# Getting rid of your fear of mathematics for Data Science

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Often at times, data science aspirants face difficulty learning or understanding the mathematics behind algorithms when starting out their data science journey. Hear Pragyan (a data strategy consultant at Kharpann) shed some light on the topic.

Well, the straight-forward answer is to start learning and practicing mathematics. However, I’m sure that isn’t the answer you’re looking for. So, let me share a trick that might actually be useful for you.

When I started out in my data science journey, I was oblivious of the fact that data science required a good amount of mathematical knowledge. Therefore, I didn’t even care about the math that was written in the books or in the online courses I took. I was much more driven by the projects I wanted to build and it was much later in my experience as a real-world data scientist that I started to take mathematics seriously.

Luckily, today, there are a lot of online courses and platforms that teach you the algorithms and principles behind various data science processes without ever showing you the mathematical complexities. This has made it a lot easier for people with non-STEM backgrounds to dive into data science and so, you can follow a similar pattern. Once you get a hold of how data science algorithms work, you can now take steps towards getting rid of your fear of the underlying mathematics.

For this, I advise you to select an algorithm that you know conceptually very well such as Decision Trees or Linear Regression, and so on. Then, take your time to research and go through how these algorithms work mathematically. Doing so will help you understand the meaning behind the symbols and expressions that build up the algorithms and how a single hyperparameter tweak can affect the final result. This will fuel you with the confidence to take on the task of demystifying the mathematics behind similar algorithms.

And, by the time you get used to this, you would have already fallen in love with how mathematics plays a vital role in making data science so intriguing.

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