From Social Science to Data Science

Bernie Hogan (he/him/*) is a Senior Research Fellow at the Oxford Internet Institute and the current Director of the University of Oxford's MSc program in Social Data Science. Bernie's work specialises in how to leverage computational tools for creative, challenging, and engaging methodologies to address social science research questions about identity, sexuality, and community. His favourite work in this area focuses on the capture and analysis of personal social networks, using both pen-and-paper tools and the recent free opensource application Network Canvas (https://www.networkcanvas.com). He also has a keen interest in how language is used to either bring people together or push them apart using large scale quantitative data. He has published over 40 peer reviewed articles and presented at over a hundred conferences, including several keynotes. His most famous work reconsidered Goffman's offline stage play metaphor of self-presentation for online life (Hogan, 2010). This piece probably helped in popularising the term "algorithmic curation". Before working at the University of Oxford's Oxford Internet Institute (https://www.oii.ox.ac.uk) he completed his undergraduate and graduate degrees in Canada. His undergraduate was in Sociology and Computer Science at Memorial University in St. John's, Newfoundland, Canada. His graduate work was in Sociology and Knowledge Media Design at the University of Toronto. During that time Bernie interned at Microsoft Research. Bernie lives in Oxford, UK with his husband and their sprawling vinyl record collection. He tweets (and collects vinyl) under the moniker "blurky" because it is a very rare word that sounds like Bernie. Most of this research is available from his departmental homepage, (https://www.oii.ox.ac.uk/people/hogan) and or/his GitHub, (https://www.github.com/berniehogan).

Part I: Thinking Pragmatically Chapter 1. Introduction Chapter 2. The Series Chapter 3. The DataFrame Part II: Accessing and Converting Data Chapter 4. File Types Chapter 5. Merging and Grouping Data Chapter 6. Accessing the Web Chapter 7. Accessing APIs Part III. Interpreting Data: Expectations versus Observations Chapter 8. Research Questions Chapter 9. Visualising Expectations Part IV: Social Data Science in Practice: Four Approaches Chapter 10. Cleaning Data Chapter 11. Introducing Natural Language Processing Chapter 12. Introducing Time Series Data Chapter 13. Introducing Network Analysis Chapter 14. Introducing Geographic Information Systems Chapter 15. Conclusion About the Author

Excellent. The students will love I think. It reminds me a bit of a Andy Field's SPSS/R books, which the students have also loved in the past too. This one has that flavour but also pushes the analytics into the contemporary era with Python. I expect it will be a real success. -- Emma Uprichard