Data Science - Training and Capacity Building and the Industrial Strategy: An Applied Quantitative Methods Network (AQMeN III) Project
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Social and Political Science
Abstract
This is a proposal for a novel set of inter-related training and capacity building activities bringing together industry and social science researchers and teachers. The activities draw on the knowledge, skills and insights from advanced quantitative social science methods and data analysis. It is an innovative attempt to bring together stakeholders in industry with social scientists engaged in advanced quantitative methods, data analysis, data science, and big data.
There will be two phases to the work.
Phase I will comprise two work packages. Work Package 1 will map the current terrain of knowledge and skills training in advanced quantitative methods and social science data analysis. Work Package 2 will identify industrial partners and scope out their knowledge and skills requirements in big social science data analysis.
Phase II will comprise one work package. In Work Package 3 we propose to design and deliver a short series of training activities. The central aim will be to undertake activities that directly target the identified gaps between existing social science training and the needs of partners in industry that would benefit from developing workforces with big social science data analytical skills.
There will be two phases to the work.
Phase I will comprise two work packages. Work Package 1 will map the current terrain of knowledge and skills training in advanced quantitative methods and social science data analysis. Work Package 2 will identify industrial partners and scope out their knowledge and skills requirements in big social science data analysis.
Phase II will comprise one work package. In Work Package 3 we propose to design and deliver a short series of training activities. The central aim will be to undertake activities that directly target the identified gaps between existing social science training and the needs of partners in industry that would benefit from developing workforces with big social science data analytical skills.
Planned Impact
This is a proposal for an innovative set of activities bringing together industry and social science researchers and teachers. The activities draw on the knowledge, skills and insights from advanced quantitative social science methods and data analysis.
We are confident that it will be impactful because it is an innovative attempt to bring together stakeholders in industry with social scientists engaged in advanced quantitative methods, data analysis, data science, and big data. The project will encourage reflection and lead to conceptual advancements in this emerging area.
The proposed programme of activities will have economic and societal impact and benefit organisations outside of the academy in a business and industry. The proposed programme of work will have instrumental impact because it will influence and shape policy, practices and knowledge exchange relating to the Industrial Strategy. The activities proposed in Work Package 3 will achieve impact in capacity building through the delivery of technical skills development. The proposed project is short term activity and the impact ambitions are therefore scaled accordingly.
We are confident that it will be impactful because it is an innovative attempt to bring together stakeholders in industry with social scientists engaged in advanced quantitative methods, data analysis, data science, and big data. The project will encourage reflection and lead to conceptual advancements in this emerging area.
The proposed programme of activities will have economic and societal impact and benefit organisations outside of the academy in a business and industry. The proposed programme of work will have instrumental impact because it will influence and shape policy, practices and knowledge exchange relating to the Industrial Strategy. The activities proposed in Work Package 3 will achieve impact in capacity building through the delivery of technical skills development. The proposed project is short term activity and the impact ambitions are therefore scaled accordingly.
| Description | The Industrial Strategy recognises that a major challenge facing UK businesses and industry is how best to utilise big data to improve economic performance and increase productivity. An emerging area of research and development is the use of big social science data. A substantial barrier to exploiting the potential offered by emerging forms of big data is the lack of a suitably trained workforce with appropriate analytical skills. The deepest skills deficit is in the understanding and application of quantitative (i.e. statistical) data analysis techniques. There are also striking skills shortfalls in the ability to organise and enable data for analysis, the ability to engage in data analytics and predictive modelling, and the ability to communicate data and produce effective data visualisations. |
| Exploitation Route | This was a short-life project, however the resources that were developed are archived and freely accessible via three git repositories https://github.com/DiarmuidM/aqmen-data-wrangling-in-R https://github.com/DiarmuidM/aqmen-predictive-analytics-in-R https://github.com/DiarmuidM/aqmen-data-visualisation-in-R The open access nature of these resources will contribute to post-workshop impact. Professor Gayle will maintain and update these repositories. This will represent and institutional commitment by the University of Edinburgh. |
| Sectors | Education,Other |
| URL | https://github.com/DiarmuidM |