16. Big Data and social research – 2 years on

Next month will be two years since my first post. Back then examples of big data in social research and evaluation were more limited and the courses on offer were not so specialised for a social research audience. Things are different today.

1) A wider range of social research methods courses connected to big data, and aimed at the beginner. From NCRM this autumn, for example, Using Sensors in Social Research, Introduction to Programming, Introduction to Python and Structural Equation Modelling in R.

2) The annual Merltech (technology enabled monitoring, evaluation, research and learning) conferences in London and DC have grown in profile and attendance. In response to demand they now offer pre- workshops in big data and evaluation. They are also trying to understand if/how evaluators use big data/data science approaches via this survey (still open & encouraging responses at the time of writing).

3) Sage publications, via Sage Campus is now offering online data science courses ‘by social scientists, for social scientists’. I had a look at their offer back when I was deciding which paid course to sign up for. It remains more expensive than DataCamp, but may be more specialist, it’s very difficult to tell from the current syllabus information. If anyone has tried their courses I would love to hear from you.

Sage Campus do offer a free taster course that takes about an hour to complete and aims to
“demystify big data and explore how it is impacting social research. It is a great starting point for anyone interested in big data analytics, social science and computational methods. It also provides a strong foundation for those interested in going on to develop programming skills in either Python or R.”

The course is divided into seven short topics for a very broad overview, but it is a good place to start if you are totally new to big data. Some of the early topics are a bit skewed to a market research perspective, before getting into the potential for big data and surveys. They also emphasise engaging with big data as a phenomenon, for example to explore the growing justice issues around how data ownership and use effects peoples’ lives and well-being.

While the last couple of topics touch on the implications of big data for social science practice, and provide some practical examples, you would need to dig into the linked studies for a more meaningful understanding. Many links require subscription, with a few open access, such as this very short paper on where the term ‘Big Data’ originated, and how it is developing as a discipline and this on using Big Data to study rare events.

I don’t think the course provides a strong foundation for developing programming skills, as is claimed, perhaps more of a useful first step before looking for programming specific content. A description of broad phases in working with big data (collection, cleaning, analysis, visualisation) is provided, rather than an introduction to the specific knowledge and methods landscape for programming.

Compared to two years ago there are more, easier to find options for social researchers interested in big data, computer science and programming. I can only imagine what it will look like two years from now. Dr Susan Halford talks about new collaborations between social scientists and computer scientists and this is something I will cover in my next post, methodological playgrounds.

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