Big Data Biology (MSc)
- Duration: 1 year
- Mode: Full time
Why study this course
Our innovative MSc will enable you to use cutting-edge, big data platforms to tackle issues from developmental biology to disease surveillance and ecology.
A unique, holistic approach
Learn how to combine big data with modelling to derive mechanistic understanding of biological processes.
Apply your skills and knowledge to real-life scenarios, specifically designed to align with problem-solving in the workplace.
Join an exciting and growing network of students and academics working in predictive biology.
Ranked 4th in the UK and 27th in the world
Our School is ranked highly for Biological Sciences by the 2021 Shanghai Ranking Global Ranking of Academic Subjects.
From microbiomes, phenomes and genomes to whole ecosystems, modern biology generates a vast amount of data. The scale and nature of this information requires a new generation of scientists with the skills to harvest, analyse, manipulate and interpret big data, and to link this analysis to underlying mechanisms through mathematical and computational modelling.
As a student on our innovative MSc Big Data Biology, you will have the opportunity to explore the cutting-edge platforms used in modern biological analysis. You will learn about the statistical and computational approaches required to analyse the big data generated, and mine the growing repositories of ‘omics data that now exist within biology.
A core distinguishing feature of our programme is that it will enable you to interface your big data analysis to dynamical systems and network theory, rather than relying solely on performing pattern analysis and/or AI-methods to data. This unique big data modelling approach will allow you to test hypotheses and analyse models to uncover biological mechanisms from high-throughput data sets. You will explore how big data can be better interpreted and mined using biological understanding and conversely, how it can generate novel biological insights.
The course is designed to equip you with the skills to work on “real-life” data problems. You will become proficient at using core tools and approaches for big data analysis and build confidence in critically selecting and applying these approaches to address a wide scope of biological questions. After completing the first core modules, you will apply the skills gained to solve a big data scenario for a “client” – a real-life problem faced by a research group within a university, industry or government organisation.
The structure of the course provides a broad overview of systems biology, as well as enabling you to specialise in an area of your interest through an extensive range of research opportunities. You will complete the programme with a solid grounding in both systems biology and bioinformatics. Furthermore, you will be able to improve your transferable skills, such as working in interdisciplinary teams, learning how to master new software in a structured manner, writing reports or grants writing, and delivering science presentations – all important skills for an early career researcher.
Big Data Biology is a progressive and exciting area of science. Advancing our understanding of living systems depends on unlocking the potential of big data, from molecules to the biosphere. This programme has been specifically designed to ensure that you are fully prepared for a career in industry or academic research, and that your skills closely reflect those currently sought by employers. Crucially, you will also learn how to keep pace with future developments in the field of big data biology by learning how to effectively absorb and employ new strategies and technology.
Where you'll study
School of Biosciences
We provide a dynamic and stimulating teaching environment with impressive modern facilities, up-to-date equipment and high-calibre staff.
Selection or interview process:
Applicants should normally possess a higher education degree with first or 2(1) second-class Honours (UK), or an equivalent academic qualification.
This programme is suitable for graduates in Biosciences or Environmental Sciences with an interest in bioinformatics, statistics, molecular biology or biomedicine. Also, graduates in computational sciences, mathematics, physics and engineering are strongly encouraged to apply, with the proviso that they have a genuine interest in biological questions (which can be highlighted in your statement of interest during the application process).
IELTS with an overall score of 6.5 with 5.5 in all subskills, or evidence of an accepted equivalent.
Find out more about English language requirements.
Applicants who require a Student visa to study in the UK must present an acceptable English language qualification in order to meet UKVI (UK Visas and Immigration) requirements.
You are not required to complete a DBS (Disclosure Barring Service) check or provide a Certificate of Good Conduct to study this course.
If you are currently subject to any licence condition or monitoring restriction that could affect your ability to successfully complete your studies, you will be required to disclose your criminal record. Conditions include, but are not limited to:
- access to computers or devices that can store images
- use of internet and communication tools/devices
- freedom of movement
- contact with people related to Cardiff University.
The course runs for 12 months full time. Students undertake modules to the value of 180 credits.
The programme consists of six core modules (120 credits) and a research project (60 credits). It comprises two stages:
- Stage 1: taught modules. There is an exit point at the end of Stage 1 (120 credits), leading to a Postgraduate Diploma. In addition, there is a possible exit award after completion of 60 credits leading to a Postgraduate Certificate.
