This module will address topics of:



Theory and Knowledge Exchange

Overview of data quality

Introduction to the different types of data (structured, semi and unstructured etc.)

Issues associated with big data and decision making

Overview of IOT sensors and their evolvement / development of automated decision making

Extraction, Transformation and Load (ETL) of data to ensure quality decision making

Overview of data mining principles and techniques: Data Mining related disciplines (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 data etc.

Technology and Resources

Data storage mechanisms

Data mining tools such as Weka and / or PowerBi

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

Presentation – A 10-minute presentation which discusses the range of issues associated with traditional data mining. All ideas and concepts discussed need to be illustrated by application to real world examples (Learning Outcome 1).

Written Report - A 1500 word report including a proof of concept: An executive report, emulating a real case scenario where you are in a work place 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).

There will be 6 hours lectures and labs/tutorials, to accompany self-directed learning using on-line material and case studies. There will be virtual lab sessions set up every week to gain practical experience and reinforce theory investigation using the Web.

1. Discuss domain-specific techniques used in Data Mining and the processing of data.

Knowledge and Understanding, Analysis



2. Apply Data Mining concepts to different domains through analysing provided study materials.

Problem Solving, Application, Reflection



3. Test and 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

Harper, J. (2019) Data Science For Business: How To Use Data Analytics and Data Mining in Business, Big Data For Business, Springer

Shan, J. et al. (2022) SQL for Data Analytics¿: Harness the Power of SQL to Extract Insights from Data, 3rd Edition. 3rd ed. Birmingham: Packt Publishing, Limited.

Dai, H.-N. et al. (2019) ‘Big Data Analytics for Large-scale Wireless Networks: Challenges and Opportunities’, ACM computing surveys, 52(5), pp. 1–36. Available at: https://doi.org/10.1145/3337065.

Du, H., (2013) Data Mining Techniques and Applications: an introduction Cengage



Advanced/Supplementary Text/Resource:

Use of library resources such as LinkedIn videos

Use of data mining tools such as Weka or PowerBi

Data analytics is the introduction to the use of algorithmic methods and data mining that enables the prescription and prediction of data that can be used to inform the decision-making process and provide quality decisions. This module will discuss issues with data analytics with respect to big data and IOT sensors and will be delivered from a heavily practical perspective.