Data Mining and Analytics

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Be able to carry out a practical data mining project.
• Be able to apply data mining algorithms using R.
• Run basic queries to obtain information from a database
• Use R for data manipulation and visualisation

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• Gain practice in working as part of a technical team on a data mining project.
• Understand how to present results from a complex data analysis to a non-expert.

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• Develop a good understanding of the underpinning statistical ideas behind data mining.
• Have a good understanding of algorithms that can be used for classification, assortment, clustering and text analytics.
• Understand the basic principles of database design

Part 1: Data Analytics

This will cover the extraction of data from a database, preliminary analysis including plotting to support a better understanding of the underlying features and preprocessing. Introduction to reporting in R, including RMarkdown.

Part 2: Statistical Methods for Data Mining

Underlying statistical ideas needed for data mining, including maximum likelihood estimation, linear & logistic regression, principal components analysis and measures of similarity/dissimilarity.

Part 3: Text Mining

Processing text data, analysing word frequency (tf-idf), bag of words, with option to cover topic modelling (LDA – Latent Dirichlet Allocation).

Part 4: Classification

A main focus on trees, boosting algorithms and an introduction to random forests.

Part 5: Association Analysis

A priori algorithm and generation of frequent item sets.

Part 6: Cluster Analysis

K-means algorithm and hierarchical clustering.

The module will be taught using a mixture of lectures and computer workshops: 2 hours of lectures and 2 hours of computer workshops per week for the duration of Semester 1.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.