This module provides an overview of the main concepts and techniques in data mining. Data mining integrates machine learning and business understanding to extract valid, interesting insights from data. As such it is extremely important and widely used in today's world, across many disciplines and sectors.
You will be introduced to the data model building pipeline, covering exploratory data analysis, feature engineering, model selection and optimisation and validation, underpinned by visualisation. Each machine learning task is introduced, its strengths and weaknesses are highlighted, and guidance for its use is given. The data mining tasks include clustering, classification, regression, and rule learning. The module is python-based and uses pandas for data processing, scikit-learn for most of the machine learning tasks, and matplotlib/seaborn for visualising both the data and the models that we derive from this data. You will be expected to complete the weekly python practicals that extend the coverage of the lecture topics by showing how they operate in practice, on real data sets. Assessment is based on being able to investigate data effectively using the tools from the module.
Applicants will normally require an Honours degree in computer science or related discipline. Applicants who do not hold the standard qualifying criteria, but have relevant industry experience, may be considered for admission under the University's Recognition of Prior Learning (RPL) process.
