| Module Title |
Data Processing and Visualisation |
| Module Code |
CSC1158 (ITS: CA273A) |
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Faculty |
Engineering & Computing |
School |
Computing |
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NFQ level |
8 |
Credit Rating |
7.5 |
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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.
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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. (Identify and use basic data analysis and visualisation tools to describe and interpret data.)
3. Select and perform appropriate data cleaning operations using different tools and techniques and documenting the processes (e.g., notebooks). (Demonstrate the ability to source and import data and apply basic functions for cleaning and processing of this data in preparation for data analysis.)
4. Design and create effective interactive data visualisations using both dedicated applications and developer libraries.
5. Explain the requirements for communicating data analysis through visualisations and critique both their own and other visualisations. (Identify the relevant insights extracted from a dataset and effectively and appropriately communicate them.)
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| 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 |
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| Section Breakdown | | CRN | 11634 | Part of Term | Semester 1 | | Coursework | 100% | Examination Weight | 0% | | Grade Scale | 40PASS | Pass Both Elements | N | | Resit Category | RC1 | Best Mark | N | | Module Co-ordinator | Suzanne Little | Module Teacher | |
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| Assessment 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% | As required | | 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 | Working in a group, design and produce a significant and interactive data visualisation in response to a criteria,
demonstrating both technical and communication skills. Present the visualisation and answer questions about the design process. | 30% | Week 10 | | Assignment | Written response to Visualisation Critique: provide a written response to a visualisation created and critiqued. | 10% | 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;
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
Data processing
data loading, formats, metadata, conversion, basic APIs to ingest data, using notebooks, standards
Data cleaning
identifying possible errors, handling null values, geolocation issues, tools to clean data set
Data visualisation
selecting appropriate graph types, creating interactive visualisations, using common visualisation tools, critiquing visualisations
Communication
design rules for visualisation, creating data exploration notebooks recording processes, presenting data visualisations
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Indicative Reading List
Books:
- 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,
Articles:
None |
Other Resources
None |
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