that the final outcome reflects a significant contribution by the student in terms of personal analysis and reflection. Students must clearly identify the parts generated using AI, specify the tools used, and include a critical reflection on how these tools influenced the process and the final result. Failure to transparently disclose the use of AI in any assessable activity will be considered academic dishonesty and may result in partial or total penalties to the grade, or more serious sanctions in severe cases.
Any student suspected of submitting assignments that have been generated by AI, written by others or copied; include unattributed AI-generated content, or fall outside the permitted scope, may be asked to provide the preliminary work or other materials that can demonstrate it is original work and the result of their own authorship. They may also be asked to separately explain or justify their work. Teachers may also use AI detection systems or carry out any verification tasks they deem appropriate. If, after review, the instructor detects irregularities, the assignment may be graded zero, and the student may be subject to further disciplinary action.
Bibliography
Abad, Jimena. (2015). Periodismo de datos: informar en la era digital. Entrevista a Florencia Coelho. Revista Dixit, 22, 58-62
Alcalde, Ignasi. (2015). Visualización de la información. De los datos al conocimiento. Editorial UOC.
Bounegru, Liliana; Chambers, Lucy; Gary, Jonathan. (Eds.). (2020). The Data Journalism Handbook II. Towards a Critical Data Practice. European Journalism Centre and Google News Initiative. https://datajournalism.com/read/handbook/two
Bounegru, Liliana; Chambers, Lucy; Gary, Jonathan. (Eds.) (2012). The Data Journalism Handbook: How Journalists Can Use Data to Improve the News, O’Reilly Media. https://datajournalism.com/read/handbook/one
Bradshaw, Paul. (2017). Scraping for Journalists. How to grab information from hundreds of sources, put it in data you can interrogate - and still hit deadlines (2nd edition). Leanpub
Bradshaw, Paul. (2019). Finding Stories in Spreadsheets. Recipes for interviewing data - and getting answers. Leanpub
Bradshaw, Paul; Maseda, Bárbara. (2015). Periodismo de datos: Un golpe rápido. Cómo entrar, obtener los datos, escabullirse con la noticia… ¡Y asegurarse de que nadie salga herido! Leanpub.
Bradshaw, Paul; Lee, Maggie; Panuccio, Erika; Aldhous, Peter. (2018). Data scraping for stories. Conversations with Data: #12. https://datajournalism.com/read/newsletters/data-scraping-for-stories
Cairo, Alberto. (2017). ¿Visualización de datos: una imagen puede valer más que mil números, pero no siempre más que mil palabras. El profesional de la información, 26(6), 1025-1028.
Carlberg, Conrad. (2011). Análisis estadístico con Excel. Anaya.
Charte Ojeda, Francisco. (2016). Excel 2016. Anaya.
Crucianelli, Sandra. (2013). ¿Qué es el periodismo de datos? Cuadernosde Periodistas, 26. APM. https://www.cuadernosdeperiodistas.com/que-es-el-periodismo-de-datos/
De Vega, Javier. (2013). Periodista, pregúntate qué puede hacer una buena Ley de Transparencia por ti, Fundación Civio. https://civio.es/tu-derecho-a-saber/2013/06/19/periodista-preguntate-que-puede-hacer-una-buena-ley-de-transparencia-por-ti/
Elías, Carlos. (2015). Big data y periodismo en la sociedad red. Síntesis.
Fama, Andrea. (2011). Open data. Data Journalism. Transparenza e informazione al servicio delle societá nell’ era digitale. Narccisa.
Ferreras Rodríguez, Eva María. (2013). Aproximación teórica al perfil profesional del Periodista de Datos. Revista Icono 14, 11(4), 467-481.
Ferreras Rodríguez, Eva María. (2016). El periodismo de Datos en España. Estudios sobre el Mensaje Periodístico, 22(1), 255-272.
Ferrer-Sapena, Antonia; Sánchez-Pérez, Enrique. (2013). Open data, big data: ¿Hacia dónde nos dirigimos? Anuario ThinkEPI, 7, 150-156.
Flores Vivar, Jesús Miguel; Salinas Aguilar, Cecilia. (2014). “El periodismo de datos como especialización en los medios generalistas con presencia en Internet”. En: Esteve Ramírez, Francisco; Nieto Hernández, Juan Carlos. (Eds.). Nuevos retos del Periodismo Especializado. Editorial Schedas. pp. 241-260.
Flores Vivar, Jesús Miguel;Carrasco Polaino, Rafael. (2020). “Periodismo de datos y visualización”. En: Sotelo González, Joaquín; Martínez Arias, Santiago. (Eds.). Periodismo y nuevos medios. Perspectivas y retos. Gedisa.
