Master of Data Science (Social Analytics)

Entry requirements

A UK first or upper second class honours degree or equivalent in ANY degree that doesn’t include a strong data science component including those in social sciences, the arts and humanities, business, and sciences. Candidates with a degree in Social and Behavioural Sciences are strongly encouraged to apply.

Months of entry

September

Course content

From security cameras and satellite transmission to our mobile devices, homes, cars, and in the workplace, we are surrounded by data. 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 social environment. With companies and organisations of all types harnessing this technology to advance knowledge and aid policy and business decisions, there has been a significant increase in demand for skilled data scientists.

Drawing on this, we have created the Master of Data Science (Social Analytics), 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 is in a non-quantitative subject such as the social sciences, the arts or humanities.

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 integrate data science with social science, equipping you with the skills to design and carry out social data science research and communicate it to optimise impact across a variety of settings.

The course begins with a range of introductory modules before progressing to more advanced contemporary techniques such as statistical modelling (in R), computer programming (in Python), machine learning, AI and neural networks. Social analytics modules provide insight into the specialised methods needed for social data as well as the theoretical foundations to understand how to use them effectively.

The MDS culminates in the research project, an in-depth investigation into an area of specific interest in which you apply the skills learned during the course to a research problem in a social science domain of your choice. The Durham Research Methods Centre can help with the allocation of project topics through local authorities, NHS Trusts and the wider health and social care sector.

Course Structure

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. The project can be practical, theoretical or both, and is designed to develop your research, analysis and report-writing skills.

Critical Perspectives in Data Science develops your understanding of the production, analysis and use of quantified data, and how to analyse these practices anthropologically. You will learn to think ethically and contextually about quantified data, and how to apply these tools to practical problems in data science, including your own research project.

Social Science: Questions, Concepts, Theories and Methods illustrates the key differences between the field of social science and other disciplines. It facilitates understanding of different types of data; uses practical examples from the social sciences to teach research design and measurement methods; and introduces state of the art applications of computational methods in social science.

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.

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.

The remainder of the course will be made up of core and option modules which will vary depending on prior qualifications and experience. These have previously included:

• Introduction to Computer Science
• Introduction to Mathematics for Data Science
• Text Mining and Language Analytics
• Data Exploration, Visualisation and Unsupervised Learning
• Ethics and Bias in Data Analytics
• Strategic Leadership
• Machine Learning
• Computational Social Science

Fees and funding