Master of Data Science (Bioinformatics and Biological Modelling) MSc

From personalised medicine, to smart cities and sustainable solutions, data science is building a better world. At the same time, developments in technology have made the field of data science more accessible than ever, creating new opportunities to gain insight into the interactions between people and their environment. This has led to a significant increase in demand for skilled data scientists, and this demand is predicted to further grow. <br/><br/>Drawing on this, we have created the Master of Data Science (Bioinformatics and Biological Modelling), a conversion course that equips you with the skills to access, clean, analyse, and visualise data, opening a future in data science even if your first degree doesn’t include a strong data component. It is likely to appeal to those with a background in biological or physical sciences.<br/><br/>The MDS provides training in contemporary data science, learning from practicing researchers who are making a difference across a range of industries. Shared core modules across the suite of MDS courses build wider skills in statistical and machine learning, while subject-specific modules will develop your quantitative skills in bioinformatics and biological modelling. It is equally suitable whether you are planning to use quantitative analysis in a research capacity in molecular biology, or if you are a physical or biological science graduate who wants to learn transferrable data and modelling analysis skills.<br/><br/>The course begins with a range of introductory modules before progressing to more advanced contemporary techniques in machine learning to expand your knowledge and understanding. We offer an extensive range of optional modules which allows you to focus on an area of interest such as text analytics and data visualization. Optional modules allow you to focus on an area of interest.<br/><br/>The MDS culminates in the research project, an in-depth investigation in which you apply the skills learned during the course to a research problem working alongside an expert in the area of application of your choice. <br/><br/>**Core modules:**<br/><br/>The **Data Science Research Project** is a substantial piece of research into an unfamiliar area of data science, or in your subject specialisation area with a focus on data science. <br/><br/>**Critical Perspectives in Data Science** develops your understanding of the production, analysis and use of quantified data, and how to analyse these practices anthropologically. <br/><br/>**Bioinformatics** provides you with a broad understanding of the field of bioinformatics as well as the R environment for data analysis and visualisation in bioinformatics. You will also learn to analyse genomic and transcriptomic data, DNA and protein sequence data, and develop the skills to use public bioinformatics databases.<br/><br/>**Programming for Data Science** uses the popular Python software packages used in a wide range of industry settings. You will learn how to gather, manipulate and process real-world data and learn the key concepts of data analysis and data visualisation.<br/><br/>**Ethics and Bias in Data Analytics** introduces contemporary debates on ethical issues and bias resulting from the application of data analytics, statistical modelling and artificial intelligence in society. You will learn about contemporary philosophical research on these issues and how to apply this research <br/><br/>**Machine Learning** introduces the essential knowledge and skills required in machine learning for data science using the R statistical language. You will develop an understanding of the theory, computation and application of topics such as modern regression methods, decision-based machine-learning techniques, support vector machines, and neural networks.<br/><br/>**Introduction to Statistics for Data Science** focuses on the fundamentals of statistics you will need for data science. The module covers topics such as exploratory statistics, statistical inference; linear models; classification and clustering methods; and resampling and validation.