We continue answering questions for the nontraditional data science job candidate who recently asked us about how to become a data scientist when their educational background was in marketing and social media.
In the previous article, we gave top down advice. That is, we started with answering the more holistic question about which hiring path to take as a nontraditional candidate - between the human resources hiring path and the hiring manager path, we suggested the hiring manager path.
One specific question the candidate had was Will they employable as a data scientist without a computer science or math degree?
The answer is Yes, they are employable as a data scientist without a computer science or math degree. There are many people working today as data scientists with degrees in hard sciences like physics, chemistry, astronomy, biology, and others. There are also many people working today as data scientists with degrees in soft sciences like psychology, sociology, political science and others. So yes, it is possible to work and get paid to do data science without a computer science or mathematics degree.
The key to getting employed as a data scientist is to show that you are able, capable of, and will be successful doing data science. Of course, data science means many very different things and skill sets to many different people. So you have to focus on being able, capable of, and ultimately being successful at doing the data science that your potential employer wants you to do.
There are two ways to show that you have the knowledge to do something:
With educational credentials, an educational institution be it a college, university, training program, MOOC, etc is essentially vouching for you that you have taken certain classes and learned certain things. As long as someone is able to look at what courses you took, they can have a relatively easy way to measure whether or not you know certain things. In the case of getting a data science job, they can easily check to see if you know the math and programming theory and techniques necessary to do their "flavor" of data science.
With knowledge work, it is much much harder to have someone be able to look at a project that you have done to measure whether or not you know certain things. In the case of getting a data science job, it is much more difficult for them to check to see if you know the math and programming theory and techniques necessary for their "flavor" of data science. This is why you'll often hear the advice that you should post your data science projects on GitHub, or on your blog, or LinkedIn, or other places to showcase data science projects. Because by posting your work, you can then talk about it and show that you actually do know the math and programming necessary to do data science.
Coming back to our friend, the nontraditional data science job candidate, their concern is that they would be negatively judged as not having the necessary skills because they didn't have the "right education." Which in this case was a degree either in mathematics or computer science. They are correct that they are going to be judged. However, the way they can fight it is by showing through projects and write-ups of those projects that they do have the math and programming theory and techniques necessary to do data science work.
So the answer we gave was that "Yes, you are employable as a data scientist without a computer science or mathematics degree. You'll just have to show that you know what you need to know for the jobs you want a different way. Which will probably entail doing projects and writing up those results."
Similarly, if you are a candidate with a nontraditional background, who is worried about now having the right educational credentials. As a first step, we suggest the following:
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