Big Data Science and Technology
Attendance mode: Part-time
Start date: September
Faculty of Engineering & Informatics
Develop the skills you need fora career in this fast emerging field of data science.
This programme will deepen your understanding of advanced software development, systems for big data analytics, statistical data analysis, data mining, data privacy and security, data visualisation and exploration.
It is an interdisciplinary programme, designed for students with a first degree in subjects such as:
- Computer Science
- Electrical Engineering
Our teaching is strongly linked to research within the Faculty of Engineering and Informatics, which includes aspects of applied computing, theoretical computer science, and communications and networks. Statistical analysis of data across various disciplines is a central theme of our research.
You can tailor your studies to particular areas of interest or career aspirations through our range of optional modules, and in the final dissertation (which is a significant piece of project work).
On graduation you’ll be ready and able to develop solutions to challenges in big data analytics and big data systems.
We also offer a one-year, full-time version of the programme.
Employability is a key focus of the programme here at Bradford. We collaborate with industry partners through our Industry Advisory Board; their input shapes the curriculum, ensuring you gain the skills employers in this sector value and / or are in shortage. You’ll have the opportunity to contribute live industry projects as part of the programme.
Our facilities are cutting-edge, and include specialised labs such as our new Internet of Things lab and our state-of-the-art Ethical Hacking lab. Our Computing Enterprise Centre offers students the opportunity to work on real-world projects using powerful hardware and industry standard software, under the supervision of subject experts.
Our staff are research active and embed outcomes of their world leading research into the design of our courses, ensuring that you are equipped not just for the job market today, but are well prepared for the future.
2:2 or above in computer science, computer engineering, informatics or other computer-related subjects from an approved degree-awarding body
Candidates who do not fulfil the normal entry requirements but have extensive industrial experience in a related area are considered on an individual basis.
English language requirements:
Minimum IELTS 6.0 or equivalent.
If you do not meet the IELTS requirement, you can take a University of Bradford pre-sessional English course. See the Language Centre for more details.
The modules for this course can be found in the latest programme specification.
The programme is intended to equip graduates with the cutting-edge knowledge and skills to work in the industry as a Data Scientist, Big Data Architect, or Big Data Analyst.
- Software Development (20 credits) - core
The aim of this module is to introduce basic programming skills through a modern object-oriented programming language, design methods and tools.
- Big Data Systems and Analytics (20 credits) core
To enable you to gain advanced knowledge and developed the skills on big data, concerning the architectures of big data systems, the management for big data projects, and computational approaches for big data analytics.
- Information Theory and Data Communication (20 credits) - optional
The aim of this module is to introduce and apply basic principles of information theory for data communication. The focus is on concepts and techniques for reliably transmitting and efficiently processing data.
- Security, Privacy and Data Protection (20 credits) - optional
- Mobile Applications (20 credits) - optional
To communicate knowledge of the technology necessary and available to deliver applications and web content to mobile devices.
Students will be eligible to exit with the award of Postgraduate Certificate if they have successfully completed 60 credits, to include 40 core credits at level 7, and achieved the award learning outcomes.
- Statistical Data Analysis (20 credits) - core
To acquire knowledge of statistical data analysis, statistical learning methods and data analytics techniques for the hypotheses generation and hypotheses testing, in order for making appropriate statements/predictions.
- Data Mining (20 credits) - core
To develop a thorough understanding of the theory and practice of advanced data processing techniques. This will include description and critical evaluation of data analysis, data cleaning, data representation and data manipulation issues for mining, data processing, pattern or correlation exploration and data mining issues.
- Concurrent and Distributed Systems (20 credits) - optional
To introduce the nature and applications of concurrent programming with typical problems requiring synchronisation of, and communication between, concurrent processes; to introduce a variety of language primitives for inter-process communication and synchronisation, and to illustrate their applications.
- Data Visualization (20 credits) - optional
The aim of this module to provide you with key principles and techniques of data visualization. By completing this module you will gain advanced knowledge and skills on developing methods and techniques of data visualisation to improve comprehension, communication, and decision making in big data applications.
Students will be eligible to exit with the award of Postgraduate Diploma if they have successfully completed at least 120 credits to include 80 core credits at Level, and achieved the award learning outcomes.
Degree of Master
- Dissertation (60 credits) - core
Students will be eligible for the award of Degree of Master if they have successfully completed 180 credits and achieved the award learning outcomes.
