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optional modules

7AAVDM12 Introduction to Data Journalism

Module Type: Optional

Credit Value: 20

Semester: 1

Module convenor: Tommaso Venturini

Module Description

In the last few years, the practices of journalism have been shaken by the advent of a new series of digital techniques of investigation and storytelling. On the one hand, the growing availability of digital data opens new ways of enquiring on actions of institutions, companies and individuals. On the other, the advent of new methods of computation and visualization allows new possibility of interactions with the news and exploration of their sources.

The ensemble of these, tentative yet promising, evolutions goes under the name of ‘data journalism’ and will constitute the object of this module.

In this module, students will learn to assume a critical attitude towards data and reflect on the renewed importance of journalistic mediation at the time of the digital networks. Students will also be trained in the fundamentals of data manipulation and experiment several conceptual and technical tools for the cleaning, analysis and representation of digital traces. The importance of embedding findings in an interesting narration will also be discussed.

But in this module, students will also experience a hands-on ‘immersion’ in the situation of a data newsroom, in which students will be divided in groups of 4 or 5 and will work intensively on a journalistic project of their choice. Under the guidance of the module convenor, the students will analyse and narrate a set of data that they will have collected and cleaned beforehand.

The assessment is based on the results produced during the intensive workshop and on a short journalistic essay that each student will individually write around them.

This module will be organized in combination with the ‘Information Visualisation (7AAVDM16)’ one. Though the two modules are separated (and none is a pre-requisite for the other), the students of this module will be able to use the diagrams developed by their Information Visualisation colleagues.

Draft Teaching Syllabus

 The module will be taught via 7 one-hour lectures, 6 one-hour seminars and 1 seven-hours intensive workshop.

Week 1 (lecture) | Introduction to the different traditions of data journalism

Week 2 (lecture & seminar) | Finding data

Week 3 (lecture & seminar) | Treating data

Week 4 (Self-guided learning) | Corpus collection

Week 5 (lecture & seminar) | Visualising data

Week 6 (lecture & seminar) | Narrating data

Week 7 (self-guided learning) | Corpus preparation

Week 8 (lecture & seminar) | Review of the projects in preparation for the workshop

Week 9 (intensive workshop) | Data newsroom hands-on workshop

Week 10 (lecture & seminar) | Conclusion: taking stock of the course

* Note: This is a sample outline of the teaching schedule.

Module Aims
  • Teaching students a critical attitude towards data and their capability to speak for themselves
  • Reflecting on the renewed importance of journalistic mediation at the time of the digital networks.
  • Training students in the fundamentals of data manipulation, presenting them a series of conceptual and technical tools for the cleaning, analysis and representation of digital traces.

Discussing the importance of embedding findings and visualisations in an interesting narration

Learning Outcomes
  • Develop a critical reflection on data and their possible uses in journalism;
  • Possess the practical skills required to clean and analyse small sets of data;
  • Understand the principles of visual semiotics and use them to represent diagrams and networks.

Recognise the importance of ‘data narration’ and be able to produce simple ‘data stories’

Key Reading

Bertin. (2010). Semiology of Graphics: Diagrams, Networks, Maps.

Gray, J., Chambers, L., & Bounegru, L. (2012). The Data Journalism Handbook. Sebastopol, Ca.: O’Reilly Media.

Herzog, D. (2015). Data Literacy: A User’s Guide. Los Angeles: Sage.

Segel, E., & Heer, J. (2010). Narrative visualization: telling stories with data. IEEE Transactions on Visualization and Computer Graphics, 16(6), 1139–48. doi:10.1109/TVCG.2010.179

Venturini, T., Jacomy, M., & Carvalho Pereira, D. (2015). Visual Network Analysis. Paris.

Module Assessment

The assessment is based on the results produced during the intensive
workshop and on a short journalistic piece that each student will individually
write around them.

Module Information page
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