Programme of study for the integrated degree of PhD and MSc Data Analytics and Society 2024/25

This programme is no longer recruiting.

Applicants will normally be required to have obtained a relevant degree at least equivalent to a UK upper second class (2:1) honours degree.

Postgraduate researchers (PGRs) whose first language is not English will normally be required to have achieved at least 7.0 on IELTS (with 7 in writing and no other component below 6), or TOEFL 100 overall, 25 writing, 22 other sections, or an equivalent English language qualification.

Year 1

A total of 120 credits in taught modular courses will be taken as well as the research project. Please see the programme catalogue for details of when each module will run in each session.

The candidate will commence research under the direction of their supervisor(s).

Compulsory specialised subject modules (105 credits):

• GEOG5000M Internship Project (University of Leeds) 15 credits
• GEOG5995M Programming for Social Sciences: Core Skills (University of Leeds) 15 credits
• JULV5000M Analysis of Human Dynamics (University of Liverpool) 15 credits
• JUMN5000M Understanding Data and its Environment (University of Manchester) 15 credits
• JUSH5000M Social Analytics and Visualisation (University of Sheffield) 15 credits
• SLSP5501M Research Strategy and Design (University of Leeds) 30 credits

Candidates must also take one 15-credit subject optional module from a prescribed list for year 1. Each module in the prescribed list is 15 credits. PGRs therefore choose just one module.

Candidates are required to pass at least 90 taught credits in order to progress on the programme.

Year 2

A total of 60 credits in taught modular courses will be taken as well as the research project. Please see the programme catalogue for details of when each module will run in each session.

The candidate will continue research under the direction of their supervisor(s).

Compulsory specialised subject modules (30 credits):

• GEOG5099M Dissertation (University of Leeds) 30 credits in semester 2

Optional subject modules (30 credits):

• Candidates will take two 15 credit subject Optional modules from a prescribed list for year 2. Each module in this list is 15 credits. PGRs should therefore choose two modules.

Candidates who have been successful in the assessed modules and research components during year 2 of study will undergo a transfer assessment process, to be successfully completed by no later than the end of year 2 (month 24) of study.

The decision to transfer will be based on the submission of appropriate material for assessment and all PGRs must undergo an oral examination by an assessment panel which must include at least one independent individual who has not been involved in the supervisory support of the candidate. The PGR will be interviewed by the assessment panel in the form of a viva voce examination.

Years 3 and 4

The candidate will continue research under the direction of their supervisor(s).

• Identify, summarise and critically compare key theories, concepts and empirical research evidence within the fields of data analytics and social science
• Apply high-level programming and analytical tools and techniques to analyse data describing social and cultural environments, and set appropriate objectives and strategies in complex situations
• Develop transferable professional skills in working with others required for using social science data to form and implement analysis strategies, such as thought leadership
• Demonstrate advanced independent research skills and analytical abilities in the fields of data analytics and social science, along with the ability to develop research ideas and questions and to undertake analysis of data and written presentation of results for practice
• Develop and deliver professional quality oral presentations that distil insights from analyses of data from social and cultural perspectives to describe, interpret, and explain the social world
• Appraise the relative strengths and weaknesses of different quantitative methodologies in analysing different types of social and cultural data, and apply knowledge in managing the implications of applied research
• Develop a good understanding of contemporary topics in social science and data analytics and how the global landscape shapes the analysis of large datasets, and non-standard forms of data structure, such as those where it is the links between observations, rather than the observations themselves
• Take a proactive and self-reflective role in working and to develop professional relationships with others
• Apply ethical codes to the practice of data analytics in the context of social and cultural sciences.

On completion of the programme students should have shown evidence of being able to:

• Demonstrate in-depth, specialist knowledge and mastery of techniques relevant to the disciplines of data analytics and social science and/or to demonstrate a sophisticated understanding of concepts, information and techniques at the forefront of the disciplines
• Identify, summarise and critically compare key theories, concepts and empirical research evidence within the fields of data analytics and social science
• Apply high-level programming and analytical tools and techniques to analyse data describing social and cultural environments, and set appropriate objectives and strategies in complex situations
• Develop transferable professional skills in working with others required for using social science data to form and implement analysis strategies, such as thought leadership
• Demonstrate advanced independent research skills and analytical abilities in the fields of data analytics and social science, along with the ability to develop research ideas and questions and to undertake analysis of data and written presentation of results for practice
• Develop and deliver professional quality oral presentations that distil insights from analyses of data from social and cultural perspectives to describe, interpret, and explain the social world
• Appraise the relative strengths and weaknesses of different quantitative methodologies in analysing different types of social and cultural data, and apply knowledge in managing the implications of applied research
• Develop a good understanding of contemporary topics in social science and data analytics and how the global landscape shapes the analysis of large datasets, and non-standard forms of data structure, such as those where it is the links between observations, rather than the observations themselves
• Take a proactive and self-reflective role in working and to develop professional relationships with others
• Apply ethical codes to the practice of data analytics in the context of social and cultural sciences. 

PGRs will have had the opportunity to acquire the following abilities as defined in the modules specified for the programme:

• the skills necessary to undertake a higher research degree and/or for employment in a higher capacity in industry or area of professional practice
• evaluating their own achievement and that of others
• self direction and effective decision making in complex and unpredictable situations
• independent learning and the ability to work in a way which ensures continuing professional development
• critically to engage in the development of professional/disciplinary boundaries and norms. 

For Masters (taught) students the learning context will include the analysis of, and decision making in, complex and unpredictable situations. The structure of the programme will provide breadth and/or depth of study and opportunities for drawing upon appropriate resources and techniques. Opportunities will be provided for students to develop:

• high level interests and informed opinions
• develop to a high level their design and management of their learning activities
• develop to a high level their communication of their conclusions
• make an original contribution to the field.

Students will be expected to engage in the exercise of autonomous initiative in their study and work in professional environments.

Achievement for the degree of Master (taught programme) will be assessed by a variety of methods in accordance with the learning outcomes of the modules specified for the year/programme and will involve the achievement of the students in:

• evidencing an ability to conduct independent in-depth enquiry within the discipline
• demonstrating the ability to apply breadth and/or depth of knowledge to a complex specialist area
• drawing on a range of perspectives on an area of study
• evaluating and criticising received opinion
• making reasoned judgements whilst understanding the limitations on judgements made in the absence of complete data.