Source – siliconrepublic.com
If you want to be a data scientist, what do you need to know? We asked some of the top employers for advice.
There is a massive data science talent gap across the world but it’s still considered an extremely exciting career to get into.
Data scientist, in turn, is now being touted as the hottest career to get into, even being dubbed the “sexiest job of the 21st century” by Harvard Business Review.
But for those who want become a data scientist, what do you need to know? What are the best tips to start your budding career in data science?
Often, the best career advice comes from those already within the industry. With this in mind, we spoke to employees from some of the top companies that employ data scientists to find out their key tips for those working in the sector.
1. Have a willingness to learn
As with all jobs, a willingness to learn is extremely important. Rory O’Riordan, a business intelligence graduate working at eShopWorld, recommends researching the courses that are available.
“There are a plenty of online resources available where you can take courses in a range of tools useful for working with data,” he said. “At the beginning, I found working with data can be overwhelming but it is important to take a step back and approach it logically, and don’t be afraid to ask questions.”
2. Keep the bigger picture in mind
For a data scientist, the devil is often in the detail. But, according to Deepanand Saha Roy, director of data analytics at Pramerica, it’s important to take a step back to see the bigger picture.
“It’s a no-brainer that you need to learn to fall in love with data, but not so deeply that you can’t see the wood for the trees,” he said. “Analysis well done is only half the battle. The other half is about how simply you can explain your work to, let’s say, your non-techie parents.”
3. Understand the needs of the end user
Data science isn’t just about the data that’s in front of you, it’s about the entire problem and the solution you’re trying to find. Oisín Lyons, a data scientist at Aon Centre for Innovation and Analytics, says you must have the confidence and drive to take a problem through to its solution.
“You may not know how to solve a problem at the start but don’t let this frighten you. Just have an inquisitive mind to pore over datasets and build your model step by step,” he said. “Understanding how the end user will interact with your data science model is key, and ultimately shapes how solutions are built.”
4. Grow your storytelling abilities
If you want to be a data scientist, you need to be a good storyteller as well as a good analyst. According to Oonagh O’Shea, analytics senior manager at Accenture Digital, communication of your output is a crucial part of your job.
“Data science is about applying analytics to solve business problems, and how you communicate the output of an analytics process is critical,” she said. “Work on building a business and storytelling aptitude as much as your data and analytics skillset.”
5. Always be curious
Just like your willingness to learn, curiosity is an important trait – not only for a data scientist, but for anyone looking to forward their career. Luca Petricca, a process monitoring analytics engineer at Bristol-Myers Squibb, explained to us the importance of being curious in data science.
“Always be curious in order to understand in detail all the aspects of the data that you are going to analyse,” he said. “Be sure to understand the scientific background behind this data and, if you do not have the scientific knowledge of the process that you are going to analyse, become best friends with the subject matter experts.”
6. Embrace the science side of data
Finally, a budding data scientist will need to focus on the scientific side of the role as well as the technology. Colin Melody, senior manager in data analytics at Deloitte, said a data scientist must embrace the scientist part of their role.
“At all times, you are looking to provide evidence which supports an idea. This means, from end to end, you must challenge your assumptions, your data, test and retest, refine, and start again,” he said. “There are a myriad of tools and technologies available for data scientists and, while it is not necessary to know how to use all of them, try to get a sense of what it might take to grow your toolbox.”