Topics covered will include:

Selecting data for analysis in the context of Business Data and Big Data
Data Mining techniques that can be used to enhance business functions
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
Legal considerations of data handling and legislation in place (UK based and internationally)
Industry supported and used standards
Innovation and critical decision making in evaluation of models
Clustering, Association Rules, Regression, Classification, Time Series Analysis, and Text Analysis
Data visualisation techniques

BCS / TechSkills / Employability elements:

In depth analysis of computing and business issues related to data handling.
Legal, social, ethical and professional issues as a general theme in relation to working with data.

PRESENTATION – The presentation is to present results of the use and an application of data mining tools and techniques within a data mining environment (using for example the Weka data mining tool or similar to address a provided scenario.

REPORT – A management style report based on a scenario, which identifies the issues involved in data harvesting and data modelling in relation to a given case study, and which makes recommendations on the use of these areas in the context of the scenario.

Formative assessment opportunities will be provided throughout the module. For the presentation staff will review your draft ideas and guide you further in forming your final presentation. There will also be a practice session where you can have a practice run of your final presentation. For the report staff will regularly guide you as to progress milestone’s, and will also review a single draft when near to hand in.

All teaching sessions will blend theory and practical learning. You will be introduced to curriculum concepts and ideas and will then be able to apply theory to practical examples within the same sessions. You will find practical sessions heavily focused on hands-on problem-solving activities revolving around industry and peer input required in finding and exploring solutions. 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 (mock or practice) exercises which will help you build knowledge and confidence in preparation for summative (formal) assessment.

1. Critically evaluate the issues involved in sourcing, preparing and making available data for analysis in practical data harvesting.

Knowledge & Understanding
Digital Literacy

2. Demonstrate systematic understanding of the concepts that underpin data mining and be able to apply relevant tools and techniques to solve complex problems.

Knowledge & Understanding
Application & Problem-Solving

3. Critically evaluate through research data mining approaches in the context of Business data and on reflection select the most appropriate tools and techniques for analysis of complex issues.

Research Skills
Reflection

4. Identify and critically discuss appropriate strategies for the practical modelling and analysing business data.

Knowledge & Understanding

Data analytics tools such as:

WEKA
ThoughtSpot
Tableau
Looker
Domo

Aspen-Taylor, S. (2025), Data and Analytics Strategy for Business: Leverage Data and AI to Achieve Your Business Goals, Kogan Page

Milligan, J. N, (2025), Learning Tableau 2025: Leverage Tableau's newest features to revolutionize your data storytelling with AI-enhanced insights, Packt Publishing

Conley, J. (2024), Advanced Data Analytics with AWS: Explore Data Analysis Concepts in the Cloud to Gain Meaningful Insights and Build Robust Data Engineering Workflows Across Diverse Data Sources, Orange Education Pvt Ltd

Mane, V. U. (2025), Advanced Data Analytics & Future Trends: Big Data, AI, and Predictive Insights for Tomorrow’s World, Data Analytics Series

Sinha, C. (2025), Dashboarding with Tableau: Design Interactive Dashboarding Applications powered with Data Load Concepts, Advanced Calculations, Insightful Visualization Techniques and New Features, Books District Publication

This module will introduce you to the issues involved in identifying correct and meaningful business data for analysis and modelling work through looking at data mining in the context of Big Data and Business Data. This will be done in depth so the knowledge you gain can be applied to any data based design activity as you progress within your career. You will gain a deep understanding of legal aspects involved, and pitfalls to watch out for. Apart from planning and designing for data use you will gain hands on experience of working with data mining tools and techniques adopted within the computing and business environments.