Master of Data Science
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 comprises a suite of programmes:
- Master of Data Science - programme code G5K823
- Master of Data Science (Bioinformatics and Biological Modelling) - programme code G5P223
- Master of Data Science (Digital Humanities) - programme code G5K923
- Master of Data Science (Earth and Environment) - programme code G5P123
- Master of Data Science (Health) - programme code G5P323
- Master of Data Science (Social Analytics) - programme code G5P423
These are conversion courses with a core of data science, intended to provide a 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 the social sciences and 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 problem-solving 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 Master of Data Science (Digital Humanities) is designed to provide Humanities graduates with advanced quantitative skills that will complement the qualitative analytical skills they have already acquired from their undergraduate degree. It caters for complete beginners and requires no prior knowledge of mathematics or programming. It shares a common core with the other Master of Data Science programmes, including an introduction to mathematics for Data Science, statistical modelling (in R), computer programming (in Python), machine learning, AI and neural networks.
In addition to the Data Science core, students also take a module in Digital Humanities which explores the application of quantitative and computational methods to cultural data: literary, philosophical and theological texts; historical data; languages; artifacts and material culture; visual art; video and music. Alternatively, you may take a traditional MA module in your area of interest (subject to departmental approval and timetabling). In your dissertation project, you will apply the techniques you have learned from your Data Science modules to a research problem of your choosing in a Humanities domain.
This course will be equally suitable for those who intend to employ advanced quantitative methods in their research in the Humanities, or for Arts and Humanities graduates who wish to learn computer programming and statistical modelling in order to enhance their employability in an increasingly competitive job market.
Aims of the Data Science Programmes
These programmes provide training in relevant areas of contemporary data science in a supportive research-led interdisciplinary learning environment. The broad aims are:
- To develop an advanced and systematic understanding of the complexity of data, including the its sources, alongside appropriate analysis techniques.
- To enable students to review critically 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 students' ability to communicate effectively both orally and in writing, using a range of media.
The suite of programmes are 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 each programme incorporates the necessary breadth and depth of material to ensure a skilled graduate.
The programmes allow 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.