The Master of Data Science (Social Analytics) shares a common core with the other Master of Data Science programmes. Social analytics will create a new cohort of social scientists with the necessary skills to apply new computational methods to inform policy and business decisions and examine social phenomena and gain insight about the interactions between people and their social environment. It will also equip social scientists to work with social media and other new sources of data. While generic data science skills are useful for social scientists, interpreting social data comes with particular challenges. This programme includes modules about specialised methods and also the theoretical foundations to understand how to use them effectively. Shared core modules with the suite of Data Science Master courses will ensure that you get equipped with the wider quantitative and computational skills required for your career. You will be carrying out team building activities, presenting case studies and carrying out both formative and summative assessments with students from all four faculties of Durham University, ensuring that you learn how to represent not just your own discipline but to also listen and integrate views and skills from other disciplines. An additional contribution to the academic environment will be provided by the Durham Research Methods Centre which will also help with the allocation of project topics through partnerships with local authorities, neighbouring NHS Trusts or other collaborators in the health and social care sectors. 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 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 well-known standard approaches to cutting-edge modern methods such as advanced causal inference techniques and deep learning. Optional modules allow students to focus on an area of interest.
Core modules: The Master of Data Science (Social Analytics) programme is comprised of the following core modules: Introduction to Computer Science (optional under certain circumstances) Introduction to Statistics for Data Science Introduction to Mathematics for Data Science (optional under certain circumstances) Programming for Data Science Social Science: Questions, Concepts, Theories, and Methods Research Project Examples of optional modules: Computational Social Science Machine Learning Multilevel Modelling Strategic Leadership Text Mining and Language Analytics
The Master of Data Science suite of programmes is research-oriented. Data Science is a driving force behind many subject specialisations today and aspects are delivered within the context of an active and varied research culture as is demonstrated via the associated academics and researchers within the Institute for Data Science. Students are also encouraged, through a range of modules, to develop research methods, skills and ethics reflecting the wide range of methods used by the research active staff. Research methodologies are actively taught through many other modules and assessments. They are also developed through innovative teaching practices such as simulations. Overall students are encouraged and guided to be ‘research minded’ in all modules, and to develop these critical skills for the future. All modules taught on this programme are underpinned by research, and embed elements of research training both in the delivery and in the assessment.
How to apply
If you are an international student who does not meet the requirements for direct entry to this degree, you may be eligible to take a pre-Masters pathway programme at the Durham University International Study Centre.
A UK first or upper second class honours degree or equivalent. Applicants with a first degree (or equivalent experience) involving significant content in Mathematics or Computer Science will be considered on a case-by-case basis.
Fees and funding
|Northern Ireland||£12900||Year 1|
|Channel Islands||£12900||Year 1|
|Republic of Ireland||£29500||Year 1|
Additional fee information
For further information see the course listing.
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