WM9A9-15 Big Data, Analytics & Optimisation
Introductory description
Advanced eCommerce and Digital Analytics involve 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 essential skills in building and managing big data pipelines and navigating the big data analytics lifecycle from raw data to actionable insights. Students will master data visualisation techniques to effectively communicate findings and drive decision-making. For eCommerce specialists, the module incorporates data-driven website optimisation to enhance digital visibility and performance. Through hands-on experience in cloud-based environments, participants will develop the ability to critically analyse a range of business scenarios and implement sophisticated big data and digital analytics solutions that address real-world business challenges. This comprehensive approach prepares students to leverage data at scale, optimising digital platforms and strategies for competitive advantage in the rapidly evolving eCommerce and digital operations landscape.
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.
Big data architecture
- Cloud computing
- Big data collection - Web crawling & API
- Big data storage - Relational database (SQL) & Non-relational database (NoSQL), data lake & data warehouse
- Big data processing - ELT & ETL
Note: The main aim of these sessions is to expose you to the cutting-edge technologies in big data area.
Big data analytics
- Big data for web optimisation (e.g. technical SEO)
- Artificial intelligence and machine learning
- Natural language processing
Data visualisation
- Best practice of data visualisation
- Dashboards
- Data story
A practical data simulation
You will have chances to apply all the analytics and visualisation techniques to complete a group data project.
Learning outcomes
By the end of the module, students should be able to:
- Critically analyse and evaluate the limitations of traditional data approaches and the key drivers of applying big data technologies.
- Critically evaluate digital business scenarios and devise appropriate big data 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 optimisation 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
Presentation skills, research, teamwork and working effectively with others, software development, critical thinking, problem-solving, communication, professionalism, organisational awareness
Study time
| Type | Required |
|---|---|
| Lectures | 20 sessions of 1 hour (13%) |
| Seminars | 10 sessions of 1 hour (7%) |
| Online learning (independent) | 30 sessions of 1 hour (20%) |
| Private study | 30 hours (20%) |
| Assessment | 60 hours (40%) |
| Total | 150 hours |
Private study description
Private study will include preparing for lectures and seminars, reviewing lecture notes, and engaging with required readings and multimedia resources.
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Assessment group A4
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
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| Big Data Analytics Presentation | 30% | 18 hours | No |
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A presentation of analyses of various datasets with visualisation and recommendations on business actions from them. Peer Marking Process will be adopted in this assessment |
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Reassessment component |
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| Big Data Analytics Presentation | Yes (extension) | ||
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A presentation of analyses of various datasets and recommendations on business actions from them. This reassessment for the presentation will be a video submission. |
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Assessment component |
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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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Reassessment component is the same |
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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.
Courses
This module is Optional for:
- Year 1 of TWMS-H1S4 Postgraduate Taught e-Business Management (Full-time)