Thomas Hills is a Professor of Psychology at the University of Warwick. He teaches courses in quantitative approaches to behavioral science, language, and computational social sciences. His publications include work in psychology, communications, education, and economics, and focus on issues associated with large-scale analysis of language, memory, and wellbeing. He is currently the Director of the Bridges Doctoral Training Centre in Mathematical and Social Sciences and the Co-Director of the Behavioural Science Global Research Priority at the University of Warwick, both of which aim to provide and develop quantitative approaches to data in the social sciences. He received a Mid-Career Fellowship from the British Academy for his application of network science to the early development of bilingual lexicons.
The aim of this workshop is to provide participants with an understanding of how data science is being applied to large data sets ('big data') to make data-driven decisions and to answer theoretical questions about human behavior. Some of the kinds of questions this approach has been involved in include predicting consumer purchases, detecting regional and historical changes in happiness, and deriving the structure of human memory from online text. Many questions in the social sciences have 'big data' analogues, which can be made accessible by thinking about how the questions might be addressed using automated procedures designed to acquire data from existing sources. More often than not, this involves data volumes orders of magnitude larger than what is used in common practice.
The course will begin by providing participants with a broad overview of data science and big data applications to existing problems across the social sciences. Specific cases will then be taken up for a more detailed analysis of their methodological approach, and participants will work with data to replicate existing findings and investigate novel hypotheses of their own. Finally, participants will receive guidance in developing and answering questions of their own.
On completion of the course, participants will be able to recognize and implement many common approaches to data science applications to big data, and take the first steps towards formulating and addressing problems of their own as data scientists. Participants will also be provided with detailed information about how to follow up and learn more with respect to their particular area of interest.
The course will use R and Excel.
Methodological texts
Case Studies -- Applications of Data Science to Big Data
Optional Reading
Participants taking this course should be familiar with basic statistics and regression. The course will cover some mathematics, but only sufficient to provide an intuition for how specific algorithms work. All the algorithms used in class can be implemented in Excel or R (see the basic texts), and training will be provided such that participants can perform analyses in these environments.