Module Catalogue 2018/19

CSC8101 : Big Data Analytics

  • Offered for Year: 2018/19
  • Module Leader(s): Dr 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

The ability to describe and discuss:
- The scale of big data and its progression in time, relative to diverse data domains, primarily e-science and social science.
- The need and structure of scalable data architectures, and their applications to large-scale scientific and social data processing.
- The structure of selected data mining algorithms and their implementation on data-parallel architectures, with special emphasis on strategies to promote strategies for collective intelligence.

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.

Graduate Skills Framework

Graduate Skills Framework Applicable: Yes
  • Cognitive/Intellectual Skills
    • Critical Thinking : Assessed
    • Data Synthesis : Present
    • Active Learning : Assessed
    • Numeracy : Present
    • Literacy : Present
    • Information Literacy
      • Source Materials : Present
      • Synthesise And Present Materials : Present
      • Use Of Computer Applications : Assessed
  • Self Management
    • Self Awareness And Reflection : Present
    • Planning and Organisation
      • Goal Setting And Action Planning : Present
    • Personal Enterprise
      • Innovation And Creativity : Present
      • Independence : Present
      • Problem Solving : Assessed
  • Interaction
    • Communication
      • Written Other : Assessed
  • Application
    • Occupational Awareness : Present

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture171:0017:00Lectures
Guided Independent StudyAssessment preparation and completion171:0017:00Lecture follow up
Scheduled Learning And Teaching ActivitiesPractical81:008:00Practicals
Scheduled Learning And Teaching ActivitiesSmall group teaching51:005:00Presentations on research papers
Guided Independent StudyProject work371:0037:00Coursework / Lab reports
Guided Independent StudyIndependent study161:0016:00Background reading
Teaching Rationale And Relationship

Lectures will be used to introduce the learning material and for demonstrating the key concepts by example. Students are expected to follow-up lectures within a few days by re-reading and annotating lecture notes to aid deep learning.

This is a very practical subject, and it is important that the learning materials are supported by hands-on opportunities provided by practical classes. Students are expected to spend time on coursework outside timetabled practical classes.

Students aiming for 1st class marks are expected to widen their knowledge beyond the content of lecture notes through background reading.

Students should set aside sufficient time to revise for the end of semester exam.

Reading Lists

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Practical/lab report2M50Batch data processing
Practical/lab report2M50Stream data processing
Assessment Rationale And Relationship

The assessment structure is designed to maximize engagement of the students with an area of technology that is evolving very rapidly. This is achieved in two ways.

Practical/lab report 1. This is designed to provide students with hands-on experience on programming using MapReduce (Hadoop) as well as graph databases, which are going to be key to employability in this area going forward.

Prof skill assessment 1. This involves asking students to read, review, and present to the class one paper that addresses cutting edge issues in the area of big data analytics. These can be either research papers or technical industry articles. The role of the lecturer here is to steer the focus and moderate the student-led sessions.

Practical /Lab report 2 is designed to assess overall theoretical knowledge of the material presented in the module. Thus it will consist of questions on the content of the module that will have been presented in class.


Past Exam Papers

General Notes


Disclaimer: The information contained within the Module Catalogue relates to the 2018/19 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 2018/19 entry will be published here in early-April 2018. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.