From Social Science to Data Science

Bernie Hogan (he/him/*) is a Senior Research Fellow at the Oxford Internet Institute and the current Directorof the University of Oxford’s MSc program in Social Data Science. Bernie’s work specialises in how toleverage computational tools for creative, challenging, and engaging methodologies to address social scienceresearch questions about identity, sexuality, and community. His favourite work in this area focuses on thecapture and analysis of personal social networks, using both pen-and-paper tools and the recent free opensourceapplication Network Canvas (https://www.networkcanvas.com). He also has a keen interest in howlanguage is used to either bring people together or push them apart using large scale quantitative data. Hehas published over 40 peer reviewed articles and presented at over a hundred conferences, including severalkeynotes. His most famous work reconsidered Goffman’s offline stage play metaphor of self-presentation foronline 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 completedhis undergraduate and graduate degrees in Canada. His undergraduate was in Sociology and ComputerScience at Memorial University in St. John’s, Newfoundland, Canada. His graduate work was in Sociologyand Knowledge Media Design at the University of Toronto. During that time Bernie interned at MicrosoftResearch. 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. Mostof this research is available from his departmental homepage, (https://www.oii.ox.ac.uk/people/hogan) andor/his GitHub, (https://www.github.com/berniehogan).

• Part I: Thinking PragmaticallyChapter 1. IntroductionChapter 2. The SeriesChapter 3. The DataFramePart II: Accessing and Converting DataChapter 4. File TypesChapter 5. Merging and Grouping DataChapter 6. Accessing the WebChapter 7. Accessing APIsPart III. Interpreting Data: Expectations versus ObservationsChapter 8. Research QuestionsChapter 9. Visualising ExpectationsPart IV: Social Data Science in Practice: Four ApproachesChapter 10. Cleaning DataChapter 11. Introducing Natural Language ProcessingChapter 12. Introducing Time Series DataChapter 13. Introducing Network AnalysisChapter 14. Introducing Geographic Information SystemsChapter 15. ConclusionAbout 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.