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Key skills to thrive in data science and analytics roles

By 11/03/2022June 27th, 2023Blog Posts, Thought Leadership
Two people looking at a computer screen displaying code.

We recently launched To Affinity & Beyond, a series of meetups for the consumer analytics and data science community.

We’ll be creating more events, content and networking opportunities over the coming months and beyond for data science practitioners, students or those with a vested interest in the industry.

Our first event saw HyperFinity’s Jessie Stuart talk everything affinity analytics, with a presentation on shocking online grocery substitutions. We also hosted a panel discussion on the key skills to thrive in data science and analytics roles with HyperFinity’s Anna Cescon and guest panellists Michelle Morris from University of Leeds and Charlotte Barnes from Asda. Click here to read our speakers’ bios.

If you’re looking to get into the industry, our panellists have a whole host of tips for you…

No two days are the same when you’re a data scientist, analyst or professor.

As a data scientist for a consultancy, Anna Cescon might be collaborating with the team, attending meetings, or working independently. Her day might involve statistics, modelling and storytelling – whatever is needed to create value for clients. Every day is different.

If you’re a data engineer like Charlotte Barnes, a typical day might include supply chain forecasting – from creating platforms, solving coding issues, changing logic, providing resources, and supporting or coaching the wider team.

Meanwhile, as an Associate Professor in Nutrition & Lifestyle Analytics, Michelle’s typical day could involve teaching, promoting data science, advisory work and working with retail partners. Governance and data licenses also feature heavily, as the nuts and bolts of data science.

Data cleaning plays a big part in typical data science and analytics roles.

If you’re getting into the industry, expect a lot of data cleaning. Data science and analytics isn’t just about finding the fanciest possible model for a problem. It’s also about solving a mystery: what’s the problem? What do I need to be aware of? What needs adding or removing? What do I need to understand?

Remember to take a step back and look at the bigger picture.

Computer screen displaying lines of code.

Storytelling isn’t just for authors.

Data scientists, analysts and engineers need a range of technical and soft skills. Technical skills might include coding, programming languages and statistics. However, Anna has found communication is equally important. You need to be able to communicate ideas to clients or stakeholders effectively – this is often referred to as ‘storytelling’ within the industry.

You can learn more about storytelling from data visualisation experts on LinkedIn and Twitter. Search for ‘visualisation principles’ for resources you can use regardless of language.

Michelle also cites an appetite to learn as a key skill – from seeking out new solutions to challenges, to outside-the-box thinking.

Google is your friend.

Data science is ever-changing. New technology is always on the horizon.

Anna recommends Google and YouTube for finding free resources – especially when you have a concrete problem to solve.

If you’re looking for more formal training, many universities (including University of Leeds) offer undergraduate, postgraduate and PhD courses. Coursera and DataCamp also offer free data science courses.

Two hands typing on a silver laptop keyboard.

Be prepared for continuous learning.

Big data and technology are moving fast. The data science industry is unique, as it’s constantly changing. The application of data and tech varies wildly from company to company. Charlotte advises asking lots of questions so that you fully understand the industry and the problems you’re solving.

If you’re looking to get a job or research positions, building a good network is important. Getting involved in communities like To Affinity & Beyond is a great for networking. Always be prepared to keep learning, no matter the stage in your career.

Diversity is valuable in data science.

Our panel discussion was the day after International Women’s Day 2022.

There are lots of female role models in the data science and analytics industry. Throughout Michelle’s career, she’s seen more and more women taking data science courses and she often works with women within retail organisations.

There’s still a barrier for women accessing more senior roles, but the panel agreed times are changing.

Charlotte underlined the value of diversity in data science. A diverse team from a collection of backgrounds is really important when problem solving, as it provides more considerations and angles.

Principles are more important than specific tools.

Many tools in data science toolkits execute similar tasks. Anna explains that the principles behind them are more important than the tools themselves, as you can apply them anywhere.

For example, Tableau is widely used, but as it’s a licenced software it might not be available at every company you work for. Making sure you have an open-source product in your toolkit (such as R or Python) is beneficial.

The path to a career in data science isn’t linear.

There are various routes into data science – employers aren’t just looking for people with a computer science degree.

Charlotte recommends looking out for internships and graduate schemes as a route into the industry.

Leeds Institute for Data Analytics offers a 12-month paid internship programme, including a training budget to learn the skills needed to complete two six month projects. Internships like this are particularly beneficial for career movers, as they’re paid.

A long, straight road stretches into the distance. The road is lined with grass and hills.

Demonstrate passion and curiosity at interviews.

If you’re preparing for interviews for data science and analytics roles, it’s important to show you’re passionate. Meanwhile, if you can demonstrate an understanding of the context of the role you’re applying for, it will help you stand out.

Anna advises that applicants shouldn’t be afraid to be honest about what they do and don’t know. It’s okay to admit you don’t know something if you can explain you’re curious and motivated to learn.

Finally, our panel host and former recruitment consultant Jag Tumber recommends setting up a LinkedIn profile. Network within the data science community, follow leaders in the industry and talk about the courses you’ve taken or the Kaggle challenges you’ve solved.

Join our LinkedIn group for updates on future events and listen to the full panel discussion below:

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