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: 2022/3

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

Type of module:

Summary module description:

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

The module lead at NUIST is Dr Han Ying.

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 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, such as software development documentation, technical reporting writing, and project management. Ìý

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 and adopt/adapt analytics techniques to tackle the problems and evaluate solutions;


• validate and redefine solutions from analytics problems, so they canbe applied to new but similar problems;


• 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;


• apply the cloud computing skills for data management and distributed and parallel data processing.

Additional outcomes:

It is expected that students will also be able to

• recognise real world applications of big data analytics and also demonstrate how to deploy and evaluate data mining applications for big data on computer clusters;


• become familiar with the potential applications of cloud computing.

Outline content:

The contents are organised in two parts:

Part 1 – Big Data

• Introduction to big data analytics principles and challenges;


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


• Algorithms and tools for the analysis of fast streaming real time data;


• Techniques for building recommender systems.

Part 2 – Cloud Computing

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


• 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, and computational efficiency;


• Distributed Ledger technologies and applications.

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, Eibe Frank

Brief description of teaching and learning methods:

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