How to start your career in Data Science?

I can, I can, I can be a data scientist

“Hey bro, I am working in the field of Human Resource. Can I pursue my career in the field of Data Science or it is not for me?”

“Hello sir, I have 5 years of experience in software development, but I want to break into the path of data science. Is it still possible?”

“I have graduated recently sir and want to start my career in the field of data science. How to do it?”

And many more like this…

The questions might come from a diverse background, knocking on the door of data science, but the answer is straightforward. A BIG YES….

But HOW??

This article is all about answering this question.

But before answering this question, I will tell you the common mistakes which most of the data science freshers commit and regret later.

1. Deep diving into theory — It will not only slow you down, but you might lose interest in data science. In addition, you don’t remember deep theory for long unless you are not revising it regularly. So, you should create a balance between theory and practical.
2. Taking the complex popular problem at the beginning — “Facial recognition problem looks so cool, why not hit it first”. It will not only exhaust you but your naive approach without understanding fundamentals will make you out of focus. Hold your horses. Stay Calm and go for a systematic study. It will make your base stronger for complex problems.
3. Juggle between programming language — “Let’s learn Python as well as R. I heard both are in high demand in the job market”. It might be true. But if you put your legs on two boats, you will eventually fall. Even in the corporate world, data scientists generally focus on training themselves in one particular language and try to excel in it. Another language, if required, could be learned easily if one is an expert in one of the data science programming languages.
4. Who cares about Statistics — “Just code, why to study Statistics”. Without understanding the logic behind machine learning, simply coding will make you a developer, not a data science practitioner. The foundation of all machine learning lies here. Don’t underestimate the power of common statistics. It helps you understand…