WM9A9-15 Big Data, Analytics & Optimisation
Introductory description
Advanced eCommerce and Digital Analytics involves the utilisation of many of the newer, and more sophisticated technologies and techniques for optimising digital assets and business processes. This module introduces some of the most important of these, and gives participants practical experience of their uses
Module aims
The module aims to expose students to the latest in technical eCommerce practice and provide a toolkit for the implementation and optimisation of digital platforms and strategies. This incorporates technological developments, strategy and management, as well as analytical methods to derive insights from data at scale (which is common to modern digital platforms). Participants will get the opportunity to develop hands-on experience with the latest technology, within a modern cloud environment, to critically analyse a range of business scenarios, and implement sophisticated big data and digital analytics solutions
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
- eCommerce optimisation
- Web crawling, API
-T echnical SEO (web optimisation) - Customer segmentation
- Big data
- Big data fundamentals
- Cloud computing
- NoSQL databases and data lakes
- Data warehouse and SQL
- Artificial intelligence and machine learning
- Natural language processing
- Unstructured data analysis
- Data visualisation
-Best practice of data visualisation
- Dashboards
- A practical simulation of the above topics
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate a comprehensive understanding of the key differences between Big Data technologies and analysis methods and traditional approaches.
- Evaluate real-world scenarios and devise appropriate analytical solutions.
- Demonstrate a comprehensive understanding of the core concepts of visual communication and data visualisation.
- Collaboratively analyse digital business requirements and practically implement analytics and optimistaion techniques in real-world settings
Indicative reading list
View reading list on Talis Aspire
Interdisciplinary
A mixture of technology/computing topics and business topics
International
Topics are of high international demand
Subject specific skills
Big data, analytics, visualisation, technical SEO, AI, machine learning, NLP
Transferable skills
Programming, statistics and modelling, team work, critical analysis
Study time
| Type | Required |
|---|---|
| Lectures | 20 sessions of 1 hour (13%) |
| Seminars | 10 sessions of 1 hour (7%) |
| Online learning (independent) | 60 sessions of 1 hour (40%) |
| Assessment | 60 hours (40%) |
| Total | 150 hours |
Private study description
No private study requirements defined for this module.
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Assessment group A3
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Big Data Analytics Presentation | 30% | 18 hours | No |
|
A presentation of analyses of various datasets and recommendations on business actions from them. Peer Marking Process will be adopted in this assessment |
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| Assignment | 70% | 42 hours | Yes (extension) |
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A business-style report discussing core topics in big data, optimisation and visualisation |
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Feedback on assessment
Verbal feedback will be provided for the group assessment. Written feedback will be provided for the individual assignment.
There is currently no information about the courses for which this module is core or optional.