- Stage 2: Research Project
Core modules (6); each 20 credits (Stage 1)
Core Modules (Stage 2) - Research Project 60 credits (dissertation)
All MSc students undertake an independent research project, which culminates in a report of approximately 8,000-10,000 words. You will be able to apply the experience you have gained in report writing throughout the Case Study module to this project. In addition, you will present your research project to the group, after which you will receive feedback that you can incorporate into your final report.
The modules shown are an example of the typical curriculum and will be reviewed prior to the 2023/24 academic year. The final modules will be published by September 2023.
Current technology, from genome sequencers to image acquisition (cellular or anatomical), fuel the need for a workforce that understands the statistical and computational approaches and cyber-infrastructure required. Such expertise will expand the frontiers of fundamental research whilst also being critical to advancements in applied research. Disease surveillance in humans, livestock and wildlife is driven by whole genome sequencing, whilst the integration of large morphological and genetic data to reveal disease association or agriculturally important plant phenotypes, both demand proficiency in big data analytics. Furthermore, linking the dynamics of disease, or linking phenotype back to the genetic level, requires a multi-level modelling approach working in synergy with the data-analysis. This modelling-big data alliance is fostered in Stage 1 of the course and constitutes one of its distinguishing features, setting it apart from standard big data approaches.
A working knowledge of biostatistics, biocomputing and modelling is essential for all life science careers, and together with our innovative ‘Case Study’ module (in which you will apply your proficiency to a “real-life” big data problem), provides the core foundation of this programme. The breadth and depth of the modules offer transferable and specialist skills, enabling you to apply the learning gained during your research towards further developing your own interests and career aspirations.
Core Modules (Stage 2) - Research Project (dissertation)
All students undertake an independent research project which culminates in a report of approximately 8,000-10,000 words. During this period, you will be embedded in an active research group, gaining an authentic research experience alongside leading academics, post-doctoral researchers and PhD students. You will be supervised by a member of academic staff with experience in your project area.
The University is committed to providing a wide range of module options where possible, but please be aware that whilst every effort is made to offer choice this may be limited in certain circumstances. This is due to the fact that some modules have limited numbers of places available, which are allocated on a first-come, first-served basis, while others have minimum student numbers required before they will run, to ensure that an appropriate quality of education can be delivered; some modules require students to have already taken particular subjects, and others are core or required on the programme you are taking. Modules may also be limited due to timetable clashes, and although the University works to minimise disruption to choice, we advise you to seek advice from the School on the module choices available.
|Module title||Module code||Credits|
|Biocomputing and Big Data Handling||BIT101||20 credits|
|Case Study||BIT103||20 credits|
|Systems and Predictive Biology||BIT105||20 credits|
|Big Data Science||BIT107||20 credits|
|Research Project||BIT104||60 credits|
The University is committed to providing a wide range of module options where possible, but please be aware that whilst every effort is made to offer choice this may be limited in certain circumstances. This is due to the fact that some modules have limited numbers of places available, which are allocated on a first-come, first-served basis, while others have minimum student numbers required before they will run, to ensure that an appropriate quality of education can be delivered; some modules require students to have already taken particular subjects, and others are core or required on the programme you are taking. Modules may also be limited due to timetable clashes, and although the University works to minimise disruption to choice, we advise you to seek advice from the relevant School on the module choices available.
Learning and assessment
How will I be taught?
Teaching strategies are custom-designed to meet the specific challenges of each module. You will be taught using a combination of lectures, small-group seminars and hands-on computer workshops.
Many of the modules will use flipped-learning strategies, to maximize hands-on learning, and support individual strengths and weaknesses. In flipped-learning, lectures are pre-recorded and made available to you to absorb the information before our face-to-face contact time. During our live sessions, we will work together to better understand the concepts, and to apply and actively explore them through a series of activities. You will be encouraged to engage in open discussion and take the lead in organising scientific dialogue in different formats. Due to the interdisciplinary nature of the programme, these activities stimulate team building and group problem solving using the specific strengths of each group member.
Programming skills and the use of relevant software packages will be taught (or developed) in our dedicated computer suites. To support students joining us without the required level of computational knowledge, we will provide background material regarding operating system usage and basic knowledge of the programming package R in advance of the course. The teaching staff are prepared to help you gain any IT skills needed to engage with the syllabus effectively - we will actively encourage student-led enquires and run additional computational support sessions when necessary. An important element of the course is that continuous technical learning takes place while exploring fundamental biological and dynamical systems’ concepts.
