Big Data Analytics and Visualisation - BUSN9165

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2024 to 2025
Spring Term 7 15 (7.5) Zhen Zhu checkmark-circle


Imagine holding the power to predict future trends, understand customer behaviour and effectively communicate findings to stakeholders. You'll gain a deep theoretical grasp of big data analytics and visualisation, coupled with hands-on Python programming skills to unlock managerial insights. You'll gain essential tools to harness the power of big data, allowing you to extract valuable insights and create impactful and meaningful visualisations. Unleash the potential of data to drive innovation, fuel growth and make informed, strategic decisions in today's data-driven world.


Contact hours

Total contact hours: 36
Private study hours: 114
Total study hours: 150

Method of assessment

VLE test: 20%
Individual Report (3000 words): 80%

Reassessment methods
100% coursework

Indicative reading

Specially written reading materials will be provided in lectures and seminars/tutorials. Students will also be required to read academic research papers available through the library (e.g. International Journal of Data Science, Big Data Research, Big Data & Society, Big Data Analytics, MIS Quarterly, Journal of Operations Management). Specific references will be provided at the end of each lecture. Although there are no required texts, the following are recommended.

Lemahieu, W., vanden Broucke, S., Baesens, B. (2018). Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data. Cambridge University Press.
Kane, F. (2017). Frank Kane's Taming Big Data with Apache Spark and Python. Packt Publishing Ltd.
Wexler, S., Shaffer, J., & Cotgreave, A. (2017). The big book of dashboards: visualizing your data using real-world business scenarios. John Wiley & Sons.
Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons.

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
- Display conceptual understanding of big data analytics and visualisation techniques.
- Critically evaluate and apply big data techniques using software such as Apache Spark and Python.
- Develop a systematic understanding in order to build and apply skills in big data network analytics, text mining, and social media data mining.
- Demonstrate critical awareness of how managers and executives utilise big data analytics for business value creation by improving their operational, social, and financial performance and create opportunities for new business development.
- Demonstrate a systematic understanding of database management concepts and their connections with big data analytics.

The intended generic learning outcomes.
On successfully completing the module students will be able to:
- Work on complex issues associated with big data analytics and business value creation.
- Scrutinize different types of data for solving complex business problems and produce reports to support business planning.
- Systematically, critically, and creatively present findings to both technical and non-technical managers and executives.
- Use computer tools to solve complex practical problems of direct relevance to contemporary business operations and management.


  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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