Module Catalogue 2020/21

CSC8101 : Big Data Analytics

  • Offered for Year: 2020/21
  • Module Leader(s): Professor Paolo Missier
  • Owning School: Computing
  • Teaching Location: Newcastle City Campus
Semester 2 Credit Value: 10
ECTS Credits: 5.0
Pre Requisites
Pre Requisite Comment


Co Requisites
Co Requisite Comment



The aim of Big Data Analytics is to analyse large amounts of data in order to extract useful information. Examples include analysing the world wide web to power web search engines, optimising the design of e-commerce sites by analysing user activity, and processing “open linked data” released globally both by governments in order to improve public services, as well as by research organizations in order to improve data sharing. Whilst data analysis has been an important topic for many decades, three developments have led to a surge of interest in new algorithms and methods. Firstly, there has been an explosion in the quantity and variety of data generated by organisations, programs and sensors: the web is one example of this. This has placed the processing of this data beyond existing approaches. Secondly, cloud computing has provided a new type of dynamically scalable platform on which to parallelise data analysis. Thirdly, there is enormous potential for insight and action deriving from the real-time analysis of data – such as from sensors, social media and e-commerce.
This module focusses on the algorithms, technologies and architectures required to analyse “big data”, with a particular focus on cloud-based solutions.

Outline Of Syllabus

- Scalable data management architectures
- Overview of data-parallel problems in e-science
- Patterns and technology for exploiting cloud infrastructure on data-parallel problems
- Graph databases and their application to social media analysis
- Scalable real-time data processing

Learning Outcomes

Intended Knowledge Outcomes

You will learn to competently discuss the merits of various technologies for Big Data Processing, with respect to specific Data Analytics challenges. Specifically:
- You will learn about the scale of big data and its progression in time, in diverse data domains, including areas of science
- You will learn how to configure and best exploit scalable data architectures in view of their application to challenges in multiple application domains
- You will learn how to combine big data analytics techniques with machine learning approaches to predictive analytics, from the perspective of both Data Science and Data Engineering

Intended Skill Outcomes

- The ability to design, implement and evaluate big data analysis systems
- The ability to apply big data analysis to specific problems in support of science and social science.

Teaching Methods

Module leaders are revising this content in light of the Covid 19 restrictions.
Revised and approved detail information will be available by 17 August.

Reading Lists

Assessment Methods

Module leaders are revising this content in light of the Covid 19 restrictions.
Revised and approved detail information will be available by 17 August.


Past Exam Papers

General Notes


Disclaimer: The information contained within the Module Catalogue relates to the 2020/21 academic year. In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described. Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2021/22 entry will be published here in early-April 2021. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.