Module aims
This module is aimed at learners who want to study advanced concepts relating to data science. Using both lectures and independent research, the module will address a number of issues relating to understanding and optimising the performance of data mining algorithms. The module will cover approximately ten algorithms, include algorithms for classification, regression, clustering, assocation analysis and sequence analysis.
Module learning outcomes
On successful completion of this module the learner will be able to:
- Discuss in depth a variety of data mining techniques, and their applicability to various problem domains, including big data analytics.
- Evaluate a business objective and related dataset to assess the appropriateness of a number data mining algorithms in achieving that objective.
- Work through the mining and evaluation stages of a data mining methodology, selecting the most appropriate mining technique, and optimising algorithm parameters to maximise performance.
- Independently research current trends and developments in knowledge discovery related technologies.
- Critically analyse relevant publications to assess the relative merits of methodologies used and conclusions made.
- Self-evaluate work done.
Indicative module assessment
- Literature reviews and analysis of topics relevant to the module.
- Practical assessment to select candidate mining algorithms appropriate to a selection of datasets and mining objectives; compare the results of these mining algorithms. The deliverable is a report justifying, evaluating and analysing the effectiveness of the algorithms used, and the effectiveness of tuning parameter settings on each algorithm. Practical work will be done using Rapidminer an opensource data mining tool.