Learning activities and assessment
You'll learn through a mixture of formal lectures, practical lab sessions, tutorials and seminars.
Some modules involve supervised group work, usually with an assigned academic staff member for each group.
Most modules are related to research in the school.
All modules require students to undertake independent study, supported through distance learning technologies such as our Virtual Learning Environment. Reading lists and suggested resources for independent study provide further direction for students to undertake this work, and regular contact hours and informal feedback throughout the courses provide opportunities for further guidance for learners.
Assessments for modules mostly take the form of practical coursework, lab tests and written exams, with all forms being well represented across all modules.
Career support and prospects
The University is committed to helping students develop and enhance employability and this is an integral part of many programmes. Specialist support is available throughout the course from Career and Employability Services including help to find part-time work while studying, placements, vacation work and graduate vacancies. Students are encouraged to access this support at an early stage and to use the extensive resources on the Careers website.
Discussing options with specialist advisers helps to clarify plans through exploring options and refining skills of job-hunting. In most of our programmes there is direct input by Career Development Advisers into the curriculum or through specially arranged workshops.
Big data is a major area for future growth and investment. As the global big data industry continues to grow year after year we continue to grow our programme, and continue to produce future leaders in an exciting and rewarding field.
We have a commitment to strong pastoral care for all of our students, which includes a Personal Tutor for all students, regular contact hours for tutor groups and our supportive student service teams who are always ready to help with any questions and provide the advice that you need.
In addition to standard study support through taught sessions, our Virtual Learning Environment allows students to access resources, participate in group work and submit work from anywhere in the world 24/7.
University central services are rich with support teams to assist students with every aspect of their journey through our degree programmes. From our Career and Employability Service, through our strong Students' Union, to our professional and efficient Student Finance team, there are always friendly faces ready to support you and provide you with the answers that you need.
There is much research taking place at the Faculty of Engineering and Informatics related to this Master's programme. This includes aspects of applied computing, theoretical computer science, and communications and networks. Statistical analysis of data across various disciplines is a central theme of our research.
Teaching informed by research is at the core of this programme. Graduates leave us well prepared to pursue academic research, or industry based research and development positions.
Our facilities are impressive, with several laboratories filled with dual-screen, dual boot (Windows and Linux) systems packed with industry-standard software.
Our specialised labs, such as the Ethical Hacking lab and the Internet of Things lab, allow students to build their skills within these key areas of growth, in a structured way through taught modules.
We provide a range of online facilities to support independent learning, including our Virtual Learning Environment which gives you access to learning materials and collaborative learning tools 24/7, anywhere in the world. We also provide virtual server technology using in house hardware to allow students to use our operating systems remotely.
Fees, Finance and Scholarships
- Home/EU: £3,980 per year
Tuition fees are subject to review for students starting their course in subsequent years. See our Fees and Financial Support website for more details.
You may be eligible to apply for the government's new Postgraduate Loan of up to £10,000 to put towards your fees and living costs. Find out more on our Fees and Financial Support website.
How do I find out more?
Steps to Postgraduate Study
Find out more about studying at a postgraduate level on the official, independent website Steps to Postgraduate Study (link opens in new window).
How to apply
The easiest way to apply is online.
- Apply for 2017/18 courses (September 2017 – July 2018 start dates)
- Apply for 2018/19 courses (September 2018 – July 2019 start dates)
This will help us process your application more quickly and allow you to submit your supporting documents electronically.
If you are unable to apply online, please email firstname.lastname@example.org to request a paper application form.
We will also need the following supporting documents, along with any other information specified on the course page:
- Degree certificates/transcripts
- Research proposal (if required)
- Two references (including one academic reference)
- Evidence of English language level (if required)
- A copy of your passport
Once you have applied you will have access to the University's Applicant Portal, where you can track the status of your application.
You should also start thinking about how you plan to fund your postgraduate study — you may need to apply for loans or grants at this stage.
Download the programme specification for Big Data Science and Technology
This is the current course information. Modules and course details may change, subject to the University's programme approval, monitoring and review procedures. The University reserves the right to alter or withdraw courses, services and facilities as described on our website without notice and to amend Ordinances, Regulations, fees and charges at any time. Students should enquire as to the up-to-date position when applying for their course of study.