Data Journalism
Code: 104991 ECTS Credits: 6| Degree | Type | Year |
|---|---|---|
| Journalism | OB | 3 |
Contact
- Name:
- Santiago Giraldo Luque
- Email:
- santiago.giraldo@uab.cat
Teachers
- Alessandro Bernardi
- Jordi Badia Perea
- Mireia Camacho Corrales
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
The course is in the area of theory and practice of journalism and accompanies the subject of Sources, Techniques and Organisation of Journalistic Work. 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 and Contextualisation
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.
Competences
- Abide by ethics and the canons of journalism, as well as the regulatory framework governing information.
- Design the formal and aesthetic aspects in print, graphic, audiovisual and digital media, and use computer-based techniques to represent information using infographic and documentary systems.
- Introduce changes in the methods and processes of the field of knowledge to provide innovative responses to the needs and demands of society.
- Relay journalistic information in the language characteristic of each communication medium, in its combined modern forms or on digital media, and apply the genres and different journalistic procedures.
- Research, select and arrange in hierarchical order any kind of source and useful document to develop communication products.
- Show leadership, negotiation and team-working capacity, as well as problem-solving skills.
- Students can apply the knowledge to their own work or vocation in a professional manner and have the powers generally demonstrated by preparing and defending arguments and solving problems within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must be capable of communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
- Students must develop the necessary learning skills in order to undertake further training with a high degree of autonomy.
- Take sex- or gender-based inequalities into consideration when operating within one's own area of knowledge.
Learning Outcomes
- Adapt the presentation of the news to the needs established by the editorial space.
- Analyse the sex- or gender-based inequalities and the gender biases present in one's own area of knowledge.
- Communicate using language that is not sexist or discriminatory.
- Describe the workings of editorial boards.
- Handle information facts in the most appropriate journalistic genre.
- Identify situations in which a change or improvement is needed.
- Properly comment on and edit texts or other media productions related to journalism.
- Propose new methods or well-founded alternative solutions.
- Propose new ways to measure the success or failure of the implementation of innovative proposals or ideas.
- Recognise the potential and limits of freedom of speech in appraising information processes.
- Research information sources, select them and apply critical appraisal criteria.
- Research, select and arrange in hierarchical order any kind of source and useful document to develop communication products.
- Show leadership, negotiation and team-working capacity, as well as problem-solving skills.
- Students can apply the knowledge to their own work or vocation in a professional manner and have the powers generally demonstrated by preparing and defending arguments and solving problems within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must be capable of communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
- Students must develop the necessary learning skills in order to undertake further training with a high degree of autonomy.
- Write news, articles and reports with their respective sub-genres.
Content
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.
(*) The calendar will be available on the first day of class. Students will find all information on the Virtual Campus: the description of the activities, teaching materials, and any necessary information for the proper follow-up of the subject. In case of a change of teaching modality for health reasons, teachers will make readjustments in the scheduleand methodologies.
The calendar will be available on the first day of class. Students will find all information on the Virtual Campus: the description of the activities, teaching materials, and any necessary information for the proper follow-up of the subject. Should the teaching modality change for reasons of force majeure according to the competent authorities, the teaching staff will inform students of any modifications to the course schedule and teaching methodologies.
The content of this subject will be sensitive to aspects related to the gender perspective.
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Theoretical sessions | 15 | 0.6 | 2, 12, 7, 3, 18, 17, 16, 15 |
| Type: Supervised | |||
| Laboratory | 30 | 1.2 | 1, 2, 11, 12, 7, 3, 13, 4, 18, 6, 8, 9, 17, 16, 14, 15, 10, 5 |
| Workshops | 50 | 2 | 1, 2, 11, 7, 3, 13, 4, 18, 6, 8, 9, 17, 16, 14, 15, 10, 5 |
| Type: Autonomous | |||
| Autonomous work: reading and following tutorials | 38 | 1.52 | 1, 2, 11, 12, 7, 3, 13, 4, 18, 6, 8, 9, 17, 16, 14, 15, 10, 5 |
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 three 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.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Final exam and factcheck exercises | 20% | 1 | 0.04 | 1, 11, 7, 3, 4, 18, 17, 16, 14, 15, 10, 5 |
| Laboratory | 60% | 10 | 0.4 | 1, 2, 11, 12, 7, 3, 13, 4, 18, 6, 8, 9, 17, 16, 14, 15, 10, 5 |
| Multimedia project | 20% | 6 | 0.24 | 1, 11, 12, 7, 3, 18, 16, 14, 15, 5 |
The assessment activities are as follows:
Activity A: Practice 1, which accounts for 10% of the final grade
Activity B: Practice 2, which accounts for 10% of the final grade
Activity C: Practice 3, which accounts for 10% of the final grade
Activity D: Practice 4, which accounts for 15% of the final grade
Activity E: Practice 5, which accounts for 15% 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
Any student who commits academic misconduct (copying, plagiarism, impersonation, etc.) will receive a mark of 0 for the affected assessment activity. In the case of multiple instances of misconduct, the final grade for the course will be 0.
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.
Bibliography
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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
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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.
Groups and Languages
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
| Name | Group | Language | Semester | Turn |
|---|---|---|---|---|
| (PLAB) Practical laboratories | 11 | Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 12 | Catalan | first semester | morning-mixed |
| (PLAB) Practical laboratories | 13 | Catalan | first semester | morning-mixed |
| (PLAB) Practical laboratories | 21 | Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 22 | Catalan | first semester | morning-mixed |
| (PLAB) Practical laboratories | 23 | Catalan | first semester | morning-mixed |
| (TE) Theory | 1 | Catalan/Spanish | first semester | morning-mixed |
| (TE) Theory | 2 | Catalan/Spanish | first semester | morning-mixed |