This module aims to introduce students to the power of big data analytics and data visualisation techniques in contributing to business value creation. The module will also enable students to solve a variety of complex data centred business problems using computer software tools like Apache Spark and Python.
The module covers two main themes as follows.
1. Theoretical understanding of big data analytics: This part involves learning about the theoretical foundations of big data analytics, text mining, and social media data mining. It also introduces the effective use of data visualisation and database management concepts and their links with big data analytics. Example applications of big data analytics and visualisation techniques discussed within the module will focus on addressing contemporary challenges faced by industry.
2. Building practical skills and managerial insights: In this part of the module, students will learn how to interact with both SQL and NoSQL databases and how to solve business problems using advanced functions within the Apache Spark and Python software platforms. Students will be guided through demonstrations involving a variety of exercises that will prepare them to be data-driven managers and executives capable of utilising big data analytics for business value creation.
Total contact hours: 36
Private study hours: 114
Total study hours: 150
VLE test: 20%
Individual Report (3000 words): 80%
Reassessment methods
100% coursework
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)
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.
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