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Becoming a data scientist

Data projects require a wide range of skills. Watch Tom Pinder explain more.
I’m Thomas Pinder, and I study maths and statistics here at Reading. And I finished three days ago, so I’m fresh. So I never grew up wanting to be into data. I’d say during the second year module here at Reading, we did a module which involved analysing some data provided Pampers, the nappy company, and finding out what makes the perfect nappy. So that obviously involves quite a heavy amount of programming and statistics. And I sort of did that and I thought, oh, this is quite interesting. So then I applied to do a placement year, and I spent 15 months working at two different companies, SOP and the IEA, working on real-life data.
And from then on in, that’s what I wanted to do. I was given a very large dataset which analysed phone network data. Off the top of my head, I think the data was around 12 million lines of data long– far too big to push around an Excel or something. And I was tasked with the job of making this data visually appealing because you can imagine a 12 million long spreadsheet isn’t very nice to look at. And it was what can we sort of gain from this data? And I decided to join in some socioeconomic data. So using data from the 2011 census, we could sort of analyse the networks.
If you imagine sort of lots of dots and lines joining them up, we can analyse that, but with a more real-life view on it. And Scottish and English data were separately found, so it was a case of marrying the two together. But most of it came from online databases, just sort of downloading it off the Office of National Statistics website. And then just sort of looking at it then and pushing it into the right format. It’s really interesting sort of going into a job and just seeing all the stuff you’ve learned in textbooks and from lecturers actually how it’s used in industry. And only then, I think, do you actually truly appreciate how useful it is.
Prior to that, I just sort of thought, oh, I can’t really think of a time when I’d ever use that in the real life. And it was when I got into a job, I thought, actually I need to use that skill now. And I found myself digging out notes from first and second year to refresh my memory and then applying it in work. I think that does give you a sort of true appreciation of the sort of skills you’re learning.
The most surprising thing in the work that I did was probably just how much you can draw out of what’s such a small amount of data with the tools sort of available now in standard programming languages and the sort of data that’s online. It’s amazing how a small dataset with relatively little insight can grow to be something so sort of in-depth and sort of broad almost– and just the details that you can pull out of that sort of data. I think the biggest skill that I’ve sort of had to use as a Junior Data Scientist would be keeping an eye on the end goal.
It’s quite easy, particularly if you’re quite technical or mathsy, to see some features and think, oh, that’s cool. We could build that in. But you have to sort of keep reminding yourself, is that actually useful for the end client or the end goal? Or is it just something that you personally find cool? And it’s keeping yourself constantly in line with the sort of aims of the approach is quite a skill. And I think it’s one that everyone sort of falls into the traps initially.
And then once you’ve sort of got your placement, it’s a case of just sort of talking to people in the companies or trying to tap into their knowledge and sort of learn as much as you can whilst you’re there. I think the major benefits of doing a placement, from my experience, was you learn to apply a lot of the skills you’ve learnt in your degree. And those methods and algorithms you learned in textbooks, suddenly become real-life applications, and you can sort of see the true use in them. You do build up a really large network of people you know.
And that becomes useful when you’re sort of looking for advice on jobs after university or maybe studying for a master’s or higher education. Finally, as a student, it’s quite nice to see money going into your account rather than out all the time. So a paid placement is always nice.
How do you become a data scientist? Watch Tom Pinder, a graduate from the University of Reading who interned at the IEA, highlight his journey into data science.
As you’ve seen throughout the course, data projects require a wide range of skills which usually include the work of many rather than just one individual. Read the next Step to understand why data scientist requires a collaborative approach and the range of career opportunities available in this field. Don’t forget to mark this Step as complete before you move on.
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