Fernández-Rovira Cristina; Giraldo-Luque, Santiago. (2021). La felicidad privatizada. Monopolios de la información, control social y ficción democrática en el siglo XXI. Editorial UOC.
Fernández-Rovira Cristina; Giraldo-Luque, Santiago (Eds.) (2022). Predictive Technology in Social Media. CRC Press. Taylor & Francis Group.
Fernández-Rovira Cristina; Giraldo-Luque, Santiago (2024). Segrestats per les xarxes. Per què els adolescents d'avui tenen menys temps, menys salut i estàn més sols. Eumo Editorial.
Giraldo-Luque Santiago; Fernández-Rovira Cristina. (2021) Economy of Attention: Definition and Challenges for the Twenty-First Century. En: Park S.H., Gonzalez-Perez M.A., Floriani D.E. (Eds.). The Palgrave Handbook of Corporate Sustainability in the Digital Era. Palgrave Macmillan. pp. 283-305.
Grassler, Marjorie. (2017). El rol del periodista de datos en el proceso de los sistemas de gestión y de decisión pública y en la recuperación de la confianza entre el ciudadano y las instituciones públicas. Tesis doctoral. Programa de Doctorado en Comunicación y Periodismo. Universidad Autónoma de Barcelona.
Herrero-Solana, V., Rodríguez-Domínguez, A.M. (2015). Periodismo de datos, infografía y visualización de la información: un estudio de El País, El Mundo, Marca y El Correo. BiD: Textos universitaris de biblioteconomia i documentació, 34.
Hidalgo, David; Torres, Fabiola. (2016). La navaja suiza del reportero. Herramientas de investigación en la era de los datos masivos. Ojo Público – Consejo de la Prensa Peruana.
La-Rosa, Leonardo; Sandoval-Martín, Teresa. (2016). La insuficiencia de la Ley de Transparencia parael ejercicio del Periodismo de datos en España. Revista Latina de Comunicación Social, 71, 1208-1229
López-García, Xose; Toural-Bran, Carlos; Rodríguez-Vázquez, Ana Isabel. (2016). Software, estadística y gestión de bases de datos en el perfil del periodista de datos, El profesional de la información, 25(2), 286-294.
Mayer-Schönberger, Viktor; Cukier, Kenneth. (2013). Big data. La revolución de los datos masivos. Turner.
O’Neil, Cathy. (2017). Armas de destrucción matemática. Cómo el Big Data aumenta la desigualdad y amenaza la democracia. Capitan Swing.
Patino, Bruno. (2020). La civilización de la memoria de pez. Pequeño tratado sobre el mercado de la atención. Alianza.
Pérez-Montoro, Mario. (2016). Visualización de información en cibermedios. Anuario ThinkEPI, 10.
Renó, Luciana; Saad, Elizabeth. (2017). Reportaje soportado por el computador: procedimientos y tecnología para el periodismo de datos contemporáneo. Razón y Palabra, 21(2_97), 128-141
Sánchez-Bonvehí, Claudia; Ribera, Mireia. (2014). Visualización de la información en la democratización de los datos: propuestas desde el periodismo y la narratividad. El profesi
Tascón, Mario. (2013). Introducción. Big Data. Pasado, presente, futuro. Telos: Cuadernos de comunicación e innovación, 95, 47-50.
Turing, Alan Mathison. (1974). ¿Puede pensar una máquina? Universidad de Valencia.
Urbano, Patrick. (2019). Manual de autodefensa jurídica para periodistas. Cómo conocer y ejercer tus derechos. UOC Editorial.
Varios Autores. (2015). Manual de periodismo de datos iberoamericano. HIVOS, International Center for Journalists (ICFJ) y la Escuela de Periodismo de la Universidad Alberto Hurtado de Chile. http://manual.periodismodedatos.org/index.php
Software
As this is a completely practical course, the software required is the usual one for the journalistic tasks of content production in different formats.
Specifically, the following tools are required:
Audiovisual editing software: DaVinci Resolve.
Audio editing software: Audacity
Text editing software: Word or similar
Data analysis software: Excel or similar
Data visualisation software: Infogram - Datawrapper - Flourish
Multimedia editing software: Wordpress - Blogger - Wix
The Faculty also has cameras and other equipment available for the correct performance of journalistic practices.
As the subject will carry out practical sessions during all its activities, it is recommended that students (if possible) always bring their laptop to the sessions.
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (TE) Theory | 1 | Spanish | second semester | morning-mixed |
| (TE) Theory | 2 | Spanish | second semester | morning-mixed |
| (PLAB) Practical laboratories | 11 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 12 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 13 | Spanish | second semester | morning-mixed |
| (PLAB) Practical laboratories | 21 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 22 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 23 | Spanish | second semester | morning-mixed |