About five years ago, I was sitting in my first job at a Mumbai fintech startup, convinced that Excel was my golden ticket. I could pivot tables in my sleep. I wrote formulas that made senior analysts nod in approval. I was the guy who could whip up a dashboard in 20 minutes.
Then one day, my manager asked me to automate a process that was eating up 8 hours of my week. Three hours of Excel later, I'd created something fragile, error-prone, and essentially unmaintainable. A colleague suggested Python. I learned it in three weeks. The same task? Done in two hours. And it actually worked.
That moment changed how I think about skill-building. And if you're reading this as a 22-year-old analyst or a 28-year-old trying to level up in your tech or finance career, you're probably asking the same question I should have asked years ago: Which one do I actually need to learn first?
Let me be straight with you — the answer matters more than you think.
Why This Question Even Matters (and Why I Got It Wrong)
Here's the thing. Excel isn't just a tool in India — it's a cultural institution. Every startup, bank, consulting firm, and even most tech companies run on it. When I joined my first job, everyone was doing reporting in Excel. Client presentations? Excel. Budget forecasts? Excel. A/B test results? You guessed it.
So the logic seemed obvious: master the tool everyone uses, become indispensable.
But that's like saying, "Everyone drives a car, so I'll learn everything about cars instead of learning how to navigate." You're optimizing for the wrong thing.
Here's what I didn't understand back then: Excel and Python solve fundamentally different problems. Excel is great at showing you data that already exists. Python is great at handling data that doesn't, creating new insights from chaos, and doing things at scale.
And honestly? In 2024, with the kind of work most young professionals in India are doing — whether it's data analysis, automation, or building something with real impact — the order matters a lot.
Let's Talk About Excel (Because You'll Need It Either Way)
What Excel Actually Does Well
Excel is a communication tool first, a calculation tool second.
Think about it. When you're presenting quarterly performance numbers to your team, you're not showing them a Python script. You're showing them a spreadsheet. When your boss asks "Can you pull the numbers for Mumbai operations from the last six months?" — Excel. When a client needs to see how their ₹50 lakh investment performed, they're looking at an Excel file, not a Jupyter notebook.
This is why Excel is still, wildly, the most important tool in most Indian offices.
The other reason Excel wins at communication: everyone understands it. Your non-technical manager, your client, your colleague in HR — they can all open a spreadsheet and see what you've done. There's no gatekeeping. No setup required. Click the file, read the numbers.
I use Excel almost every day, even now. I track personal investments across Zerodha, CRED, and PhonePe using spreadsheets. I maintain a simple P&L tracker for freelance work. I build quick models to answer "what if" questions. Excel is muscle memory at this point.
Where Excel Actually Falls Apart
But here's where it breaks: the moment you need to do something *systematic*, Excel becomes a liability.
Imagine you're analyzing user behavior data from a SaaS product with 100,000 rows. Excel will slow down. Now imagine you need to do that analysis every week. You'll spend 30 minutes manually refreshing data, copying columns around, praying you don't mess up a formula. Six months later, you're drowning.
Or say you need to clean data — remove duplicates, standardize formats, merge datasets. In Excel, this is manual, error-prone work. Someone's going to miss something. Someone always does.
And the worst part? Excel doesn't scale. A dashboard that works for 10,000 rows might crash at 100,000. A process that took 2 minutes last month now takes 20 because the data grew. You can't version control it properly. You can't audit who changed what. You can't automate it cleanly.
Now, Python — This Is Where Your Real Leverage Lives
Why Python Changes the Game
Python is a programming language. Not a spreadsheet. Not a calculator. A proper programming language.
This means: you can automate things. You can process 10 million rows in minutes. You can build processes that run every morning at 6 AM without you touching anything. You can combine data from multiple sources — APIs, databases, CSVs, whatever — and blend them seamlessly. You can catch errors automatically. You can version control your work. You can collaborate without accidentally overwriting someone else's formulas.
And here's the thing that really sold me: Python is *readable*. If I write a Python script today, I can come back to it in a year and understand what I was trying to do. Try reading a complex Excel formula from 2023. It's like deciphering ancient Sanskrit.
I started learning Python out of necessity, not interest. But once I did, I realized it wasn't just "better" than Excel for big problems — it fundamentally changed how I approached problems.
Instead of "How do I calculate this in Excel?" I started asking "What process do I actually need to build here?" Those are two wildly different questions with wildly different answers.
The Real-World India Context
Here's something I notice: Python skills in India are still seen as slightly "techy." Like it's something engineers do, not analysts.
That's rapidly changing. Every data analyst job posting now asks for Python. Every FinTech company in Bangalore, Mumbai, and Delhi is looking for people who can Python. Banks are automating compliance reporting. Insurance companies are building data pipelines. Even old-school consulting firms are asking for it.
The salary jump is real too. A data analyst who knows Excel? You're competing with 10,000 people. An analyst who knows Python? You're in a much smaller pool. The ₹8-10 lakh role suddenly becomes a ₹12-15 lakh role just because you can automate things others can't.
And honestly? If you're 25 and learning a skill right now, you should be learning for the next 10 years, not the last 10.
So Which One Should You Learn First?
Here's my actual recommendation, and I'm going to be really specific because vague advice is useless.
If you're just starting out (fresh graduate, first job, no data background): Learn Excel first, but not deeply. Get comfortable with basic formulas, pivot tables, charts, and filtering in about 2-3 weeks. Just enough to be functional. Then move to Python. Don't get trapped trying to become an Excel master.
If you're already working in a data-adjacent role (analyst, operations, finance): Go straight to Python. You already use Excel daily. Spending months perfecting it is wasted time. Python will make you exponentially more valuable in half the time.
If you're switching careers into tech or data: Learn Python seriously. Excel can wait until you're on the job. Your job interview doesn't need to see Excel skills; it needs to see you can think like a programmer.
The nuance here matters: Excel is a *tool*, Python is a *skill*. Tools you learn on the job. Skills take time and you should invest in them properly.
| Aspect | Excel | Python |
|---|---|---|
| Learning Curve | Steep then flat. Easy start, hard to master. | Gentle throughout. Gets better as you practice. |
| Time to Competence | 2-3 weeks for basics; months for mastery | 3-4 weeks for basics; 3-6 months for real skill |
| Data Size Limit | Breaks around 1 million rows | Handles millions without flinching |
| Automation | Possible but clunky (macros, VBA) | Natural and elegant |
| Job Market (India) | Expected everywhere, not a differentiator | High demand, strong salary premium |
| Career Longevity | Stable but not growth-oriented | Opens doors across industries and roles |
Final Thoughts — Be Strategic About Your Time
Here's what I wish someone had told me when I was starting out: you don't have unlimited time to learn. Every hour you spend perfecting Excel is an hour you're not building the skill that will actually accelerate your career.
I'm not saying Excel is useless. I use it constantly. But I learned it contextually — by doing real work, not by taking a course. And that's probably how you'll learn it too, once you're on the job and actually need it.
Python, though? That requires deliberate practice. A good course. Building projects. Debugging. Getting frustrated and pushing through. The kind of investment that doesn't happen by accident.
If you're between ages 22-35 and thinking about skill development, I'm going to be bold and say: Python is the better investment. Not because it's cooler or more prestigious. But because it fundamentally expands what you can do. It makes you more valuable. It opens more doors.
Master Excel on the job. Build Python in your own time. That's the play.
And if you do learn Python and love it? There's so much more beyond that — data science, machine learning, building web apps. Excel doesn't lead anywhere. Python leads everywhere.
Start there.
Written by Dattatray Dagale • 10 May 2026
.png)
.png)
0 Comments