KF7032 - Big Data and Cloud Computing

You will learn through a combination of methods to support learning, including lectures, practical sessions in workshops and guided learning. Topics will normally be introduced in lectures and explored through practical exercises (helping you develop the practical skills needed) and guided learning activities. You will be encouraged to develop independent learning skills and the development of critical analytic approaches to the big data and cloud computing area.

More specifically, you will work in teams using a leading cloud services provider and big data analysis techniques as the basis of your practical work, giving you immediate saleable skills. Staff will support your learning through verbal feedback on your practical achievements.

All module material will be available on the eLearning Portal (ELP) so that you can access information when you need to. The university library offers support for all students through its catalogue and an Ask4Help Online service.

Staff will support you in the practical sessions, providing advice and feedback on your progress and engaging in discussion with you, to examine your ideas and those of others as your tutors value your input and opinions. You will be strongly encouraged to engage in further study by yourself or with other students outside of class time to become an independent learner. This is an essential capability in every area of Computing, whose utility will long outlive the detail of current technical approaches.

This module will use and promote an eLP (Blackboard) based discussion forum. This will be configured to encourage you, other students and academic staff to participate in discussion about the subject matter of the module.

You will read books, scientific refereed articles and conference papers. You will be expected to go beyond blogs, way beyond web pages and to develop independent critical research capabilities. This capacity to research and critically analyse formal literature will stand you in good stead when confronted with the swathes of uncritical marketing white papers with which the modern IT professional has to contend.

All modules at Northumbria include a range of reading materials with which students are expected to engage. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk

Knowledge & Understanding:
1. Apply big data analytic algorithms, including those for visualization and cloud computing techniques to multi-terabyte datasets.
2. Critically assess data analytic and machine learning algorithms to identify those that satisfy given big data problem requirements

Intellectual / Professional skills & abilities:
3. Critically evaluate and select appropriate big data analytic algorithms to solve a given problem, considering the processing time available and other aspects of the problem.
4. Design and develop advanced big data applications that integrate with third party cloud computing services

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):

5. Critically assess and interpret primary research to identify its applicability to a given big data problem scenario.

The assessment will be via a Portfolio (100%) comprising summative and semi-formative elements where a semi-formative element is a formative task critical to the MLOs that attracts a nominal mark to ensure engagement.

The portfolio will assess all Module Learning Outcomes. In the summative element big data methodology will be applied to address topical social issues using big datasets from (e.g.) data.gov.uk. These questions might include the relationship between poverty and burglary, or whether violent crime is really increasing nationwide etc. That is, student will design, construct and critically justify an appropriate solution for a given big data problem scenario by provisioning and configuring appropriate Cloud Computing resources. Appropriate algorithms and methods of visualising the results to best answer the research question will require selection and justification.

There are two semi-formative assessment tasks. One takes the form of group-oriented study where the module team will pose several topical questions during the module such as ‘Is personal interaction monitoring feasible and responsible?’ student groups will discuss the questions between themselves and with the module team. Responses will be collected for a group reflective diary relating answers to current thinking in both computing and wider academic literature. Brief written feedback will be provided on the summative assessment.
In the second semi-formative task each individual student will post their task-focused exercise worksheet from weeks 1-4(with integral documentation and theory) on a Blackboard discussion board. Using a supplied marking scheme (based on the MLOs), student peers will mark the submission above and below their own on the list (First and last posted will receive a tutor mark). Credit will be given for reviewing appropriately, whilst the average peer review mark will receive course credit.

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Big Data is the colloquial term used to describe the acquisition of knowledge, insights and understanding gained through identification of patterns in huge, multi-terabyte datasets. In this module you will develop knowledge and skills that will enable you to tackle a realistic big data problem. Furthermore, you will learn how to implement your solution using an industry leading Cloud computing provider together with appropriate distributed processing environments such as Hadoop. Frequently a first step in Big Data analysis insight is gained through visualizing the data. This may give insights into appropriate analytic approaches. You will also learn some of the principal machine learning techniques and statistical approaches used in big data analysis.