Paperback : $108.00
From Social Science to Data Science is a fundamental guide to scaling up and advancing your programming skills in Python. From beginning to end, this book will enable you to understand merging, accessing, cleaning and interpreting data whilst gaining a deeper understanding into computational techniques and seeing the bigger picture.
With key features such as tables, figures, step-by-step instruction and explanations giving a wider context, Hogan presents a clear and concise analysis on key data collection and skills in Python.
From Social Science to Data Science is a fundamental guide to scaling up and advancing your programming skills in Python. From beginning to end, this book will enable you to understand merging, accessing, cleaning and interpreting data whilst gaining a deeper understanding into computational techniques and seeing the bigger picture.
With key features such as tables, figures, step-by-step instruction and explanations giving a wider context, Hogan presents a clear and concise analysis on key data collection and skills in Python.
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
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).
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*
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