| Module Title |
Data Processing and Visualisation |
| Module Code |
CA273A |
|
School |
School of Computing |
| Module Co-ordinator | Semester 1: Suzanne Little
Semester 2: Suzanne Little
Autumn: Suzanne Little
| | | Module Teachers | Suzanne Little
| |
NFQ level |
8 |
Credit Rating |
7.5 |
|
Pre-requisite |
None |
|
Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
|
Coursework Only
|
Description
This module will equip students with knowledge of methods for processing, ingesting, cleaning and reformatting data sets using a variety of tools. It will introduce exploratory data analysis through interactive visualisation and develop student skills in creating effective data visualisations. The module will enable students to develop skills in communication, visualisation design and the ability to critique the effectiveness of data visualisations.
|
Learning Outcomes
1. Explain the data analysis pipeline and the stages of data processing, transformation, cleaning, management, visualisation
and communication.
2. Identify and describe relevant data formats and standards.
3. Select and perform appropriate data cleaning operations using different tools and techniques and documenting the processes (e.g., notebooks).
4. Design and create effective interactive data visualisations using both dedicated applications (e.g., Tableau, Spreadsheets) and developer libraries (e.g., matplotlib, seaborn, bokeh).
5. Understand the requirements for communicating data analysis through visualisations and critique both their own and other visualisations.
|
| Workload |
Full-time hours per semester |
| Type |
Hours |
Description |
| Lecture | 24 | No Description | | Laboratory | 20 | Computer-based | | Online activity | 10 | Portfolio preparation | | Independent Study | 133.5 | Self-directed skills practise and assignments | | Total Workload: 187.5 |
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
Data processingdata loading, formats, metadata, conversion, basic APIs to ingest data, using notebooks, standardsData cleaningidentifying possible errors, handling null values, geolocation issues, tools to clean data setData visualisationselecting appropriate graph types, creating interactive visualisations, using common visualisation tools, critiquing visualisationsCommunicationdesign rules for visualisation, creating data exploration notebooks recording processes, presenting data visualisations |
| Assessment Breakdown | | Continuous Assessment | 100% | Examination Weight | % |
| Course Work Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| Assignment | Demonstrate basic git skills by cloning, editing and updating a project
including a report on how to use git. | 9% | Week 3 | | Loop Quiz | Synchronous Online Quiz via loop on the main concepts from weeks 1 to
3 on the data analytics pipeline, data types and formats and data
standards. | 12% | Week 5 | | Assignment | Through ongoing lab assessments, load, transform and clean a given data set or sets producing a suitable report documenting the process. | 12% | n/a | | Loop Quiz | Synchronous Online Quiz via loop on the content from weeks 5-8
including creating a simple graph, explaining big data, the Internet and
communication theory. | 12% | Week 9 | | Assignment | Design and produce a significant and interactive data visualisation
demonstrating both technical and communication skills. Produce the
visualisation (as a screen capture, app or web site), a report and give a
presentation on the visualisation. | 20% | Week 10 | | Assignment | Visualisation Critique: review both provided visualisations and peer
submissions for the visualisation assignment and write a brief analysis of
their use of visualisation principles. | 20% | Week 12 | | Reflective journal | Write a set of skill summaries and self-assessments from the
technologies used in the module. | 15% | 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
- Field Cady: 2017, The Data Science Handbook,
- Stephen Few: 2012, Show Me the Numbers, 2nd Ed,
- Andy Kirk: 2016, Data Visualisation,
- Cole Nussbaumer Knaflic: 2015, Storytelling with Data: A Data Visualization Guide for Business Professionals,
- Sinan Ozdemir: 2016, Principles of Data Science,
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Other Resources
None |
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Programme or List of Programmes
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| Archives: | |
