This module will focus on the following topics:



How Big Data has moved on from the over emphasis on V’s in definitions
Structured, semi structured and unstructured data

Issues with respect to why Big Data cannot be processed within typical relational databases

Data Warehousing, data lakes and structures to process Big Data

Dimensional modelling including slowly changing dimensions
NOSQL, Sharding and other technologies to improve big data processing

Big Data and the impact of cloud storage and processing

Use of tools to build big data analytics (Visualisation)

Big Data in action and how different organisations are using Big Data in fields such as banking, travel, networking, medical,

IoT and generation of Big Data

Presentation - A 10 minute presentation which discusses the range of issues associated with traditional data processing, Big Data and IoT. All illustrated by an undertaken case study project (Learning Outcomes 1, 2 and 4).

Written Report - A 1500 word report including discussion of a proof of concept case study scenario. This is an executive style report, emulating a real case scenario where you are in a work place and are given a small Big-Data analytics project and are required to write an executive report to explain the problem, its characteristics and how it has been approached (Learning Outcomes 1 ,3 and 4).

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. 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 practically apply to a given scenario.

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

4. Create artefacts that reflect on undertake research in the area of Big Data analytics.

Application

Problem Solving

NoSQL Datastores
Relational datastores

AWS library and teaching materials

Clarke, E. (2022), Everything Data Analytics-A Beginner's Guide to Data Literacy: Understanding the Processes That Turn Data Into Insights (All Things Data), ¿Kenneth Michael Fornari

Aspen-Taylor, S. (2022), Data and Analytics Strategy for Business: Unlock Data Assets and Increase Innovation with a Results-Driven Data Strategy, Kogan Page; 1st edition

Theobald, A. (2022), Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy: (Introduction to Data, Data Visualization, Business Intelligence & ... Science, Python & Statistics for Beginners), Independently published¿¿

Maheshwari, A. (2019), Big Data Made Accessible: 2019 edition Kindle Edition
Sergio (2021), Data Science for Economics and Finance Methodologies and Applications

AWS Certified Data Analytics Study Guide, 2020, Abbasi

Data Protection Act 2018 and GDPR 2018

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.