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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Data Communication
Module Code CM5033
School School of Communications
Module Co-ordinatorSemester 1: Donal Mulligan
Semester 2: Donal Mulligan
Autumn: Donal Mulligan
Module TeachersPadraig Murphy
Donal Mulligan
NFQ level 9 Credit Rating 10
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Coursework Only
Resubmission of new project utilising a different data source.

This module covers the theoretical, representational and practical aspects of retrieving, organising and presenting data across a range of formats in the era of Big Data. These central skills are supported by the development of numeracy and familiarity with statistical concepts and tools on the part of the learner. The module will equip those who enter any form of contemporary communication profession with the skills to retrieve, read, understand and make decisions on large amounts of data, whether in in media, ICT, healthcare, policy-making or wider STEM-related areas. The module also provides students with the tools to develop various translatable and accessible data objects through relevant contemporary formats and platforms.

Learning Outcomes

1. Read and understand complex analytics from a wide variety of data sources
2. Familiarisation with contemporary sources of large structured data and its associated APIs
3. Develop good methodological practice for working with data, particularly focussing on data collection, representativeness, and limitations.
4. Understand and apply critical perspectives on data management such as ethics and privacy
5. Develop numeracy and statistical method familiarity to facilitate effective communication of inferential data to identified audiences.
6. Develop content for various modes of delivery: writing, databases, infographics, social networks

Workload Full-time hours per semester
Type Hours Description
Lecture22Lecture -interactive seminars
Workshop11Practical data retrieving and visualisation; writing and digital content
Independent Study57Student-centred learning
Assignment Completion35No Description
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

Developing numeracy and statistical methods familiarity
Exploring core concepts of mathematical analysis and statistical methodology as a foundation for competent data comprehension and communication.

Understanding analytics
Reading and understanding analytics from various commercial and public sources; uncovering and cleaning data; identifying principle stats producing reports and maintaining scientific integrity.

Visualisation concepts and aesthetics
Study historic and currently emerging trends and techniques in the visualisation of data for particular audiences.

Critical perspectives on data communication
Data ethics, ownership, privacy, responsibility, and associated legislation.

Report Writing
Writing and publishing skills for data and internal communication in industry

Infographics design and visual techniques
Practical workshops in visualisation including proficiency in the use of commerical data packages

Assessment Breakdown
Continuous Assessment100% Examination Weight0%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Completion of online activityWeekly Assessed Tasks are present in 6 of the module weeks, and are clearly identified as such. Students are required to complete 5 out of 6 of these tasks, which are submitted and graded on loop, and which follow themes from the lectures.10%Every Second Week
Report(s)Write a data analysis report on ONE of the provided data sources indicating key historical data, trends, themes, successes/areas for improvement, and include visualisation, tables and graphics reformatted, annotated and made accessible. A brief description at the beginning should pitch the main points. Outline a rationale section separate to the report on the domain and reasons for choosing this (eg business, technology, policy, higher education etc). Also consider in this section how you identified the primary client or audience, the secondary readers and ethical considerations for data.50%Week 8
AssignmentDevelop structured data objects from recommended data sources that could be supplied to an identified audience, providing suitable rigour and appropriate modes of communication to inform and engage this cohort effectively. Visual, textual and online media formats may be used.40%Week 12
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

  • Diez, D., Barr, C., Çetinkaya-Rundel, M.: 2015, OpenIntro Statistics, 3, https://www.openintro.org, 978-194345003
  • Kubitschko, S. and Kaun, A. eds.,: 2016, Innovative Methods in Media and Communication Research., Springer,
  • Few, S.: 2012, Show me the numbers: designing tables and graphs to enlighten.,, Analytics Press., Burlingame, Calif.,
  • Herzog, D.: 2016, Data Literacy: A User’s Guide,, Sage,
  • Kennedy, H.: 2016, Post, mine, repeat: Social media data mining becomes ordinary, Palgrave.,
  • Rajaraman, A., & Ullman, J. D: 2012, Mining of massive datasets, Cambridge University Press,, Cambridge,,
  • Rao, C. R., Wegman, E. J., & Solka, J. L.: 2005, Handbook of Statistics, Volume 24 Data Mining and Data Visualization.,, Elsevier, Burlington,
Other Resources

Programme or List of Programmes
CSPMMaster of Arts
GDSMCGrad Dip in Social Media Communications
MAPMA in Political Communication
MPCSMPhil (Communications)
MSHCMSc in Science and Health Communication
MSMCMA in Social Media Communications

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