1) Critically discuss and evaluate techniques used for processing Big Data and information.
Knowledge and Understanding

2) Research, analyse and critically evaluate best practices in Big Data analytics and show how to apply these practically.
Learning, Analysis, Problem Solving, Reflection

3) Present logical and coherent written arguments for choice of approaches in finding solutions to Big Data design problems.
Problem Solving, Communication, Application

Assignment 1
A Presentation: 10 minutes which discusses the range of issues associated with traditional data processing, Big Data and IoT. All illustrated by an undertaken project. Learning Outcomes 1 and 2.

Assignment 2
An 1500 word Report including a proof of concept: is more an executive report, emulating a real case scenario where you are in a work place and are given a small Big-Data analytics project and you are required to write an executive report to explain the problem, its characteristics and how it has been approached. Learning Outcomes 1 to 3.

This module develops the following –

- Introduction to concepts of Big Data
- Introduction to concepts of relational data and issues with respect to why Big Data cannot be processed within typical relational databases
- Entity relationship modelling and normalisation
- OLTP and OLAP
- Big Data in action and how different organisations are using Big Data in fields such as banking, travel, networking, medical, and how the IoT can generate Big Data
- Preparing data and ensuring quality

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 to different domains. The module will also focus on why Big Data cannot be simply processed by using standard database management systems.

1 x 5-hour practical session a week. This workshop will cover the learning content described above. 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 also 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.

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.

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
Marr, B. (2016 ) Big Data in practice : how 45 successful companies used Big Data analytics to deliver extraordinary results Wiley ISBN: 1119231388; 9781119231387
Data Protection Act 2018 and GDPR 2018

NoSQL Datastores
Relational datastores

None