WM9QX-15 Big Data Technology and Analytics
Introductory description
Modern digital business and operations involve the utilisation of many of the newer, and more sophisticated technologies and techniques for analysing and optimising digital assets and business processes. The management of an organisation's data lifecycle has been an essential activity in modern digital business. This module introduces rapidly evolving fields of big data, the challenges and opportunities they bring, and gives students practical exposure to the use of practical data tools.
Module aims
The module aims to expose students to the latest big data technologies and analytics techniques. The full big data analytics lifecycle will be covered in this module, from data collection, data storage, data processing, data analysis through to data visualisation. Participants will get the opportunity to develop hands-on experience with the latest technologies within a modern cloud environment, to critically analyse a range of business scenarios, and implement sophisticated big data and digital analytics solutions following the data pipeline.
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.
Introduction to key concepts of big data
- Big data pipeline
- Introduction to cloud platform
- Analytics full lifecycle (alignment of business and data goal)
Data collection/extraction
- API/web crawlers
- Open data/public data
Data storage & processing
- RDBMS and NoSQL databases
- Data lake and data warehouse
- Hadoop and Spark
- Querying and processing data from database/data warehouse
Data analysis
- Implementing analytics projects in the cloud environment
- Analyse data with Python
- Analyse data with SQL
- Artificial intelligence and machine learning
- Natural language processing
Data visualisation
- Introduction to BI tool
- Best practice of data visualisation
- Data report and dashboard
A practical simulation of the above topics under the digital business context
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate a comprehensive understanding of the big data technologies and their differences to traditional data technologies.
- Evaluate real-world scenarios and devise appropriate big data technologies and analytical solutions.
- Demonstrate a comprehensive understanding of the core concepts of big data analytics and data visualisation.
- Collaboratively analyse digital business requirements and practically implement big data analytics 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, data engineering, analytics, visualisation, AI, machine learning, NLP
Transferable skills
Programming, statistics and modelling, team work, critical analysis, communication
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 A
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
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| Delivering a big data analytics project | 30% | 18 hours | No |
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Students are given business context and access to data (either shared data or open data). Students are required to analyse business requirements and design analytics solutions using the data. This culminates in a group demonstration of the data solutions and insights. Peer Marking Process will be adopted in this assessment |
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Reassessment component |
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| Delivering a big data analytics project | No | ||
|
A presentation of group analytics project reflection and an analytics solution to the case study company. |
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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, including data engineering, data analytics 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)