The rise of big data has resulted in a growing need for data scientists. According to a report by The Royal Society, demand for data scientists has tripled over the last five years. So it should come as no surprise that the Harvard Business Review named data science as “the sexiest career of the 21st century”. As a data scientist, your main tasks will be to use computer science, statistics, analytics and business knowledge to draw actionable insights from data — to assist your organisation with strategy and decision making. It’s a highly valued role and lucrative career, so what do you need to become a data scientist?
Strong technical skills
There’s no getting around it — it’s essential to have a strong foundation in both maths and statistics. Understanding data, how to analyse it and what those results means requires an in-depth technical understanding of the subject. For this reason, a number of data scientists have an academic degree in traditional subjects such as computer science, statistics, maths or physics — especially for advanced level roles. However, if you’re determined, retraining is an option. For example, Mastercard company NuData Security’s data science manager, John Hearty, started out as a philosopher. He then completed a data science master’s degree, with no other prior computing skills, but found that philosophy informed his data science approach, stating that,“Philosophy also arms one well for the hypothesis-driven, logical practice of data science.” Given that data scientists are now being asked to adopt ethical practices when it comes to issues such as data privacy, alternative backgrounds like Hearty’s may provide their own advantages.
Programming is another inescapable skill you will have to master to become a data scientist. Python is the most sought over programming language for data science jobs. Luckily, Python is user friendly and relatively easy to learn for beginners. There are plenty of free online courses to get you started and a very active community to support your learning, such as NumFocus and Pydata. A further advantage is that the concepts are applicable to other languages if needed, so it’s highly recommended as a starting language. Other popular programming languages for data scientists include SQL, Java and R (among others).
As you progress in your career, more advanced techniques such as machine learning and neural networks are incredibly useful. However, put in the groundwork first by making sure your coding skills are up to scratch. According to Upwork, a freelancing platform, the five highest-paid coding skills in 2020 are: Objective-C, Golang, Windows PowerShell, Excel VBA and Kotlin. It’s likely your organisation will have specific requirements, so you’re best off learning a widely used language such as Python, then specialising in further skills as you develop.
Organisational knowledge and soft skills
Ok, so you’re a maths whiz and an expert programmer — that still doesn’t mean you’re able to provide useful insights for your organisation. It can help to gain industry-specific knowledge (e.g. finance or healthcare) so you’re able to put datasets in a wider context and develop strong presentation skills so you’re able to convince others of the information you’ve uncovered. Finally, it’s important to embrace business practices such as writing reproducible code and version control, to become a valuable member of a data science team.
Hopefully, this has given you a better idea of how to start your data science career. It’s an industry that requires advanced technical skills, but don’t let that put you off. Data Scientists are in demand, and an alternative background can equip you with a valuable fresh perspective.