You will be encouraged to attend our School seminar series with the opportunity to interact directly with the speakers and, on some occasions, to help propose which scientists to invite. Dissertation projects are designed for you to apply your knowledge to an open-ended research project. Both the choice of the project topic for the ‘Case Study’ module and the dissertation topic will be supported by “speed-dating” sessions, in which potential supervisors will briefly describe the data set and type of questions they would like to see addressed. Please see the Placement Opportunities for further details.
How will I be assessed?
You will be assessed through a combination of assessment of practical skills, coursework, reports, presentations and a research project (= dissertation; 8,000–10,000 words). Assessments are designed to reinforce and stimulate the learning process in a bespoke and varied manner. They may take the form of coursework (designing bioinformatic flows, statistical analysis of data sets, model exploration for a given biological problem, critical review of a scientific paper, podcasts, code repository etc), poster presentations, portfolios and on-line assessments.
All assessments may be submitted through the medium of Welsh as determined by the Higher Education Welsh Language Standards. The School of Biosciences has an established record of marking Welsh-language assessments internally, or if necessary, via qualified translation services. This process is supervised by the School Welsh Language Liaison Officer.
How will I be supported?
You will be provided with a Personal Tutor on your arrival at Cardiff University, and you can request a Welsh-speaking personal tutor if required. Several academic staff in the School of Biosciences are first-language Welsh speakers and the provision of Welsh-speaking Personal Tutors is well-established in our School. You are encouraged to contact your Personal Tutor should you have any academic or pastoral issues that you wish to discuss. You will meet your Personal Tutor within the first two weeks of the course and at regular intervals through the year. Your Personal Tutor will also help ensure that any research experience with external organisations follows the same high academic standards as those within the School.
Part of the strength of this programme lies in students with different backgrounds learning side-by-side and working together. We anticipate that students will join us with vastly different levels of prior computational experience, and we are prepared to support your learning and acquisition of technical skills, whatever your level. Before you arrive, we will provide reading material and pre-recorded lectures of the relevant, computational background aspects. During the course, you will receive ample support for formative assessments to prepare you to work independently on the summative assessments, as well as organising additional computational tutorials as and when needed. The hands-on nature of our many workshops are great opportunities for you to gain computational skills, test them and ask instructors for additional guidance.
Big Data Network
You will be encouraged to participate in an exciting and growing network of students and academics working in predictive biology using big data as an integral part of their search for biological insights, both within the School and in other disciplines. This network will “meet” in virtual spaces, dedicated seminars, and social events, and interact with computational biologists, mathematical modellers and biomedical researchers all asking similar questions and using similar resources. MSc Big Data Biology students will be able to attend a Career Research Day to explore employment opportunities as a new generation scientist, discussing routes to industry as well as academia, such as appropriate PhD programmes. As a student, you are also encouraged to help organise and decide on speakers, to build up transferable skills and scientific independence.
Peer Support for Post Graduate Students
In addition to the personal tutoring system, Cardiff University offers peer-to-peer support specifically for postgraduate students, recognising the specific needs of students studying at this level. Postgraduate Peer Supporters volunteer to support other postgraduate students’ wellbeing by facilitating monthly Postgraduate Peer Support Groups. The Postgraduate Peer Support Groups run all year round (including over the summer). Module leads will also promote peer-supported learning activities for their specific modules, which will be dependent on the needs and levels of engagement of the students.
What are the learning outcomes of this course/programme?
The Learning Outcomes for this Programme describe what you will be able to do as a result of your study at Cardiff University. They will help you to understand what is expected of you.
The Learning Outcomes for this Programme can be found below:
Knowledge & Understanding:
On successful completion of the Programme you will be able to:
- Perform a rigorous hypothesis-driven approach to biological big data and recommend appropriate experimental design for future data collection.
- Translate complex and entangled biological systems into tractable mathematical and computational models.
- Relate ‘omics’ approaches to mechanistic models and derived understanding.
Evaluate biological problems at different scales – from molecular and cellular, to ecosystems.
On successful completion of the Programme you will be able to:
- Compare and contrast different hypotheses and models analytically.
- Apply independent learning strategies to emerging theories, technologies and software.
- Use innovative approaches to analytic problem solving.
- Critically appraise and modify different emerging modelling formalisms to biological challenges.
Professional Practical Skills:
On successful completion of the Programme you will be able to:
- Employ advanced numeracy and computer programming to a wide spectrum of data-driven problems.
- Communicate effectively and confidently through scientific writing and presentation.
- Plan, design, and optimise scripting solutions.
- Critically evaluate informatic workflow described in reports and papers.
Judge the validity of biological conclusions drawn from large data sets.
On successful completion of the Programme you will be able to:
- Lead and support “open biology” best practices.
