MDS Master Of Data Science (Bioinformatics And Biological Modelling)

Gain quantitative skills in bioinformatics and modelling in molecular biology.

The Master of Data Science (Bioinformatics and Biological Modelling) will provide graduates with quantitative skills in bioinformatics and modelling in molecular biology. It shares a common core with the other Master of Data Science programmes.

In addition to the Data Science core, students also take a core module named “Bioinformatics”. There is also an optional Module available, which is named “Modelling in Molecular Biology”. The Bioinformatics module explores quantitative tools and methods used to work with biological data, and Modelling in Molecular Biology module explores quantitative modelling tools and methods for exploring metabolic and signalling networks in molecular biology. In your dissertation project, you will apply the techniques you have learned from your Data Science modules to a research problem. This course will be equally suitable for those who intend to employ quantitative analysis in their research in molecular biology, or for physical or biological graduates who wish to learn transferrable data and modelling analysis skills in order to enhance their employability in an increasingly competitive job market.

All around us, massive amounts of increasingly complex data are being generated and collected, for instance, from mobile devices, cameras, cars, houses, offices, cities, and satellites. Business, research, government, communities, and families can use that data to make informed and rational decisions that lead to better outcomes. It is impossible for any one individual or group of individuals to keep on top of all the relevant data: there is simply far too much. Data science enables us to analyse large amounts of data effectively and efficiently and as a result has become one of the fastest growing career areas.

Previously, data science was the province of experts in maths and computer science, but the advent of new techniques and increases in computing power mean that it is now viable for non-experts to learn how to access, clean, analyse, and visualize complex data. There is thus a growing opportunity for those already in possession of knowledge about a particular subject or discipline, and who are therefore able to grasp the full meaning and significance of data in their area, to be able to undertake data analysis intelligently themselves. The combination of primary domain knowledge with an expertise in extracting relevant information from data will give those with this ‘double-threat’ a significant employment advantage.

The Master of Data Science suite of programmes is a conversion course with a hard-core of data science, intended to provide Masters-level education rich in the substance of data science for students who hold a first degree that is not highly quantitative, including those in social sciences, the arts and humanities. Introductory modules are designed to bring students with non-technical degrees up to speed with the background necessary for data science. This is done on a need-to-know basis, focusing on understanding in practice rather than abstract theory. Core modules then introduce students to the full range of data science methods, building from elementary techniques to advanced modern methods such as neural networks and deep learning. Optional modules allow students to focus on an area of interest.

The programme provides training in relevant areas of contemporary data science in a supportive research-led interdisciplinary learning environment. The broad aims are:

• To develop advanced and systematic understanding of the complexity of data, including the sources of data relevant to science, alongside appropriate analysis techniques
• To enable students to critically review and apply relevant data science knowledge to practical situations
• To develop a critical awareness of current issues in data science which is informed by leading edge research and practice in the field
• To develop a conceptual understanding of existing research and scholarship to enable the identification of new or revised approaches to data science practice
• To develop creativity in the application of knowledge, together with a practical understanding of how established, advanced techniques of research and enquiry are used to develop and interpret knowledge in data science.
• To develop the ability to conduct research into data science issues that requires familiarity with a range of data, research sources and appropriate methodologies and ethical issues.
• To develop advanced conceptual abilities and analytical skills in order to evaluate the rigour and validity of published research and assess its relevance to new situations
• To extend the ability to communicate effectively both orally and in writing, using a range of media.

The programme is designed around a pedagogical framework which reflects the core categories of the data science discipline.

A number of subjects can be identified and defined within each application domain. Whilst a Masters programme cannot incorporate all subjects, a selection of subjects representative of each domain ensures that the programme incorporates the necessary breadth and depth of material to ensure a skilled graduate.

The programme allows for progressive deepening in the students’ knowledge and understanding, culminating in the research project which is an in-depth investigation of a specific topic or issue.

The global dimension is reinforced through the use of international examples and case studies where appropriate.

Course Structure

Core modules

The Master of Data Science (Bioinformatics and Biological Modelling) programme is comprised of the following core modules:

• Introduction to Computer Science
• Introduction to Statistics for Data Science
• Ethics and Bias in Data Analytics
• Machine Learning
• Programming for Data Science
• Introduction to Mathematics for Data Science
• Bioinformatics
• Research Project (60 credits)

Examples of optional modules:

• Modelling in Molecular Biology
• Text Mining and Language Analytics
• Data Exploration, Visualization, and Unsupervised Learning
• Strategic Leadership