MSc Big Data Biology
Our innovative MSc will enable you to use cutting-edge, big data platforms to tackle issues from developmental biology to disease surveillance and ecology.
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.
Our innovative MSc will enable you to use cutting-edge, big data platforms to tackle issues from developmental biology to disease surveillance and ecology.
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.
