My Journey Into Data Science (And How You Can Start Yours Too)

Data Science Insights

The Beginning – Lost in a Sea of Tabs

I still remember the first time I decided I wanted to “learn data science.”

It sounded so exciting — like unlocking a new superpower. But as soon as I started, I found myself drowning in open browser tabs. “Learn Python in 30 Days!” “Master Machine Learning Fast!” “Become a Data Scientist in a Week!”Each tutorial promised to make me an expert overnight. Instead of feeling smart, I felt… overwhelmed.

I wasn’t sure where to start. Should I learn R? Python? SQL? Or should I jump straight into machine learning? I even wondered if I had made the right choice pursuing this path at all.

Finding My Footing

But I didn’t give up.

I’ve always loved solving problems, and my academic path reflected that — I earned a university rank in my B.Tech and later, a gold medal in my M.Tech in Computer Science and Engineering. Even with that foundation, the world of data science felt new and intimidating.

The breakthrough came when I decided to focus on one thing first: Python.

Why Python? Because it wasn’t just for programmers — it felt like a language designed for humans. Clean, simple, and powerful. I could write a few lines of code and actually see something happen. That was the moment I felt a spark — I can do this.

The Struggles (and Little Victories)

Of course, it wasn’t all smooth sailing.

There were nights when I stared at error messages for hours. There were days I installed the wrong library, broke my code, and felt like throwing my laptop out the window. But there were also moments that kept me going — the first time I plotted a simple graph in Matplotlib, the first time Pandas turned a messy dataset into something clean and understandable.

Those small wins built my confidence. Slowly, the puzzle pieces started to fit together.

Growing Into the Role

Step by step, my learning turned into something bigger. I started building projects. I worked on models. And eventually, I began working as a Data Scientist at EY — tackling projects from VAT prediction to building GenAI-powered tools and solving complex business problems.

I’m still learning every day. But here’s the truth: you don’t need to know everything to start. You just need to start — and keep moving.

Why I’m Writing This Blog

When I look back at my journey, I see how much time I wasted being confused, jumping between tutorials, and second-guessing myself.

I’m writing this blog because I want to make that journey easier for you.
I want you to have the roadmap I wish I had.

How You Can Start Too – Your First 5 Steps

If you’re standing where I once stood — curious, confused, maybe even a little intimidated — here’s what I would tell you today:

Step 1: Learn Python Basics
Start simple. Understand variables, loops, and functions. Write tiny scripts and watch them run.

Step 2: Learn to Work with Data
Get comfortable with libraries like Pandas and NumPy. Play with CSV files. Clean messy data.

Step 3: Visualize Your Data
Use Matplotlib or Seaborn to turn numbers into visuals. It makes data feel real.

Step 4: Learn Basic Statistics
Mean, median, correlation — just enough to make sense of your data.

Step 5: Build Mini Projects
Analyze your expenses, a Netflix dataset, or even weather data. Don’t wait to be an “expert.” Start small.

Closing – Let’s Begin This Journey Together

If you’re reading this, you’re already ahead of where I once was — because you’re starting with a guide I didn’t have.

This blog isn’t just about Python or data science. It’s about learning, failing, trying again, and building something meaningful step by step.

So, if you’ve been waiting for the right time to learn… this is it.
Let’s begin — together.