The module will address the following topics:


OLAP approaches and technologies (Online Analytical Processing)

Introduction to Business Data Analytics

Data Analytics Lifecycle

Clustering

Association Rules

Regression

Classification

Time Series Analysis

Text Analysis

Data visualisation techniques

Legal aspects of data including data privacy and governance

Data structures required to support data mining – weka files, relational, noSQL etc

Concepts of Data Warehousing

Data Warehousing design approaches

Architectures, schemas, tuning, loading, etc.¿

Dimensional modelling¿

Performance issues

Kimball and Inmon

Data lakes

Issues of data cleansing

Data governance

Data integration

Data transformation

Working with structured and unstructured data

Using Data Warehousing techniques with a relational database

Written Report – This is a practical element, being the study of an application of data mining tools and techniques in a data mining environment, using for example the Weka data mining tool or similar and documenting these (Learning Outcome 1).

Written Report - A management style report based on a scenario which identifies the issues involved in data harvesting and data modelling and which makes recommendations on the use of these areas in the context of the scenario of the first report (Learning Outcomes 2 and 3).

Theory will be delivered via lectures and supported by practical classes, seminars and discussion groups. In addition, you will be provided with a range of resources for independent study such as case studies, academic papers and industry stories. There will be a mixture of practical and theoretical formative exercises which will help you build your knowledge and confidence as well as preparing you for the summative assessment.

1. Understand, discuss, and critically evaluate the issues involved in sourcing, preparing, and making available data for analysis (Data harvesting).

Knowledge and Understanding

2. Critically evaluate data mining approaches in the context of Business data and select the most appropriate tools and techniques for analysis of complex issues.

Analysis,
Reflection

3. Critically identify and discuss appropriate strategies for modelling and analysing business data.

Problem Solving

Open source data analysis tools

Gilbert, S. (2022), Good Data: An Optimist's Guide to Our Digital Future, Welbeck

Comprehensive Technology Research, (2022), The New Digital Revolution For Beginners: Practical uses of Metaverse, Web 3.0, Blockchain, Cryptocurrencies, NFTs, DeFi, Virtual and Augmented Reality, Independently published

Dietrich, D. et al. , (2015), Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, John Wiley & Sons
Holmes, D. E. (2017), Big Data: A Very Short Introduction (Very Short Introductions), OUP Oxford

Sharda, R. (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective (4th Edition), Pearson

Clause, P. S. (2022), Data Warehouse Cloud, Espresso Tutorials GmbH

This module will focus on data mining and data structures to establish a strong infrastructure so that Business Intelligence and Data Analytics can be successfully deployed to improve an organisations ability to gain knowledge from its existing databases. The module will specifically focus on Data Warehousing and the techniques of handling data. Within the assessment students will look at case study examples to implement proposals around data harvesting and mining.