This module is not currently running in 2024 to 2025.
Data types: nominal, numerical, ordinal, text, audio, visual, temporal and non-temporal. Basic descriptive statistics: measures of average and spread, different ways of graphing data. Visualisation techniques for data of different types and scales. Choosing appropriate and valid methods for the analysis and presentation of data, and understanding the limitations of methods. Data at different scales, including big data, and the computational challenges of processing data at scale. The process of discovering useful knowledge from data: including understanding the need for preprocessing and cleaning data, the challenges of gathering relevant data, and the need to present results in a comprehensible and actionable way. Data mining: classification and clustering, and the idea of predictive analytics. Ethical, privacy and security issues concerning data.
Total contact hours: 32
Private study hours: 118
Total study hours: 150
Main assessment methods
Coursework (50%)
Exam (50%)
Reassessment methods
Like for like.
John. H. Kranzler, Statistics for the Terrified, Pearson, 2010.
Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2011.
Joel Grus, Data Science from Scratch, O'Reilly, 2015.
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:
1. Present data using descriptive statistics and visualisations.
2. Describe methods for obtaining knowledge from data at different scales and of different types.
3. Apply computer packages for data visualisation and data mining to sample datasets.
4. Describe the entire knowledge discovery from data process and be able to apply it to specific examples.
5. Describe the challenges of ethics, privacy and security in data and apply these to specific examples.
The intended generic learning outcomes.
On successfully completing the module students will be able to:
1. Use sophisticated computer software.
2. Write reports using appropriate language and visual methods.
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