Data science has been one of the hottest industries over the past few years, with LinkedIn ranking it as the most promising job in 2019, and with the ever increasing demand for data, the field is poised to stay in the spotlight for years to come. There are plenty of articles that highlight the skills data science requires, Coding being the primary one in many cases. Coding is undoubtedly a very important aspect, yet it is not the only one, nor is it the most important. The (over) emphasis of learning to code puts the cart before the horse. What’s absolutely essential is possessing domain-specific knowledge as well as technical expertise.
There’s More to It than Just Coding
Learning/knowing how to code won’t by itself land you a job in Data Science. A bank, for example, looking to hire a bilingual associate, does not hire anyone off the street who knows Japanese or French. The person must first and foremost possess knowledge of the finance domain. Knowing a language is just a way to communicate what you know. Coding is no different. With respect to data science, coding is simply a tool to help you convey and apply your domain-knowledge to a specific problem. To put it differently, it is a means to an end, not an end in itself. Knowing how the data behaves and what questions to ask of it -examples of domain knowledge— are instead the fundamental building blocks to success.
How to Acquire Domain Knowledge
One straightforward way to learn more about data science, statistics, etc. is pursuing a degree. This is typically the best signal to employers that you have the domain knowledge they’re looking for. At the same time, however, this route is the least accessible, especially in terms of cost. On the other hand, there are tons of free or low cost resources that can provide you with an excellent foundation in data science. Yet this method may not carry the same weight as a degree from a university, so what are you supposed to do?
So You’re Interested in Data Science: Show It!
What’s helped me land and do well in interviews is my past work on independent projects. These act as a showcase for my skills and understanding of key concepts. Additionally, it allows me to have in-depth discussions with my interviewer about projects, my knowledge of the subject, and my approach to problem-solving. Pursuing projects on your own time gives you the opportunity to broadcast what you know (on Medium, LinkedIn, GitHub, etc.) and signal to employers that you mean business. By the way, listing articles I’ve published and links to projects I’ve worked on at the top of my resume has really helped me stand out.
Circling Back to Coding:
I might have been a little harsh on coding at the beginning of the article. In no way did I mean to diminish the importance of it – trust me, knowing how to code is a must! The best and fastest way to become more familiar with coding is coming up with a project to apply it to. Do you send the same email at the same time every week? Do you generate the same financial report for your boss every month? Learning how to code this way is far less daunting, than starting from ground-zero and printing hello world, because you’re focused on a very specific task. Plus, it makes the experience more engaging and fun, since you’re solving a real-world problem.
Final Thoughts
Only focusing on one skill is not enough to become a successful data scientist. A data scientist is required to wear many hats, being highly proficient in each area. Acquiring these competencies requires you to focus on multiple areas including technical, knowledge of the business domain, and interpersonal and communication skills. As discussed in this article, dedicating time to work on and share independent projects is a perfect way to hone all of these competencies. Here is an excellent article to help you get your projects off the ground.
