May is the month for Data Visualisation! Stay tuned to the SAGE Campus blog to discover from Andy Kirk, data viz guru, the small decisions that make a big difference to your visualisations.
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Phillip Brooker is an interdisciplinary researcher in the field of social media analytics, with a background in sociology and sociological research methods. Phillip co-convenes the Programming-as-Social-Science (PaSS) network (www.jiscmail.ac.uk/PaSS) which explores computer programming as a subject and methodological tool for social research and teaching. Phillip is also our social science expert and course instructor on Introduction to Python for Social Scientists. We spoke to him about his background in computational social science and what he’s been working on recently.
You’ve probably heard of R, the statistical software package, but are you aware of all its benefits? I’m going to briefly outline the main advantages of R, with a focus on how it can help you clean up and sort all that messy data that threatens to disrupt your research project if not dealt with properly (as well as give you a major headache!).
To investigate data and tell a story with it requires a process and a reliable set of tools. Martin Hadley, course instructor for Interactive Visualization with R, explains how 3 interactive viz tools can be applied to social science research.
We asked Phillip Brooker, an interdisciplinary researcher in the field of social media analytics, and social science expert on Introduction to Python for Social Scientists, for his advice on using data science methods in social science research.
Phillip has background in sociology and sociological research methods, and co-convenes the Programming-as-Social-Science (PaSS) network which explores computer programming as a subject and methodological tool for social research and teaching. So if you’re looking into computational social science, listen up, you’re in good hands!
This blog post talks about how online learning can benefit learners and in particular how it allows learners to fail.
The digital age has made huge amounts of data available for analysis in the form of newspapers, blogs, social media feeds, government documents, the list goes on!
In this post we consider some of the challenges of working with such vast amounts of data and the role that QTA plays.
The big data revolution offers huge potential for social scientists. However, the successful collection and rigorous analysis of this data require new skills, new collaborations, new research methods, and new computational tools. Learning data science skills may seem daunting, but there are many reasons why learning to program will benefit both you and your field of study. Find out why here.