- Effectively work in interdisciplinary teams.
- Design and coordinate pipelines to facilitate high volume analyses.
- Select and evaluate open-source software appropriate for specific data processing.
- Use presentation skills to communicate effectively with diverse audiences using different media.
- Apply data visualisation methods to data presentation and communication strategies.
- Appraise and combine key concepts in bioinformatics into state-of-the-art algorithms.
Tuition fees for 2023 entry
Your tuition fees and how you pay them will depend on your fee status. Your fee status could be home, island or overseas.
Learn how we decide your fee status
Fees for home status
Students from the EU, EEA and Switzerland
If you are an EU, EEA or Swiss national, your tuition fees for 2023/24 be in line with the overseas fees for international students, unless you qualify for home fee status. UKCISA have provided information about Brexit and tuition fees.
Fees for island status
Learn more about the postgraduate fees for students from the Channel Islands or the Isle of Man.
Fees for overseas status
More information about tuition fees and deposits, including for part-time and continuing students.
Financial support may be available to individuals who meet certain criteria. For more information visit our funding section. Please note that these sources of financial support are limited and therefore not everyone who meets the criteria are guaranteed to receive the support.
No additional charges are made for other aspects of tuition, although some services (such as student printing on demand) may incur a charge.
Will I need any specific equipment to study this course/programme?
No specific equipment is required, although it is highly recommended that you have a personal laptop or equivalent. We are able to advise on minimal specifications if required. The University provides IT facilities (in a communal space), laboratories equipped with specialist equipment including computational resources, and all specialist software required for the course. We also provide specialist bioinformatics and numerical modelling packages.
During the MSc, we teach and use the programming language R. We use RStudio in many modules to assess and analyse big data, as well as to study mechanistic models that help to capture and unravel the observed biological dynamics.
The School of Biosciences has a 160-seater e-Learning e-Assessment Facility. Students will have access on-site, and off-site via VPN, to High Performance Computing facilities.
We’re based in one of the UK’s most affordable cities. Find out more about living costs in Cardiff.
Careers and placements
Our MSc offers excellent, broad-spectrum training for the future systems biologist. As well as becoming technically adept, you will be able to consider biological implications and hypotheses, derive predictions, and efficiently engage in modelling-data-experiment cycles. The programme will therefore prepare you for the future of predictive biology in different contexts from developmental biology to disease and ecology, as well as equipping you with the skills to excel in any industrial-based research working with big data. Its blend of theory and practical research skills, solid fundaments of dynamical systems and theoretical biology will arm you with the scientific knowledge, hands-on experience and adaptability that are highly valued by employers in today’s global job market.
In particular, we expect many of our graduates to enjoy successful careers in interdisciplinary research, in both academia and the private sector. With its focus on practical training in subject-specific and generic research skills, our programme provides the ideal platform for further study and a career in academia. During the course, many of the modules’ coursework will also emphasise how to translate science for a variety of audiences, opening other possible exciting avenues, such as scientific editing and public engagement.
Alongside the focus on biological systems, you will develop transferable skills in high demand in applied sciences, and vital in a range of roles across the public, private and third sectors. These skills include data management, curation, analysis and literacy; computational modelling; data visualisation methods; making complex research accessible to a wide audience; scripting and documenting complex computational pipelines; managing and interpreting visual data; and generating and testing hypotheses using both simple mathematical models and large data sets.
You will be part of an active research environment where novel ideas and methods can be tested and explored with the aim of deriving new biological insights. You will have access to world-leading academics with a range of expertise, who are committed to helping guide you through any interdisciplinary challenge. This environment will prepare you for your post-university career, and you will be part of a growing network that will maximise future opportunities in whichever direction you wish to pursue.
This course does not include any formal work placements. There are, however, two opportunities where you can choose to work with an external organisation.
Our unique ‘Case Study’ module allows you to apply your skills to a “client project” – either as a real-life scenario faced by a research group within a university, industry or government organisation, or directly with an external organisation. Students working with external organisations as part of taught modules will be supported by a University mentor to ensure optimal communication and research progress.
You can also choose to undertake your final research project with an external organisation, subject to approval from the Course Director. The Course Director will also ensure that the project objectives of the module are met and that appropriate supervision is given, usually through co-supervision by a University mentor.
HESA Data: Copyright Higher Education Statistics Agency Limited 2021. The Higher Education Statistics Agency Limited cannot accept responsibility for any inferences or conclusions derived by third parties from its data. Data is from the latest Graduate Outcomes Survey 2019/20, published by HESA in June 2022.