18. Sense about Data Science
Thanks to a recommendation from Tracey Gyateng of DataKind, I went along to the launch of ‘Data science: a guide for society’ from Sense about Science, an independent campaigning charity that challenges the misrepresentation of science and evidence in public life. The purpose of the guide is to equip people with the tools they need […]
17. Methodological Playgrounds
I first heard the term methodological playground in 2014, from Institute for Education’s Professor Carey Jewitt. She was talking about her work with digital artists and social researchers, synthesizing methods to open up different perspectives, generate imaginative research questions, and create a wider range of research tools. Around that time I was working on an […]
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, […]
15. Where R we going – part II?
In my last post I talked about why I chose to pursue R via DataCamp. Between January and the end of March I completed the first three courses of the data analyst with R track. I found a rhythm for learning in limited time – structured exercises through the DataCamp course, daily practice to recap […]
14. Where R we going?
An upsurge in paid work meant professional development took a back seat for a while. In this post I talk about finding a realistic way forward for developing my data skills, within a limited amount of time. In my last post, ten months ago, I described lessons learned from dipping into different software and programming […]
13. Try, try again: getting started with some software and coding
In this blog I will share what I have learned from sitting at my desk trying to install and use new software and code. The aim is to help you get started and, more importantly, keep going. First up, an overview. • Installing software and loading data rarely worked perfectly and takes some time. Think […]
12. Back to reading, Bit by Bit
I paused efforts on an introductory course on machine learning and statistical inference after finding Professor Matthew J. Salganik’s draft book, Bit by Bit: social research in the digital age, which has been available for open peer review since summer 2016 (click on the book title to access it, and here to learn about the […]
11. Jumping through Hadoops
Week one of the course introduced Apache Hadoop software, used for distributed processing of massive unstructured data, week two took us deeper into accessing and operating on big data and took me far, far away from my comfort zone. The least geeky explanations of Hadoop and MapReduce that I could find are here and here […]
10. Getting going with big data analytics
After social media analytics I signed up for this Big Data Analytics programme. Also by Queensland University of Technology, it is a series of four courses covering the collecting, storing and managing (week 1); statistical inference and machine learning (week 2); mathematical modelling (week 3) and data visualisation (week 4). (As an aside for anyone […]
9. A pause for tips on evaluation
This post is a short detour from the Big Data path, and a brain dump of ways of working I find to be helpful when commissioning an evaluation, or responding to an ITT to deliver an evaluation (if you have ever asked me for advice then it will look familiar!). As I started to explore […]