CSMBDNU: Big Data and Cloud Computing

Module code: CSMBDNU

Module provider: Computer Science; School of Mathematical, Physical and Computational Sciences

Credits: 20

Level: Postgraduate Masters

When you'll be taught: Semester 2

Module convenor: Professor Atta Badii, email: atta.badii@reading.ac.uk

NUIST module lead: Xinxin Liu, email: liuxinxin@nuist.edu.cn

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: No

Last updated: 27 June 2024

Overview

Module aims and purpose

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 management and advanced network-centric computing platforms. 

This module also encourages students to develop a set of professional skills, such as software development documentation and project management. Students will also be able to demonstrate their abilities in professional and effective writing to communicate data science concepts, solutions and outputs in technical reports and utilising knowledge and skills to continue learning and adapting to new data science technologies. 

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Describe concepts and models of distributed system and cloud computing as well as cloud computing design principles;
2. Identify and describe the challenges of big data management and appraise relevant tools and techniques to tackle such challenges;
3. Acquire an integrated perspective on big data processing in cloud computing platforms, this includes handling and processing large-scale data using big data framework and distributed computing technologies, designing, implementing, and validating cloud-based solutions for solving big data problems; and
4. Address socio-legal, security, privacy and trust issues involved in operating and using cloud services.

Module content

The module covers the following topics:  

• Introduction to cloud computing (e.g., IaaS, PaaS, SaaS, and AI-as-a-S) and big data 
• Cloud computing middleware, e.g., Hadoop, Map/Reduce 
• Cloud computing design features, such as consistent hashing and partition for computational processing 
• Big data platform, e.g., Spark, for handling large-scale data in a semi-structured and unstructured-data mode 
• Cloud-based big data access and performance efficiency 
• Design cloud platform with big data governance embedded 

Structure

Teaching and learning methods

Material will be delivered via lectures and practical classes on a weekly basis. The lectures will introduce students the theories, concepts and underpinning principles specified in the indicative content. Students will be supervised in the practical sessions to apply the concepts and principles to a given problem context and develop a technical solution. Additional resources will be available on Blackboard for self-study.  

Study hours

At least 48 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 50% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• 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 three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/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.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Weekly practical exercises (some may be in the form of groupwork) will be used as formative assessment. Feedback on weekly practical exercises will be given to students which will act as feedforward for the coursework assessments. 

Reassessment

Additional costs