Big Data Analytics - COM00148M
- Department: Computer Science
- Credit value: 15 credits
- Credit level: M
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Academic year of delivery: 2024-25
- See module specification for other years: 2023-24
Module summary
This module will provide the skills in data analytics, including the preparation of data, data handling, formulating precise questions, and using tools from statistics and data mining.
Related modules
Pre-requisite modules
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Online Teaching Period 5 2024-25 |
Module aims
This module will provide the skills in data analytics, including the preparation of data, data handling, formulating precise questions, and using tools from statistics and data mining to address those questions. The module will also cover the privacy aspects of big data and the techniques to mitigate these risks.
Module learning outcomes
By the end of this module you should be able to:
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Create a data set using modern database models and technology,
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Manipulate a data set to extract statistics and features,
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Critically evaluate and apply data mining techniques/tools to build a classifier or regression model, and predict values for new examples,
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Analyse and communicate issues with scaling up to large data sets, and use appropriate techniques to scale up the computation,
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Critically discuss the need for privacy, identify privacy risks in releasing information, and use design techniques to mediate these risks.
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100.0 |
Special assessment rules
None
Additional assessment information
Reassessment will occur within 6 weeks.
Indicative reassessment
None
Module feedback
Feedback will be provided in line with University policy.
Indicative reading
- Data Mining: Practical Machine Learning Tools and Techniques, 4th Edition, Ian Witten, Eibe Frank, Mark Hall, Chris J Pal, 2016
- Data Science, Kelleher & Tierney, 2018
- Principles of Database Management, Lemahieu, Broucke, and Baesens, 2018