Your pathway into DS

  • 12 April 2022
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Conversations with my teammates have shown me that they paths to a successful datascience career are as diverse as python packages. Four years ago, I would have probably waved off the sheer thought of pursuing a career in data science, I laughed tiredly. My interests at that time were more focused on political science and history. However, after a few detours I found my passion for the field and today I couldn't imagine doing anything else. 


I would be super interested to hear how other people ended up in their current positions and what convinced them to take this path. 


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I ended up working in recruitment after university, luckily it was recruiting for Data & Analytics roles. I found it fascinating speaking with Data Scientists about their roles, the impact they were making at the businesses they worked for and the potential for Data Science to drive change! I specialised in recruiting for this area to learn more about the industry and tried my hand at learning relevant skills in my spare time. 

One of the candidates I spoke with had recently graduated from a Data Science bootcamp with General Assembly and highly recommended it. At this point I seriously began looking at my options and decided to take the plunge and join the bootcamp myself. 

After the bootcamp I worked as a Teaching Assistant for the next cohort of students while looking for DS roles. Teaching really helped solidify my understanding of the concepts and was a great experience. 

Not long after that I landed my job here at Peak! 

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I did a PhD in evolutionary Biology/Psychology and wanted to be an academic initially, but over time became a bit fed up with the academic system. I was thinking about what bits of my PhD I liked the most, and felt like I loved R programming, big data and statistics. Academia also told me what I didn’t like; I had felt pressured to do research on topics that were interesting to a few select colleagues but didn’t really contribute anything to the world. Also the job security was terrible and you have to move around a lot!

I looked into what kinds of careers had the bits I liked about academia but not the bits I didn’t like, and landed on data science!

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I got into data science through an internship I did while studying maths at university. The internship had undergraduates working in groups for a summer on problems posed by industrial sponsors. We had both industry supervisors and academic supervisors and it was such a good way to learn about real world problems and work with some real world data sets. The problem my group worked on used natural language processing which I found so exciting as it seemed like such a cool way of combining my interests in both maths and languages! After that, I then joined a centre for doctoral training in data science which was a four year programme, starting with a one year masters and then a three year PhD. I loved this programme and really enjoyed getting to look at natural language processing problems for so long and in so much depth. After that I decided it was time to take the plunge and start trying to apply data science in a properly real-world setting which is what led me to join Peak! 

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I was always interested by the integration between statistics and consumer behaviours during my Economics degree, but was unaware of any roles out there which would allow me to use both of these! I took a role in Buying but found myself more drawn to supply and demand analytics. Whilst I was expected to ring suppliers and enquire around new raw materials, I just wanted to number crunch! I also discovered SQL through a friend which I thought was magical (having no previous knowledge of any programming languages). I had to reflect for a while on what was important to me and would bring genuine fulfilment from a role, and I realised that Analytics had it all. So I began the search and eventually found Peak!

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I’ve always said I had a pretty non-traditional entry into Data Science. Whilst I did both Physics and Maths at University I never finished my degree and moved first into running coffee shops (!) then eventually into BT as a person on the phones.

Then my manager taught me Pivot Tables and VLOOKUP!

Ever since then I moved into more and more analytical roles, first learning the ropes within a big enterprise organisation, then ultimately leading Analysis teams. This gave me a huge appreciation for the power of Data in a business, but also (and more importantly) the challenges to influence an organisation with Data. When I left BT and I was looking at roles I was really drawn to Data Science as a discipline. I’d had some conversations before I left with a few folk around what to learn (learn R!) and what to look for.

I realised that I had been applying Data Science approaches to all my work, ever since I’d learned Pivot Tables (aggregation!) and VLOOKUP (joins!) and I was fascinated by the predictive side. I then just went off and learned R and Data Science and snagged my first DS role. And the rest was history. 

I think the experience I got within a business (and a big one as well) helped set me up to be successful as a DS, then leading DS teams. Being driven to delivery (no hanging around when you’re doing analytics!) put me in good stead.

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My year in industry was in delivery management at a digital consultancy, where I spent some time working on a project using big data to improve learning from patient safety events in healthcare settings. I was just chatting to a colleague about how fascinating I was finding it all, who suggested I have a bit more of a look into data science as he thought that it might align with my strengths and interests. And the rest is history….

(The rest is actually some carefully selected final year undergrad modules and then an MSc Data Science 👌) 

 

 

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In the awful covid summer of 2020, right after finishing my BSc in Mathematics and Philosophy, I stumbled onto a Data Science online course ad, while scrolling on a social media. I was bored and confused about what to do next (as we probably all were). The online marketing campaign was on point. The deadline for signing up was midnight, how perfect, I thought and signed up. I really enjoyed the course and after finishing it I signed for another one, which was a bit more advance. I really enjoyed it as well and this solidified my belief that data science is what I wanted to do after uni. 

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