| Module Title |
Data Communication |
| Module Code |
STA1002 (ITS: CM5033) |
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Faculty |
Communications |
School |
Humanities & Social Sciences |
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NFQ level |
9 |
Credit Rating |
10 |
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Description
This module covers the theoretical, representational and practical aspects of retrieving, organising and presenting highly technical data across a range of formats in the era of Big Data. 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, policymaking or STEM-related areas. The module also provides students with the tools to develop various translatable and accessible data objects through different formats and platforms.
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Learning Outcomes
1. Read and understand complex analytics from a wide variety of data sources
2. Apply dissemination decision-making in fast-paced communications environments with multiple datasets;
3. Develop content for various modes of delivery: writing, databases, infographics, social networks
4. Understand and apply critical perspectives on data management such as ethics and privacy
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| Workload | Full time hours per semester | | Type | Hours | Description |
|---|
| Lecture | 22 | Lecture -interactive seminars | | Workshop | 11 | Practical data retrieving and visualisation; writing and digital content | | Independent Study | 57 | Student-centred learning | | Assignment Completion | 35 | No Description |
| Total Workload: 125 |
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| Section Breakdown | | CRN | 20554 | Part of Term | Semester 2 | | Coursework | 0% | Examination Weight | 0% | | Grade Scale | 40PASS | Pass Both Elements | Y | | Resit Category | RC1 | Best Mark | N | | Module Co-ordinator | Dónal Mulligan | Module Teacher | |
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| Assessment Breakdown |
| Type | Description | % of total | Assessment Date |
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| 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. | 60% | Week 8 | | Assignment | Develop FIVE separate data objects from the supplied data sources that might be uses by a news agency to provide deeper information in a breaking news story in a highly accessible, informative, accurate and engaging way. Visual, textual and online media formats must be used. | 40% | Week 12 |
| 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
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Pre-requisite |
None
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Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
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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
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Indicative Content and Learning Activities
Understanding analytics
Reading and understanding analytics from various commercial and public sources, using software such as Adobe or Chartbeat; uncovering and cleaning data; identifying principle stats producing reports and maintaining scientific integrity
Report Writing
Writing and publishing skills for data and internal communication in industry
Visualisation concepts and aesthetics
History and current trends in visualisation
Infographics design and visual techniques
Practical workshops in visualisation including proficiency in the use of commerical data packages
Digital storytelling
The process and application of telling a story about data for frontline communicators; explain, enlighten and engage using visuals, narrative and data
Critical perspectives on data communication
Data ethics, ownership, privacy, responsibility
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Indicative Reading List
Books:
- Herzog, D.: 2016, Data Literacy: A User’s Guide,, Sage,
- Kennedy, H.: 2016, Post, mine, repeat: Social media data mining becomes ordinary, Palgrave.,
- Kubitschko, S. and Kaun, A. eds.,: 2016, Innovative Methods in Media and Communication Research., Springer,
- 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,
- Few, S.: 2012, Show me the numbers: designing tables and graphs to enlighten.,, Analytics Press., Burlingame, Calif.,
Articles:
- Manovich, L.,: 2018, Digital Traces in Context 100 Billion Data Rows per Second: Media Analytics in the Early 21st Century., International Journal of Communication,, 12,
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Other Resources
None |
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