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Sector Insights: Data Science

By Joe O'Brien, on 17 November 2020

Read time: 3 minutes

Written by Susanne Stoddart, Recruitment & Selection Advice Manager

What is Data Science?

Data science is concerned with turning raw data into meaningful information that organisations can use to inform their decisions and improve their work. Data scientists work with huge datasets, such as online reviews of products and services or health care records. This big data is generally too large for analysis by using conventional statistical methods and analytical tools. Rather than data science being a sector in itself, there is need for data scientists across a wide range of sectors, including technology, transport, retail, finance, consulting, government, manufacturing, pharmaceuticals and health care. Everyday activities are increasingly leaving digital footprints and employers seek workers who can help them make sense of it.

Meet the Data Scientists

I recently contacted Pooja Trivedi and Adam Davison on LinkedIn to find out about their experience in data science, and about their routes into this area of work. Pooja currently works as a Data Scientist at Curve and completed her MSc in Social Research Methods at UCL in 2019.

Adam is Head of Insight and Data Science at The Economist. Adam completed his MSci in Physics at UCL in 2006 and his PhD in High Energy Physics in 2010, also at UCL.

Did you do anything during your time at UCL or after you finished your degree that helped prepare you for your current job?

Pooja: I currently work at Curve as a Data Scientist, and I learned about them through the UCL Careers Fair in the summer of 2019. I started working at Curve as an intern while I was doing my course, so it was a very unique learning opportunity.

Adam: Not especially unfortunately. I was lucky in that my PhD research work was focussed on analysis of very large datasets coming from the Large Hadron Collider at CERN. I never had a strong career plan to move to data science, and debated the merits of leaving vs. remaining in academia for a long time. Luckily when my moment to move came a lot of my skills were a good fit for what industry was looking for.

What are three key skills that you use in your current job?

Pooja: The three main skills involve: attention to detail, the ability to think about the customer and their needs, as well as working well with others, as there are many stakeholders who rely on data.

Adam: When I first moved to data science I was working hands-on problem solving myself so it would have been software engineering, data analysis and a knowledge of statistical modelling. Over time career progression means that now I spend much more time trying to connect what is possible with the data to the problems the business needs solving, so today the list would focus more on interpersonal skills and a broad knowledge of techniques and technologies.

What does a typical day at work look like for you?

Pooja: My role as a data scientist is unique because I don’t just work in quantitative areas. I also do a lot of qualitative research that involves customer interviews, research on consumer behaviours, and research on the field as a whole.

Adam: When I first transitioned to commercial data science I was surprised at how little my job differed from the research I was doing in academia. A typical day would have been discussing an issue the business was facing with my manager, then spending most of my day writing code (SQL/Python) to access and convert data into a form I could analyse or build a statistical model around.

What would be your top piece of advice for current students interested in a career in data science?

Pooja: It’s important to at least know SQL, and there are bonus points if you know Python. I’d also recommend that anyone looking to pursue data science finds a field that they are interested in, because they will be constantly looking at the data for that field.

Adam: Don’t assume you need to know everything about every technology or complex machine learning tool to apply for a job, everyone recognises that entry-level candidates will be lacking some skills. For someone applying for their first role I’m looking for someone that’s done some data analysis and statistical inference, and most importantly understands why they did it and how the tools they used work. I expect to find gaps where training will be needed, so if you need to get better at Python or learn about a machine learning technique that’s a secondary concern.

What Next?

If you’re feeling inspired by Pooja and Adam’s careers in data science, here are some ideas on what you can do right now to start developing your skills and building your network: · Become a member of the UCL Data Science Society to gain access to the Data Hub, offering workshops, articles, competitions, networking opportunities and more. · Sign up to Data Science Weekly, a free newsletter featuring curated news, articles and jobs.

· Develop your data science skills with online data science competitions hosted by organisations such as Kaggle and Topcoder.

· Build your network by reaching out to data science experts on platforms such as LinkedIn and UCL’s Alumni Online Community. You can find out more about using online platforms for networking in our recent blogpost on 5 Key Resources for Networking from Home.

· Remember that if you would like to explore your career in data science further you can book in with UCL Careers for a one-to-one guidance appointment.

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