Data Analytics and Visualisation

This topic will introduce the learner to the SOTA data analysis tools and techniques, which help to interpret and extract meaningful information from data. The learner will gain expertise in data preprocessing, exploratory data analysis and visualisation, pattern recognition and discriminative classification.

Most modules require a 2.2 degree in a related discipline or equivalent professional experience. Should you have any queries regarding your eligibility, please contact us at

N.B. Required to have completed computer programming at degree level.

On completion of this module the learner will/should be able to:

  • Apply techniques such as feature scaling, standardisation, missing data handling and encoding to preprocess and clean data.
  • Visualise the processed data graphically, identify the correlation between the features, interpret the linear/non-linear relationships in the data.
  • Make meaningful inferences from the data, remove/retain features based on variable importance.
  • Create interactive dashboard for data visualisation.
  • Identify patterns in the data using exploratory data analysis and clustering techniques.
  • Discriminate patterns in new data using trained discriminative classification models.
  • Summarise, analyse, and relate research in the area of exploratory data analysis and pattern recognition in writing. Appreciate the data ethics and constraints that apply to the use of data in real-world scenarios.
  • Design, implement and test a real world problem using the above learned techniques.

Detailed Course Information

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