Data Science seems to be the new hot item that everyone wants to jump onto because of the job opportunities and high pay.
Those flashy, expensive online certificates makes it seem so easy for anyone to just hop into the data science bandwagon only to find out in the end that it’s still extremely difficult to get into.
We do need more data scientists in this world, especially with all the useful data available. But truth be told, even being in data science is exhausting to be at because there’s just so many things you have to keep up with.
In this article, I’ll share some of the key foundations that I think one should have and continuously learn in order to get in or thrive in this field. Hopefully this will help you navigate to better online courses that are more helpful in case you’re trying to get in this field.
Learn Statistics and Probability
The main core of deriving insights are from mathematical areas of statistics and probability. Some people would jump straight to learning a new programming language or tool that Data Scientists or Engineers use presuming that it is the key to getting into this field — but without mathematical knowledge and theory, it’s most likely impossible to thrive.
It’s a common misconception that you have to be great at programming to become a good data scientist. In fact, some of the best data scientists don’t even know how to code but have a very advanced level of statistics which is the mainstay of data science.
Learn Linear Algebra for Algorithm Development
Linear algebra powers various data science algorithms and models. Algorithms are the procedures that are implemented in code or any other tool and are run on data. Models are the output of these algorithms.
You can’t build a house without a strong foundation. Having a good grip on linear algebra is useful in solving different optimization problems and is behind all the powerful machine learning algorithms.