Myths about Data Science Debunked!

Becoming a data scientist is complex. Not only do you need to learn mathematics, statistics, and programming, but you also have to constantly battle the myths you hear around you. These misconceptions create a sense among people that only geniuses can work in data science, which is simply not true.

1. It is mandatory to have a Ph.D. to become a data scientist
In data science, there are two types of people. A) researchers who conduct research B) those who apply already devised algorithms in practice. The second type does not require a Ph.D. at all. What matters more here is practical experience. Today, there are many books and courses available where you can learn the theory from the comfort of your home, and no degree is necessary for top-notch work. A case in point is Greg Brockman, co-founder and president of OpenAI, who does not hold any university degree.

2. You will utilise all your experiences from previous jobs in data science.
That would be nice, but no. If you worked as a tester for five years before starting in data science, you won't leverage your previous experience much and will be starting almost from scratch. It’s a different story if you are working in the same domain of data science – for example, in banking. You can certainly make use of your knowledge of the environment. Personally, before entering data science, I worked in IT analysis, architecture, and project management. Here, I would argue with the author and believe that most of these experiences can be smoothly transferred into data science.

3. You need to have a solid understanding of mathematics, statistics, and be an amazing developer.
If you know these things, you'll have an advantage, but it's not essential. What’s important is the willingness to learn new things. In the resources, there are links to stories such as “From Paper Supplier to Lead Engineer”, “How I Became an ML Expert in 10 Months”, or “An Inspirational Story of a Non-Programmer Who Became a Top Competitor on Kaggle”.

You can find more debunked myths in the original article.

Sources: https://www.analyticsvidhya.com/…/myths-data-science-tran…/…

https://www.analyticsvidhya.com/…/exclusive-interview-with…/

https://www.analyticsvidhya.com/…/mystory-became-a-machine…/

https://www.analyticsvidhya.com/…/datahack-radio-episode-3…/