This module will examine the issues involved in selecting data for analysis in the context of Business data and Big Data and will consider whether and how techniques associated with Data Mining can be used to enhance business functions.
Topics covered will include:
- An overview of the Data Analytics domain with attendant disciplines and supporting technology
- The definition of data harvesting and its relevance in a Big Data context
- Data preparation in a Business Data context (gathering and validating data, and evaluating the quality of the data)
- Utilise mathematics in conjunction with working with data
- Identifying analysis requirements in a Business context
- Concepts that underpin data mining
- Tools and techniques for data mining
- Evaluation of tools and techniques and suitability for use in specified contexts
- Introduction to Business Data Analytics
- Data Analytics Lifecycle
- Industry supported and used standards
- Show innovation and critical decision making in evaluation of models
- Clustering
- Association Rules
- Regression
- Classification
- Time Series Analysis
- Text Analysis
- Data visualisation techniques

Practical Assessment - An application of data mining tools and techniques in a data mining environment, using for example the Weka data mining tool or similar (Learning Outcomes 1 and 2).

Written Report – A management style report based on a scenario, which identifies the issues involved in data harvesting and data modelling which makes recommendations on the use of these areas in the context of the scenario (Learning Outcomes 1, 3 and 4).

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

Knowledge and Understanding


2) Systematically understand the concepts that underpin data mining and be able to apply relevant tools and techniques to solve complex problems.

Knowledge and Understanding,
Application


3) Critically evaluate data mining approaches in the context of Business data and be able to select the most appropriate tools and techniques for analysis of complex issues.

Analysis,
Reflection



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

Knowledge and Understanding,
Problem Solving

All teaching sessions will blend theory and practical learning. Students will be introduced to curriculum concepts and ideas and will then be able to apply theory to practical examples within the same sessions. In addition, students 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 (mock or practice) exercises which will help students build knowledge and confidence in preparation for summative (formal) assessment.

All texts and electronic resources will be updated and refreshed on an annual basis and available for students via the online Study Links resource platform. All reference materials will be collated and curated and aligned to Equality, Diversity & Inclusion indicators.



Clarke, E. (2022), Everything Data Analytics-A Beginner's Guide to Data Literacy: Understanding the Processes That Turn Data Into Insights (All Things Data), ¿Kenneth Michael Fornari

Aspen-Taylor, S. (2022), Data and Analytics Strategy for Business: Unlock Data Assets and Increase Innovation with a Results-Driven Data Strategy, Kogan Page; 1st edition

Theobald, A. (2022), Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy: (Introduction to Data, Data Visualization, Business Intelligence & ... Science, Python & Statistics for Beginners), Independently published¿¿

Dean J., (2014), Big Data, Data Mining and Machine Learning, John Wiley & Sons
Ahlemeyer-Stubbe A. et al, (2014), A Practical Guide to Data Mining for Business and Industry, John Wiley & Sons

Krishnan, K. (2013), Data Warehousing in the Age of Big Data, Morgan Kaufmann
Russell, M. A., (2013), Mining the Social Web Reilly, O’Reilly
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

Open source data analytics tools such as
WEKA

The module will introduce you to the issues involved in identifying business data for analysis and modelling and will look at data mining in the context of Big Data and Business Data and will give you hands on experience of working with data mining tools and techniques.