PE7049 - Big Data & Cloud Computing

What will I learn on this module?

Throughout this module you will develop knowledge and skills that will enable you to tackle a realistic big data problem, using some of the principal machine learning techniques and statistical approaches used in big data analysis. Furthermore, you will learn how to implement your solution using an industry leading Cloud computing provider together with appropriate distributed processing environments.

You will learn how to host multi-terabyte sized big datasets using a cloud service provider. This will includes provisioning a commercial cloud provider, and then mastering appropriate distributed operating systems, such as Hadoop. You will also learn approaches to processing and analysing big data, based on advanced statistical processing, supervised and unsupervised machine learning algorithms and other state of the art big data analytic methods. Such techniques include clustering algorithms, pattern based information extraction, linear and non-linear regression, and feature based models. Inevitably, much work on big data analysis is statistical, so you will therefore develop some relevant statistical understanding. As data visualization is frequently critical in helping to develop hypotheses about the data, you will also cover and apply problem relevant 2D and 3D visualization methods where appropriate to the particular datasets.

How will I learn on this module?

Each module session follows a similar structure of Learn, Explore Further and Apply. All learning materials and resources are accessible via our virtual learning environment. Indeed, through the e-learning portal you will be provided with resources in the form of scanned articles, links of books/articles/journals, PowerPoint lectures, word document, video lectures etc. relevant to your module. You will be given an on-line reading list, but will also be required to create your individual reading resource as well. You will be using a discussion board to share your work and create a knowledge base for your peers. You will be also using Wiki (a learning tool on e-learning portal) to form focus groups on module submission and assessment criteria.

You will learn through a combination of on-line methods to support learning, including recorded lectures, practical sessions in workshops and guided learning. Topics will normally be introduced in recorded 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 formative feedback on your practical achievements.
You will also conduct independent study, as it forms an important element of the module. Independent learning will centre upon identification and pursuit of areas of interest, by providing deeper/broader knowledge and understanding of the subject through a range of learning activities that might include extended reading, reflection, research etc.
You will access virtual classrooms at key points in the module (available on the e-learning portal) for live discussions and virtual taught sessions, which will be recorded and stored on the e-learning portal. These online sessions are timetabled and will deliver relevant knowledge, information and direction for you to fulfil the learning outcomes.

How will I be supported academically on this module?

A range of approaches are adopted to accelerate your learning in this module.

During the first week of this module, you will receive information about the module and Teaching & Learning Plan. The teaching and learning plan (TLP) sets out
• Learning outcomes and overall module and programme aims
• Teaching, learning and assessment strategy
• Teaching schedule
• Directed reading references (text and journals) and core texts for the module

During this module your module tutor will provide academic support including:
• Delivering on—line materials
• Providing guidance in relation to assignments
• Development of key resources, made available through the VLE
• Assessing assignments and assess or review any other agreed summative or formative outputs as appropriate

You will be supported by a team of academic experts and will have the opportunity to discuss your ideas and methods through project supervision. You will engage in a rich dialogue with tutors (and fellow students) and receive feedback on on-going work giving you the opportunity to respond directly and as part of your process.

Where appropriate, students may also be directed to engage with Study Skills +, or other resources offered through the University Student Support Services such as Dyslexia Support.

The Library is open 24 hours a day and E-Learning Portal houses all your module documents including your timetable. These services can be accessed on a range of devices

The module will also have an e-reading list which directs learners to specific reading for each session. This includes direct access to repositories, journal articles and other academic sources. You will also be provided with access to a significant set academic research sources via the Northumbria University library portal.

You will also have opportunities to receive formative feedback from your tutor in response to opinions you express and issues you raise during on-line workshop sessions and face-to-face or online tutorials. These formative feedback sessions are formally scheduled at key points throughout the module.

What will I be expected to read on this module?

All modules at Northumbria include a range of reading materials that students are expected to engage with. The reading list for this module can be found at:
(Reading List service online guide for academic staff this containing contact details for the Reading List team –

What will I be expected to achieve?

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.

How will I be assessed?

Summative assessment

You will research, design and construct, an appropriate solution for a given big data problem scenario by provisioning and configuring appropriate Cloud Computing resources. You will need to select appropriate algorithms and methods of visualizing the results to best satisfy a realistic task. This assignment will assess MLOs 1, 2, 3, 4 and 5

Formative assessment and feedback
Formative assessment will take the form of practical tasks in workshop exercises. Feedback and guidance will be provided on these.

Feedback will be provided on the formative assessment during the workshop sessions. Written feedback will be provided on the summative assessment.





Module abstract

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.

Course info

Credits 20

Level of Study Postgraduate

Mode of Study 2 years Distance Learning

Department Computer and Information Sciences

Location City Campus, Northumbria University

City Newcastle

Start Upcoming Intakes - Academic Year 22/23: Jan 23, Apr 23, July 23

Fee Information

Module Information

All information is accurate at the time of sharing.

Full time Courses starting in 2023 are primarily delivered via on-campus face to face learning but may include elements of online learning. We continue to monitor government and local authority guidance in relation to Covid-19 and we are ready and able to flex accordingly to ensure the health and safety of our students and staff.

Contact time is subject to increase or decrease in line with additional restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors, potentially to a full online offer, should further restrictions be deemed necessary in future. Our online activity will be delivered through Blackboard Ultra, enabling collaboration, connection and engagement with materials and people.


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