Thinking of becoming a data scientist? Here are three areas you should consider focusing on to set yourself up for success.
Data scientists with the right combination of skills are in high demand. But what are hiring teams on the lookout for? As with many roles, both technical expertise and soft skills are important. As data scientist Vin Vashishta wrote, data science without soft skills has “limited value to the business”.
An increasingly diverse skillset is proving essential to data science and its future. “Very non-typical skillsets” are getting more attention, said head of data science at Aon’s Centre for Innovation and Analytics (ACIA) Jennifer Cruise.
So, don’t be put off pursuing a career in data science if you think your skills are mostly on the softer side. Medb Corcoran, the Ireland lead at Accenture Labs, explained: “Data scientists use a blend of skills including data inference, algorithm development and technology to solve analytically complex problems.
“These skills are drawn from across mathematics, statistics and computer science, but you don’t have to be a mathematician or a computer programmer to pursue careers in data science, artificial intelligence or any of the other STEM areas.”
Coding is one of the most obvious skills a data scientist needs. CodeLani reports that there are an estimated 250 to 2,000 coding languages in the world.
For those starting out in the field, the number of languages out there can be daunting. Dun & Bradstreet’s director of emerging analytics, Eoin Lane, recommends starting with Python: “The industry has really standardised on Python at this point for data science.”
Other languages beginners should consider learning include “major deep-learning frameworks” such as TensorFlow or PyTorch, advised Asos lead data scientist Ben Chamberlain.
And ones to keep in mind as you move further along your career journey are Objective-C, Golang, Windows PowerShell, Excel VBA and Kotlin. According to freelancing platform Upwork, these were the five most highly paid coding skills on its site in the first half of 2020.
Communication is a critical skill in virtually every industry. But how does it impact your abilities as a data scientist? According to Vashishta, people working with data must be able to create messages. This involves distilling large volumes of information about a project into “a few high-level points”.
The message should also consider who you’re speaking to and what they need to know. After that, it’s important to stick to and retain your message. It’s easy to get distracted by questions when working with data, Vashishta said, so remember to “avoid those rabbit holes and stay on point”.
Message retention applies to your client or stakeholder too. You’ll know you communicated something effectively if the people you are telling understand and remember what you have said. You also need to really hear what your stakeholders are asking of you. As Dun & Bradstreet’s Deirdre Linnane said, effective communication is a “vital skill” for understanding client needs and “collaborating with multiple teams”.
Curiosity might not sound like a skill, but it’s high on the list for many recruitment teams. Corcoran tells us that “curiosity is a key skill for a data scientist as the tools and techniques we use are constantly evolving”.
“As well as basic technical skills, I always look for evidence of candidates being curious to learn,” Corcoran said. “It’s also really important to consider the real-world application. So having someone who thinks about how the models they develop will work in the real world is crucial.”
Karl Heery, head of IT at the Aon Centre for Innovation and Analytics, agrees. His team helps develop industrial-scale data analytics solutions, and builds models, dashboards and insights. This requires a range of technical skills – from cloud engineering to IT security – but “lots of curiosity” is key, he said.
Like other skills, it’s important that you demonstrate your curiosity at interviews. Showing potential employers that you’re eager to keep learning throughout your career can help. Ann Marie Clyne, head of HR at Mastercard, said: “If it is not specifically called out during an interview, interviewees should look for ways to be able to bring it into the conversation.
“Be curious about the company’s learning strategy, seek out opportunities to talk about any special projects worked on outside of the day-to-day role, and ask what supports and resources are in place to provide opportunities for continuous learning.”