Supported by SAMS, BAM Ebusiness & Egovernment SIG, Newcastle University Business School, Coventry University, Queen Mary University of London

New forms of data, data science and data analytics have reshaped social science research over the past years. Rapid growth has been spurred on by the proliferation of complex and rich data in science, industry and government, which are potentially available for research. As a result, (big) data analytics are no longer the preserve of engineering and computer science students, but as big datasets are increasingly becoming common in most domains, analytical skills have become essential to most fields of study and practice, and certainly to social science and Business School students. These trends have set new expectations about research questions, data collection and analysis methods as well as the general skills of doctoral students in social science research . Social science doctoral programmes have responded to the challenge of integrating new methods at the intersection of the computational and social sciences with new programmes and communities, such as the Social Data Science group at the Alan Turing Institute. Beyond these specialised programmes, the vast majority of doctoral courses have not caught up methodologically with advances in the area or are not able to harness the potential fully.

Doctoral students and early career researchers in management studies will need to acquire some familiarity with big data techniques alongside their traditional training over the course of their academic careers. If doctoral programmes do not include data science aspects (beyond the ones typically covered in quantitative methods) they risk limiting the scope of tackling objectives that are topical and relevant to theory and practice. For example, it limits the potential of projects to applying mixed methods approaches where qualitative insight can be combined with sources such as social media data or crowdsourcing . This is especially true when it comes to interdisciplinary projects that require novel ways of methodological synthesis. Introducing data science into doctoral programmes is not to say that such training would result in cross-functional, cross-discipline, all-knowing candidates who can tackle any research objective that can deliver valuable analytical insights. Instead, such curriculum and training interventions can complement the business/domain knowledge and the soft skills (especially communication) that graduates have with:

• framing complex data-driven research questions and data capturing skills
• data validation, manipulation and storage, using widely supported tools
• application of statistics, machine learning and predictive analytics
• data visualisation in appropriate forms

To contribute to the application of such tools and methods at the doctoral level in management studies, we seek to organise three workshops that will focus on each of the above four areas. The workshops will be organised in Newcastle, London and Coventry to ensure a wide geographical coverage and accessibility for the intended audience.

The events are free to attend but please note that there is a limited number of spaces available at each location.