MSc Big Data Technologies

Embark on a career in a leading-edge field and master the exciting and challenging world of big data!

Big data techniques are revolutionising how organisations and industries acquire and analyse data, offering valuable insights into how people live, work, play, travel, shop and exercise. These insights are valuable to marketers, researchers, designers, city planners, app developers, educators and many more.

GCU London's MSc in Big Data Technologies helps you build the fundamental knowledge and practical skills for success in this fast-growing field. You'll develop competence in a range of emerging technologies: big data, cloud computing, data analytics, iartificial ntelligence and machine learning, the internet of things and data visualisation. You'll learn from the experts; GCU is internationally recognised for the strength of its research in these exciting subjects, driving 21st-century innovation.

With both full-time and part-time study available, the course is ideal for someone with a background in computer science, software engineering, web technologies or computer engineering who wants to enhance or update their skills. Those with backgrounds in mathematics and electronics are also well suited.

The up-to-date curriculum keeps a career-focused approach, so you'll gain valuable skills you can immediately put to work in the industry.

  • Utilise leading-edge tools and technologies from companies such as Google and IBM, including the latest AI and ML techniques 
  • Apply a wide range of industry-standard open-source development platforms and databases 
  • Confidently analyse and visualise data sources, to deliver new levels of understanding
Your expertise in big data will enable you to provide new insights into human behaviour and psychology, which can help us build stronger and happier societies across the globe. Your work could shape smart, sustainable cities; remove barriers to education; help people make healthier choices day-to-day; improve public health and so much more. All meaningful ways of contributing to the Common Good.

Graduate prospects

When you graduate, you'll be a competitive candidate for roles as a systems developer, architect or administrator in data and analytics. You'll find opportunities in a diverse range of industries: engineering, pharmaceuticals, finance, healthcare, retail, security, smart environments and more.

Full-time students complete eight taught modules; four in trimester A and four in trimester B - and undertake an MSc dissertation project in the trimester following the completion of taught modules. Part-time students complete eight taught modules; four in year one, four in year two and undertake an MSc dissertation project over two trimesters following the completion of taught modules.

Module list

Trimester A: Big Data Landscape, Software Development for Data Science, Artificial Intelligence and Machine Learning, Data Ethics and Research Methods.

Trimester B: Cloud Computing and Web Services, Internet of Things, Big Data Platforms, Data Visualisation. 

Final Trimester: Dissertation

Download the Programme Specification for a detailed breakdown of its structure, what you will learn and other useful information.

Due to significant demand, applications for this course are now closed to international applicants for January 2022. Please consider our following intake in January 2023.

Study Options

  • 2021/22

Award

Mode of study

Duration

Start date

Location

MSc
Full Time
16 Months
31 Jan 2022
GCU London
MSc
Part Time
2 Years
31 Jan 2022
GCU London
Award Mode of study Duration Start date Location  
MSc Full Time 16 Months 31 Jan 2022 GCU London Enquire Apply
MSc Part Time 2 Years 31 Jan 2022 GCU London Enquire Apply

Study Options

  • 2022/23

Award

Mode of study

Duration

Start date

Location

MSc
Part Time
2 Years
Jan 2023
GCU London
MSc
Full Time
1 Year
Sep 2022
GCU London
Award Mode of study Duration Start date Location  
MSc Part Time 2 Years Jan 2023 GCU London Enquire Apply
MSc Full Time 1 Year Sep 2022 GCU London Enquire Apply

All entry requirements listed here should be used as a guide and represent the minimum required to be considered for entry. Applicants who are made a conditional offer of a place may be asked to achieve more than is stated.

Entry Requirements

UK honours degree 2:2 (or equivalent) in computing with software development, for example, computing, computer science, software engineering, web technologies and computer engineering. We also welcome applicants with Industry qualifications/experience within the GCU Recognition of Prior Learning (RPL) Policy.

English language

Academic IELTS score of 6.0 (or equivalent) with no element below 5.5.

Please note: if you are from a majority English speaking country, you may not be required to provide further proof of your English Language proficiency.

Additional information

Other academic and vocational qualifications

Each application to GCU is considered on an individual basis. If you do not have the typical academic entry qualifications, but can demonstrate relevant work experience and/or credits from recognised professional bodies, you may be eligible to enter this course via the University's Recognition of Prior Learning scheme.

The tuition fees you pay are mostly determined by your fee status. What is my student fee status?

Annual full-time tuition fees

2022/21
Home (UK including Scottish): £8,700
International: £14,500

2021/22
Home (UK including Scottish): £8,700
International: £13,500

Fees are subject to change and published here for guidance only.

Additional costs

As a student at the University, there are additional fees and costs which may or may not apply to you, but that you should be aware of.

View additional costs

Scholarships

We provide high-quality education for a fair price; as the University for the Common Good, we are committed to offering accessible higher education for talented students by keeping our tuition fees low and providing a generous scholarship package of £2 million per year.

View scholarships

This course will equip you with the fundamental knowledge and skills of the core technologies for harnessing the big data challenges, including capture, curation, storage, integration, sharing, search, analysis, mining of large distributed unstructured datasets. Studies on this course are supported and enhanced uniquely by the University’s internationally excellent research strengths, especially in cloud computing, cyber security, Internet of Things and cyber-physical systems.

 
Of parallel importance in our course is to cultivate the professionalism which is expected within the industry. With all the future-proofing capabilities synthesised coherently together, graduates of the MSc in Big Data Technologies will be amongst the most highly-skilled ICT graduates, responding confidently to the needs and challenges in diverse big data application domains.

GCU is committed to supporting and encouraging students from all backgrounds to undertake studies in STEM courses. Addressing STEM skills and gender gap is a key priority for GCU and we have various initiatives in place to drive forward a skilled and diverse workforce.

We have a range of scholarships at postgraduate level to support students financially.

You can read more about our focused activities and support at:
The School of Computing, Engineering and Built Environment is proud to be an Athena Swan Bronze Award holder in recognition of its commitment to promoting gender equality among students and staff.
This course features employer events or talks on campus.

This degree has been accredited by BCS, The Chartered Institute for IT. Accreditation is a mark of assurance that the degree meets the standards set by BCS. An accredited degree entitles you to professional membership of BCS, which is an important part of the criteria for achieving Chartered IT Professional (CITP) status through the Institute. Some employers recruit preferentially from accredited degrees, and an accredited degree is likely to be recognised by other countries that are signatories to international accords.

This course makes use of industry-standard facilities including: IBM, Microsoft, Google, Apache, AWS, Weka, Databricks, MongoDB.
This course has been designed with industry input.

Assessment is used to demonstrate achievement of learning outcomes. The methods of assessment include class tests, coursework assignments, practical tests and technical reports. Practical implementation and evaluation form a significant part of the assessment for the taught modules and for the work of the MSc dissertation.