This module will address the following topics:

Theory & Knowledge Exchange

Overview of data quality
Introduction to the different types of data (structured, semi and unstructured etc.)
Extraction, Transformation and Load (ETL) of data to ensure quality decision making.
Dimension modelling and integration of possible uses of Data warehousing
Overview of data mining principles and techniques:
Data Mining related disciplines (e.g., using OLAP etc.),
Classification and Clustering Algorithms
Decision trees and associated algorithms
Overview of use of other algorithms to support decision making with respect to time, social media, and data etc.

Technology & Resources

Data storage mechanisms
Data mining tools such as Weka and / or PowerBi

Practical Content

Use of data mining tools and storage mechanisms to provide practical use of different algorithms to inform decisions.

PRACTICAL PRESENTATION: A 10-minute presentation which discusses the range of issues associated with traditional data mining (all illustrated by application to real world examples provided by industry partners/collaborators) (Learning Outcome 1).

WRITTEN REPORT: A 1500-word report including a proof of concept: an executive style report, emulating a real case scenario where students work in a realistic workplace setting, and are given a small data mining project and are required to write an executive report to explain the problem, its characteristics and how it has been approached (Learning Outcomes 2 and 3)

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.

1. Discuss and evaluate domain-specific techniques for processing Data Mining with respect to different disciplines.

Knowledge and Understanding

2. Develop systematic analytical approaches to problem solving, presenting logical and coherent written arguments for choices made in relation to Data Mining.

Problem Solving, Communication, Application

3. Demonstrate the knowledge to test analytical approaches applied to problem situations.

Analysis, Application

4. Demonstrate the ability to communicate data science concepts to a non-specialist audience.
Communication

Use of data mining tools such as Weka or PowerBi

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.

Core Text/Resource:
Harper, J. (2019) Data Science for Business: How to Use Data Analytics and Data Mining in Business, Big Data for Business, Springer, ISBN:9781386573241
Nigam, M, (2020), Advanced Analytics with Excel 2019: Perform Data Analysis Using Excel’s Most Popular Features (English Editions), BPB Publications, ISBN-10:9389845807
Knight, D, Pearson, M, Bradley, S, et. al (2020), Microsoft Power BI Quick Start Guide: Bring your data to life through data modelling, visualization, digital storytelling, and more, 2nd Edition, Packt Publishing; 2nd edition, ISBN-10:1800561571

Optional Text/Resource:
Du, H., (2013) Data Mining Techniques and Applications: an introduction Cengage ISBN 978-84480-891- Boobier, T, (2018), Advanced Analytics and AI: Impact, Implementation, and the Future of Work (Wiley Finance), Wiley; 1st edition, ISBN-10:9781119390305

Advanced/Supplementary Text/Resource:
Use of library resources such as LinkedIn videos

All resources will be updated regularly and available via a module KeyLinks online function

Data Informatics is the introduction to data modelling and the use of algorithmic methods and data mining that enables the prescription and prediction of data that can be used to the inform the decision making process and provide quality decisions. Students on this module will practically investigate these issues in order they form their own viewpoints and perspectives.