Introductory description

The module aims to provide an understanding of how analytics projects are structured from start to end.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

This module is dedicated to conveying a sense of how to structure analytic projects systematically, from understanding of the business problem over modelling up to model assessment and communication of the project's results.
This module introduces a way of such a structure with an applied, step-by-step introduction that mixes theory and practical, hands-on implementation tasks:

• Introduction to data mining and CRISP-DM.
• Data collection, understanding data
• Integrating and cleaning data, deriving and reclassifying fields
• Introduction to predictive modelling with decision trees, oversampling, partitioning
• Model understanding, comparing and combining models, model assessment and model deployment

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate understanding of how to structure data mining projects systematically.
• Demonstrate an awareness of challenges in predictive modelling and how to address them.
• Critically evaluate the performance of predictive analytics models and data products.
• Identify potential problems in data sets (data cleaning).

Indicative reading list

Reading lists can be found in Talis

Subject specific skills

Perform basic data extraction, cleansing and manipulation tasks.
Visualise data in suitable formats.
Conduct basic predictive modelling analysis on realistic data.
Identify potential problems in data sets (data cleaning).

Transferable skills

Work within a team to analyse data issues and propose solutions.
Communicate analytic findings to a non-technical audience
Communication skills