CSMBD16-Big Data Analytics

Module Provider: Computer Science
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites: CSMCC16 Cloud Computing and CSMDM16 Data Analytics and Mining
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.

Aims:

The analysis of Big Data is not just the analysis of very large data sources, even though this is part of it. Typically data comprises four aspects, Volume, Velocity, Variety, and Veracity. This view on Big Data is commonly accepted. Volume refers to the actual size of the data, here computationally well scaling methods are needed; Velocity refers to the very fast generation of data, here data stream processing methods are needed for time critical applications; Variety refers to the different types of data, possibly unstructured data such as video streams, click streams or audio files; Veracity refers to the challenge of establishing the trust of decision makers in the Knowledge extracted from Big Data Analytics techniques.Ìý

This unit’s aim is to address these aspects and challenges of Big Data Analytics by introducing scalable parallel data mining algorithms which can be executed on computer clusters such as Hadoop; the introduction of data stream mining techniques and algorithms for the analysis of high velocity data; the introduction to sentiment analysis techniques for unstructured data such as micro-blogging data and social network data; and the introduction of scalable recommender systems. A further aim of the unit is to introduce software systems used for Big Data Analytics such as KNIME,ÌýMOA, MapReduce and Spark.

Assessable learning outcomes:

• The students will be able to discuss, identify and describe challenges of Big Data Analytics. Furthermore the students will be able to appraise relevant algorithms, tools and techniques to tackle these challenges.


• The students will learn how to apply Big Data Analytics techniques and algorithms to solve challenges in Big Data Analytics.


• The students will be able to analyse complex Big Data Analytics problems, develop and appraise analytics techniques to tackle the problems and evaluate solutions.


• The students will learn how to redefine and modify solutions from analytics problems, so they can be applied to new but similar problems.

Additional outcomes:

The students will 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.

Outline content:

• Introduction to Big Data Analytics principles and challenges;


• Data mining techniques and tools for Large Data Set Analysis, in particular parallel data mining techniques;


• Data mining algorithms and tools for the analysis of fast streaming real time data;


• Data mining techniques for building recommender systems;


• Data mining techniques and algorithms for unstructured data analysis.

Reading List: Essential Text:

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 and practical sessions. The lectures introduce the basic concepts, methodologies of advanced Data Analytics. Students will gain more insights and skills in the taught subjects through the practical sessions. A project-based assignment will allow students to apply the concepts learned to a practical case.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures		10
Practicals classes and workshops		10
Guided independent study:		80
Ìý	Ìý	Ìý	Ìý
Total hours by term	0		0
Ìý	Ìý	Ìý	Ìý
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Written exam	50
Set exercise	50

Summative assessment- Examinations:

One 1.5 hour examine paper in May/June.

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: /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: /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 2-hour examination paper in August/September. Note that the resit module mark 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).ÌýÌý

Additional Costs (specified where applicable):

1) Required text books:ÌýÌý

2) Specialist equipment or materials:ÌýÌý

3) Specialist clothing, footwear or headgear:ÌýÌý

4) Printing and binding:ÌýÌý

5) Computers and devices with a particular specification:ÌýÌý

6) Travel, accommodation and subsistence:ÌýÌý

Last updated: 22 September 2022