Written by George Karkera who worked as a Consulting Intern in November and December 2016
After finishing my Physics degree and undertaking some postgraduate research, I decided I wanted to explore the world outside of science. I had a real interest in education, particularly educational disadvantage. But I also wanted to make use of some of the skills I had gained through my scientific background.
The internship with Mime presented the perfect opportunity to use my data analysis and computing skills in an educational context, where I could make a difference to the life chances of young people.
‘It was often a fun problem solving exercise…’
From day one I was able to get stuck straight in with the initial stages of a special educational needs project.
At first this involved finding and downloading relevant data from online sources and then processing it in a standardised way for entry into Mime’s DataHub. It was often a fun problem solving exercise getting all the data into the same format in both the most efficient and accurate way.
After this, I moved onto the ‘visualisation’ stage. This involved drawing data from the DataHub and presenting it in an easy to understand dashboard. I really enjoyed the challenge of using VBA in Excel to speed up and standardise different processes and I was able to work more in this area as my interest developed.
‘I had excellent exposure to the many aspects of data analysis and consulting…’
Throughout the internship, I had excellent exposure to the many aspects of data analysis and consulting. I learnt a great deal about educational data.
One of the things I found particularly interesting was how the measures for pupil attainment have evolved, and how different measures used on the same data could lead to varied, sometimes contradictory, outcomes.
I also discovered that there are many nuances to educational data. It requires a good understanding of the education system in order to properly represent the data and draw accurate conclusions.
Additionally, the process of gathering and manipulating the data was really interesting; how relatively simple information can be collated and manipulated to answer much more complex questions, with equally complex implications. I found this particularly pertinent for attainment and destinations data based on characteristics such as race and gender, where simple numbers and calculations can be used to produce analysis that fits into a broader, hugely complicated social context.
Overall, I had an excellent introduction to data analysis and it has definitely whet my appetite for socially oriented data science.