Big Data Skills in Social Sciences

Time: 1200 - 1600

Venue: Boardroom, Arthur Lewis Building, The University of Manchetser

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This October the Cathie Marsh Institute for Social Research (CMIST) Data, Skills & Training Research Group will be organising a Big data Skills in Social Sciences event.

Many new sources of data have become available for studying individuals and society in recent years.  Social media data, transaction information, administrative data, sensor data and other new sources of data have huge potential for social scientists to learn more about how people interact, what they buy, where they go and many other aspects of society for which there was previously little data. 

Many of the skills that are needed in this new environment are not ones that social scientists have traditionally learned.  For example, dealing with very large datasets, collecting data from websites, storing and manipulating very large datasets, dealing with different kinds of data e.g. structured vs unstructured data and deriving information from the data using appropriate analyses.

Aimed at social science researchers or lecturers with limited knowledge of big data, this event aims to discuss what new skills are needed to do social science research using these new forms of data and the challenges of acquiring these skills. 

Programme

12.00    Lunch and registration

12.55    Introduction
            Rachel Gibson, Director of CMIST, and Magnus Rattray, Director of the Data Science Institute, University of Manchester

13.00    Big data skills for online data
            Dr Suzy Moat, University of Warwick

13.30    Big data skills in health
            Dr Georgina Moulton, University of Manchester

14.00    Tea/Coffee

14.30    Big data skills in geo-spatial or smart cities data  
            Dr Richard Kingston, University of Manchester

15.00    Big data skills in transactional and consumer data
            Dr Andy Newing, University of Leeds

15.30    Big data training needs for social scientists – discussion

16.00    End