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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Data Journalism
Module Code CM2003A
School School of Communications
Module Co-ordinatorSemester 1: Jane Suiter
Semester 2: Jane Suiter
Autumn: Jane Suiter
Module TeachersJane Suiter
NFQ level 8 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Any failed assignment must be re-submitted as a resit over the summer.

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 clean and analyze it critically, 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 sets. Students will apply these principles and tools in telling journalistic stories. The module will be delivered as a series of lectures and seminars which will cover file management, spreadsheets, document and presentation preparation and visualisation tools 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. How to interview the data Sorting, Cleaning, Analytics, Pivot Tables, Basic Statistics. How to Bring the Data to Life Principles. Design. Animation. Interaction

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.

Workload Full-time hours per semester
Type Hours Description
Lecture22A 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 Completion22Weekly Preparation
Independent Study22Researching Data Journalism Exemplars
Laboratory24Software and Analytics Practice
Independent Study35Writing / Designing Project
Total Workload: 125

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


Publically funded datasets

Social networks

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

How to Bring the Data to Life Principles

Design, Animation, Interaction

Assessment Breakdown
Continuous Assessment100% Examination Weight0%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Short Answer QuestionsKubicle Online Assessment30%Week 29
AssignmentIn class presentation20%Week 30
AssignmentAssignments to find, combine, and present data from multiple data sources50%Week 11
Reassessment Requirement Type
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 1
Indicative Reading List

  • Liliana Bounegru Liliana Bounegru: 2012, The Data Journalism Handbook, European Journalism Center,
  • 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,
  • Brant Houston,: 2005, The Investigative Reporter's Handbook, 5th, St. Martin's, Boston, 978-0312589974
  • Stephen J. Berry,: 2009, Watchdog Journalism, 1st, Oxford UP, Oxford, 978-0195374025
  • Paul Bocij, Andrew Greasley, Simon Hickie: 0, Business Information Systems: Technology, Development and Management for the e-Business, 5th, 1,2 3,4 5,6, 7, 13, Prentice Hall London,
Other Resources

Programme or List of Programmes
JRBA in Journalism

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