CSMBD21NU-Big Data and Cloud Computing

Module Provider: Computer Science
Number of credits: 20 [10 ECTS credits]
Level:7
Semesters in which taught: Semester 2 module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2023/4

Module Convenor: Dr Carmen Lam
Email: carmen.lam@reading.ac.uk

NUIST Module Lead: Wenwen Liu
Email: w.liu@nuist.edu.cn

Type of module:

Summary module description:

This module covers the topic of big data and advanced computing.

 

Aims:

The massively increased uptake of computing, with devices at all scales of operation, has driven the development of large-scale distributed systems capable of meeting the demands for handling scalable parallel data analysis and processing and supporting the execution of analytical algorithms on computer clusters such as Hadoop. This module aims to introduce the concepts and design principles for big data analytics and advanced computing platforms.

This module also encourages students to develop a set of professional skills, become familiar with Big Data analysis pipelines within a cloud computing service environment, effective Data Analytics results visualisation for decision support, software development documentation, technical report writing, and project management.  

Assessable learning outcomes:

It is expected that students will be able to:

• Identify and describe challenges of analysing big data and appraise relevant algorithms, tools and techniques to tackle these challenges;


• Analyse complex data in structured, semi-structured and/or unstructured format, become familiar with data analytics workflows incorporating data engineering, modelling and results visualisation within a Big Data Analytics Cloud Services environment, demonstrate effective adoption and adaptation of analytics techniques to tackle the problems specified, display the required results and evaluate solutions;


• Understand the rationale for the design choices to be made in building live web-scale service-oriented architectures;


• Acquire an integrated perspective on data processing in cloud computing platforms;


• Address socio-legal, security, privacy and trust issues involved in operating and using cloud services;


• Validate and redefine solutions from analytics problems, so that they can be applied to new but similar problems.

Additional outcomes:

It is expected that students will also be able to develop a set of professional skills, such as software development documentation, technical report writing, and project management.

Outline content:

• Introduction to distributed and parallel computing; Cloud Computing (IaaS, PaaS, SaaS, AI-as-a-S);


• Framework design of large-scale distributed systems to support web-scale service-oriented architectures;


• Security and privacy protection challenges in Cloud Computing;


• Cloud Computing middleware, Map/Reduce; RESTful systems;


• Cloud computing design features, such as consistency, availability and partition tolerance in distributed information systems, consistent hashing, parallelism and computational efficiency;


• Trustless Distributed Ledger Technologies such as Blockchain and its applications;


• Cloud-based big data analytics principles and challenges;


• Techniques and tools for large data set analysis, including unstructured data analysis;


• Algorithms and tools for stream processing and real-time analytics.

Recommended Textbooks:

• Data Mining, Concepts and Techniques, (Second Edition) Jiawei Han, Micheline Kamber Morgan Kaufmann Publishers, March 2006. ISBN: 978-1-55860-901-3


• Mahout in Action Sean Owen, Robin Anil, Ted Dunning, and Ellen Friedman ISBN 9781935182689

Further reading:

Data Mining: Practical Machine Learning Tools and Techniques (Second Edition) Ian H. Witten, EibeFrank

 

Brief description of teaching and learning methods:

The module comprises lectures, practical sessions, and a project-based assignment.

Contact hours:

	Semester 1	Semester 2
Lectures		20
Practicals classes and workshops		10
Guided independent study:
Wider reading (independent)		20
Wider reading (directed)		20
Exam revision/preparation		20
Advance preparation for classes		20
Preparation for tutorials		20
Preparation of practical report		30
Essay preparation		30
Reflection		10
Total hours by term	0	200
Total hours for module	200

Summative Assessment Methods:

Method	Percentage
Written exam	50
Set exercise	50

Summative assessment- Examinations:

One 2-hour examination paper.

Summative assessment- Coursework and in-class tests:

One project-based assignment.

Formative assessment methods:

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy 'Penalties for late submission for Postgraduate Flexible programmes', which can be found here: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmissionpgflexible.pdf
The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

A mark of 50% overall.

Reassessment arrangements:

One 3-hour examination paper. Note that the resit module mark, used to determine progression, will be the higher of (a) the mark from this resit exam and (b) an average of this resit exam mark and previous coursework marks, weighted as per the first attempt (50% exam, 50% coursework).  

Additional Costs (specified where applicable):

1) Required text books: 

2) Specialist equipment or materials: 

3) Specialist clothing, footwear or headgear: 

4) Printing and binding: 

5) Computers and devices with a particular specification: 

6) Travel, accommodation and subsistence: 

Last updated: 18 April 2023