Can I still become a data scientist or data analyst if I am not good at math?

One popular question that we always get asked is: “Dr. Lau, can I become a data scientist or data analyst if I am not good with math or statistics?”

Well, Dr. Lau’s reply is always yes you can. He added: “I am not good at math. I became a data scientist with logic and algorithms first. Then I picked up mathematics and statistics during my career.”

Hence, let’s find out the role of math and statistics in data science.

Mathematics is called the universal language of science. Scientists use a standardized vocabulary, grammar and syntax to communicate with each other. So that everybody can understand each other.

## Don’t do well in math in high school or dislike it? – you’re not alone

Most people didn’t learn mathematics well during their high school – because of the way math is taught.

We are always **told to memorize formulas**, equations like theorem Pythagoras, normal distributions, etc.

But does it really matter?

Let’s hear it from Dr Lau: “as an applied science person, what I don’t get is, how is this going to change my life, or is there any real world application?”

So even if you are not good at math right now, you can still begin data science and build up your mathematics knowledge.

Then start to uncover the joy of using them to solve real-world problems.

For example, referencing from the video above, Dr. Lau mentioned: “you can use the theorem Pythagoras, the one that I’ve mentioned earlier, to compare the similarity of two text documents – and then group these similar documents together.”

Or you can simply calculate the frequency of the words in your email. Then using probability theories to predict whether an incoming email is spam mail or not.

## Many people force themselves to learn math & statistics without a purpose

What we have seen is many people forcing themselves to learn math and stats before breaking into science. You don’t have to do that.

At the end of the day, knowing the minimum amount of math and statistics. So that you know how to pick the right model for different data types.

That my friends, it’s good enough to kickstart your data science career.

Which part of the math and stats is the one you like? Let us know in the comment section below!