Module: Data Journalism

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Data Journalism
Module Code	JRR1010
Faculty	Humanities & Social Sciences	School	Communications
NFQ level	8	Credit Rating	5

Description

This module focuses on how data and IT tools can assist and support journalists in digital storytelling. The module aims to give students a solid basic understanding of the tools available and possibilities of data journalism. Besides learning about the basics of this increasingly vital discipline, students will learn how data is used in the media industry today, where to locate data, how to analyse it, and how to optimise the presentation of information for maximum readability and interactivity. The module also gives students a thorough grounding in the use of common tools including spreadsheets and presentation. The module focuses particularly on using visualisation to effectively communicate data. Students will apply these principles and tools in telling journalistic stories and the importance of a narrative remains crucial throughout. The module will be delivered as a series of lectures and workshops which will cover spreadsheets, document and presentation preparation and visualisation tools.

Learning Outcomes

1. Explain how data is used in journalism
2. Understand where to source data for journalistic stories
3. Learn how to clean and utilise appropriate data, including FoI, open government and public datasets
4. Report and write complex, multi-source data driven journalism
5. Solve analytical problem and learn how to view data critically (incl. spreadsheets)
6. Apply the basic principles in effectively communicating data visually.
7. Utilize online tools to both collect and visualize data sets.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	22	A formal lecture which typically presents the essential ideas and core concepts pointing students towards resources where they can get further information. Students are expected to prepare for each lecture by, for example, reading materials suggested by the lecturer
Assignment Completion	46	Weekly Preparation
Laboratory	22	Software and Analytics Practice
Independent Study	35	Writing / Designing Project
Total Workload: 125

Section Breakdown
CRN	20569	Part of Term	Semester 2
Coursework	100%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Dawn Wheatley	Module Teacher	Jane Suiter

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	In class presentation	15%	n/a
Portfolio	Portfolio of weekly work	15%	n/a
In Class Test	Test student knowledge of analysing datasets	20%	n/a
Assignment	Final data journalism project	50%	Week 11

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Introduction and Context

Ubiquity of computers, information and systems

Data vs Information

Where to Find Data and the Stories

Open data

FoI

Publically funded datasets

Social networks

Data Sorting, Cleaning, Analytics, Pivot Tables, Basic Statistics

How to Bring the Data to Life Principles

Design, Animation, Interaction

Indicative Reading List

Books:

European Journalism Centre and Google News Initiative: 0, The Data Journalism Handbook 2, https://datajournalism.com/read/handbook/two,
Liliana Bounegru Liliana Bounegru: 2012, The Data Journalism Handbook, European Journalism Center,
Brant Houston,: 2005, The Investigative Reporter's Handbook, 5th, St. Martin's, Boston, 978-0312589974
edited by Jonathan Gray, Liliana Bounegru and Lucy Chambers: 2014, The Data Journalism Handbook. How Journalists can use data to improve the news, O'Reilly, New York,

Articles:
None

Other Resources

None