Data mining refers to the process of deploying advanced analytical solutions throughout an
organization, from initial planning to final implementation. This module will guide students through
a typical life cycle, such as the CRISP-DM model, and examine each stage in detail, including the
tasks and technologies involved.
This module covers a variety of data discovery techniques and algorithms can be used to identify
patterns within the data. The main goal of the students in this module is to give an overview of the
different steps in data mining such as business and data understanding, data preparation,
modelling, evaluating, and deployment. We use both drag-and-drop modules software and some
basic programming commands for creating basic pipelines.
Module content will be broadly as follows:
Overview
- Introduction to data mining and applications of data mining
- Data, Information, Knowledge
- Framing a business model
- How Data Mining fits within the organisation
Data Mining Life Cycle
- Stages of a DM project
- Explore various data mining life cycles.
- Evolving nature of roles and responsibilities of people involved in data mining projects.
Data Preparation
- Extracting and loading data mining
- Data transformations
- Data sampling
- Data aggregation
- Feature engineering
Exploring Data and Gaining Insights
- Using a variety of analytic methods to gain data insights
- Role of visualisations in pattern discovery
- Time-series forecasting
- Exploring and mining text
Data Mining Techniques. Explore the use of various techniques for structured and
unstructured data including:
- Classification
- Regression
- Association rule analysis
- Data cluster analysis
- Anomaly Detection
- Reinforcement Learning
Understanding and evaluating the outputs and determine what to use.
Deploying Data Mining Solutions
- Issues around deployment of data mining solutions
- Combining multiple algorithms and models
- Creating pipelines for deployment
- Model management and when to retrain models and solutions.
Topics on the Management of the Data Mining Process and Life-Cycle
- Legal Issues
- Ethical Issues
- Biases in data
- Using and managing different technologies
The module is designed to be delivered within a blended learning model, employing mixed modes (online and face to face) of learning, teaching and assessment.
TU059 will be delivered primarily in a face-to-face mode while TU256 and TU060 will be delivered in a blended mode.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 50 |
| Other Assessment(s) | 50 |