How I Got into Data Science — And Why You Should Too
Back in 7th grade, I first heard the term “Data Science.” But like most of us at that age, I didn’t take much notice — I didn’t even Google it.
My real journey began elsewhere — I learned Meta Ads and later came up with the idea of starting a digital agency (that’s a story for another day).
It wasn’t until 10th grade that I stumbled upon Dr. Zeeshan Usmani, who is one of the best data scientists in the world. He made the subject seem vibrant — and for the very first time, I seriously considered data science. And you know what? I liked it.
Data science is all about employing data + math to address real-world issues — and that’s something I could envision myself doing.
What Is Data Science?
Ever wonder how Netflix knows exactly what you want to watch next?
Or how YouTube is suggesting videos that are just too relatable?
Or how Amazon is showing you what you need before you even know it?
That’s data science in action.
Data science is the backbone of contemporary decision-making — from product suggestions and ad targeting to fraud detection and even disease outbreak prediction. It’s where math, logic, and creativity converge to find patterns and solve problems.
A Brief History of Data Science
The history of data science stretches centuries back — to the 1600s, when statistics and probability were initially developed to study uncertainty.
Here’s a quick timeline of how data science came to life:
1600s–1900s: Math and statistics form the foundation.
1950s–1980s: Computers allow large-scale data processing; SQL and programming emerge.
1990s: Companies realize the value of storing and analyzing data.
2001: The term “Data Science” is officially introduced by William S. Cleveland, combining:
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Statistics
Computer Science
Data Visualization
Domain Knowledge
2010s: The Big Data era begins — tools like Python, Hadoop, and Tableau rise.
2020s–2025: Machine learning, AI, and automation enhance the value of data scientists.
Why Data Science Is AI-Proof
Folks worry AI will steal jobs — and they should. But data science is one of the few domains where AI doesn’t automate. it augments.
Here’s why:
Data scientists create and train AI.
AI can’t design itself — it requires human judgment, ethical thinking, and domain knowledge.
AI repeats tasks to free up data scientists’ time for strategy, creativity, and analysis.
In other words: AI will assist you, not replace you. Want to learn more about this? I strongly encourage you to take a short course on why data science is one of the safest professions currently.
How to Get Started (Even as a Student)
I’m taking the IBM Data Science Professional Certificate on Coursera — and it’s one of the best organized beginner courses (provides experience with projects and hands on lab).
Below are a few ways to get you started:
✅ Paid Course (Recommended)
Coursera — IBM Data Science
(Beginner to Professional)
link here
✅ Free Course
YouTube — Simplilearn Data Science Full Course
link here
You do not require a university degree or sophisticated setup. All you require is a laptop, internet, and some curiosity.
Last Thoughts: No Excuses
Today, it’s just an excuse to say “I don’t have enough resources.”
Whatever you need is out there — free or paid. You are the only one who is holding you back.
If you wish to have a future-proof career, start learning.
If you wish to stand out, create projects.
If you desire success, remain consistent.
I’ve done my part by pushing you. It’s now your turn to move.
