1. Demonstrate critical Knowledge and understanding of the concepts of Big Data, the challenges it presents, and the legislation that applies to its collection storage and use.
Knowledge and Understanding¿

2. Demonstrate, using logical and coherent arguments, the ability to critically evaluate the potential for AI and Machine Learning to contribute to important global interests such as the smart management of cities, sustainability, and climate change.
Learning, Analysis, Problem Solving, Communication

Assignment 1
A Presentation (possibly group): 20 minutes which discusses the range of issues associated with Big Data, AI and Machine Learning, including questions and answers, all illustrated by an undertaken project. (30%) (Learning Outcome 1).

Assignment 2
A 2,000-word Report, emulating a real case scenario where you are in a workplace and are given a small Big-Data analytics AI and Machine Learning project and you are required to write an executive report to explain the problem, its characteristics and how it has been approached. (70%) (Learning Outcome 2).¿

The module will explore the following

Introduction to concepts of Big Data¿
How big data is captured, transformed, stored and analysed
Use of IoT to generate Big Data¿
Risk assessment when merging IoT with industry, and the environmental effects
Big Data in action and how different organisations are using Big Data in fields such as banking, travel, medical,¿climate change, sustainability, and ethical responsibility
How big data contributes to sustainable management of smart cities
Introduction to AI, Machine Learning and Business Intelligence
Visualisation through dashboards
Application of AI and Machine learning to¿climate change, sustainability
Ethical and social responsibilities, Legislation
Understanding implications of AI for business strategies

This module will enable students to apply their knowledge in small close-to-real world projects emulating real work scenarios which require using domain-specific techniques for applying Data Science, AI and Machine Learning to different domains. The module will also focus on why Big Data cannot be simply processed by using standard database management systems.

The content will be flexible but serves the anticipated outcomes. Flexibility in terms of the case studies is essential to address the student’s specific needs, and to keep up with the rapidly evolving nature of the subject domain. Therefore, developmental workshops will be used:¿

a. To discuss case studies¿
b. To test and experiment with some use cases and small examples, allowing for peer learning and hands-on practice.¿
c. To work on student projects¿
d. To deliver presentations for the assessment.¿

The VLE will be used for discussing the case studies outside classes and provide the students with relevant material to support their learning.¿

You will undertake ‘formative’ assessments during the module to help you monitor your learning and provide you and us with ongoing feedback on your progress, that helps you prepare for the ‘summative assessment(s) during or at the end of the module.

These are indicative only. Texts are updated on an annual basis and when you start to study this module, you will be referred to an online reading list, currently provided through Keylinks. You are advised not to buy any textbooks for this module without checking the online reading list.

Agrawal, A (2018) Prediction Machines: The Simple Economics of Artificial Intelligence Harvard Business Review Press ISBN: 978-1633695672

Maheshwari, A. (2019) Big Data Made Accessible: 2019 edition¿Kindle Edition, ASIN:¿B01HPFZRBY¿

Cox, I. (2016) Visual Six Sigma: Making Data Analysis Lean 2nd Edition John Wiley and Sons ISBN:¿1118905687;¿9781118905685

Du, H. (2013) Data Mining Techniques and Applications: an introduction Cengage ISBN 978-84480-891-5

Hoeren, T. (2017) Big Data in Context: Legal, Social and Technological Insights Springer Nature ISBN: 978-3319624600

Marr, B. (2016) Big Data in practice: how 45 successful companies used Big Data analytics to deliver extraordinary results Wiley ISBN:¿1119231388;¿9781119231387

Richterich, A. (2018) The big data agenda: data ethics and critical data studies University of Westminster Press ISBN: 978-1-911534-72-3

Thompson, J (2020) Building Analytics Teams: Harnessing analytics and artificial intelligence for business improvement Packt ISBN: 978-1800203167

Yao, M. (2018) Applied Artificial Intelligence: A Handbook for Business Leaders TOPBOTS ISBN: 978-0998289021

Data Protection Act 2018 and GDPR 2018