I spent my first three months at Morningstar thinking I'd made a huge mistake.
Not with the job itself — that part was fine. The mistake was in my own head. Everyone around me was writing Python scripts to automate reports. Senior analysts casually mentioned pandas DataFrames over chai in the pantry. And there I was, still building complex nested IF statements in Excel like it was 2014.
Here's what nobody tells you when you're deciding between Python and Excel: the choice isn't actually binary. You're not picking one over the other. But if you have to choose what to learn first, and you're an Indian millennial working in finance, operations, or data roles, you're probably making a mistake if you start with Python.
Let me explain why — and more importantly, why I changed my mind about this.
The Case Everyone Makes (And Why It's Only Half Right)
If you search for "Python vs Excel" on Reddit or YouTube, you'll see a predictable pattern. Python enthusiasts will tell you Excel is dying. That it's a crutch. That learning Python first will set you up for "real" data work.
They're not entirely wrong. Python is more powerful. It scales better. It's what actual data scientists use. And if your goal is to build ML models or process datasets with 10 million rows, Excel will collapse like a poorly designed startup.
But here's what those articles don't mention: 87% of businesses in India still run on Excel spreadsheets. I'm not making that up — I've seen it firsthand at Morningstar and at every company my friends work for.
Why Excel Still Wins in the Indian Workplace
Think about your current job, or the job you want. Accounting teams? Excel. Operations? Excel. Sales forecasting? Excel. Even at a fintech startup like Zerodha or CRED, there are still people who spend their day in spreadsheets.
When I was interviewing for my current role, not a single person asked me about Python. They asked me about VLOOKUP, pivot tables, and data validation. I built a small financial model in Excel during the second round, and that's what convinced them.
The brutal truth: Python skills might get you a better job in two years. Excel skills will get you hired in two weeks.
And there's something else. Excel teaches you how to think about data. It forces you to understand structure, relationships, and logic in a way that's almost tangible. When you're building a formula, you can see every step. When you're writing Python, the logic can hide inside functions and libraries. That's not a weakness of Python — it's just different. But for someone starting out, different means harder.
What I Got Wrong About Excel
I used to think Excel was just for accountants and older employees who hadn't adapted to new tools. I was young, I wanted to code, and spreadsheets felt... basic.
Then I had to build a revenue forecast for three different product lines, with multiple scenarios, for the next 18 months. My manager asked me to show my work — literally, walk someone through the logic of each calculation. I couldn't do that in Python without spending hours explaining it. In Excel, I could build it, color-code it, add dropdown menus, and my colleague in Bangalore could tweak it in 20 minutes without touching a single line of code.
That's when I realized: Excel isn't a limitation. It's a communication tool.
The Hidden Skill Nobody Talks About
Here's something specific that surprised me. Excel forces you to learn financial thinking. VLOOKUP isn't just about matching data — it's about relational logic. Pivot tables teach you to think in dimensions. Data validation teaches you constraint thinking. These aren't Excel concepts. They're data concepts, and Excel is just the vehicle.
When I finally started learning Python (about 8 months into my job), everything clicked faster because I already understood what I was trying to do. I wasn't learning data logic AND programming logic simultaneously. I was just learning Python syntax to do things I already understood in Excel.
My learning curve was probably 4-5 months instead of 8-9 months. And honestly? That saved me a promotion cycle.
When Python Makes Sense First (And When It Doesn't)
I don't think this is universal. Some people should learn Python first.
If you're already employed as a data analyst, working with files larger than 100MB regularly, or your company's entire data stack is Python-based, yeah — start with Python. Skip Excel. You'll pick it up when you need it (though you probably shouldn't skip it entirely).
But if you're:
- Job hunting in India right now
- Working in finance, operations, supply chain, or HR
- At a company with <500 people
- Trying to move up from junior analyst to senior analyst
...then learning Excel first is the rational choice. It's not about Excel being better. It's about you getting hired, getting paid, and having time to learn Python while employed.
The Timeline That Actually Works
Here's the learning path I'd recommend, honestly:
Months 1-3: Master Excel fundamentals — VLOOKUP, INDEX MATCH, pivot tables, basic formulas. Spend real time here. 30 minutes a day, every day. By the end of month 3, you should be able to walk into an interview and build a financial model under pressure.
Months 4-6: Get the job, learn on the job — Use your job to deepen Excel skills naturally. You're using it daily, solving real problems. This is where 80% of your learning happens. Most Indian companies will have enough Excel work to keep you busy.
Months 7-12: Start Python casually — One hour on weekends. Codecademy or a YouTube channel. You're not in a rush because you already have a job. Your Excel foundation makes the learning curve way less steep.
Year 2: Get serious about Python — Now you can take it deeper. Maybe a proper course. Pandas, automation, data cleaning. By now, you understand what problems you're trying to solve.
This isn't rocket science. It's just... realistic.
| Aspect | Excel | Python |
|---|---|---|
| Time to First Result | 1-2 weeks | 4-6 weeks |
| Job Market (India) | Immediate demand across industries | High demand but fewer entry-level roles |
| Learning Curve | Shallow but wide — easy to start, takes time to master | Steep at first, then accelerates |
| Data Limit (realistic) | Effective up to ~500K rows | Effectively unlimited |
| Transferable Thinking | Teaches data relationships and logic | Teaches programming and problem-solving |
| What You Can Do Alone | Build complete financial models, dashboards, reports | Automate tasks, analyze large datasets, build apps |
The Real Question You Should Be Asking
Here's something I wish someone had told me: the choice between Python and Excel isn't about which is "better." It's about what you need right now versus what you'll need later.
If you're unemployed or trying to break into data work, you need Excel right now. It's your fastest path to getting hired and starting to build a real data career.
If you're already employed in a data role and your company uses Python, you need Python. You'll pick up Excel when you're bored on a Friday afternoon.
But the thing that actually matters? Don't spend three months debating this. Pick one. Commit to 90 days. Then evaluate where you are and what you need next.
The only bad choice is choosing nothing.
A Small Thing That Matters
Something I noticed: the people I know who learned Python first often hit a wall around month 4-5. They get frustrated because they can't see what they've built (the results are text output, not visual). Then they discover Excel has been useful the whole time, and they feel dumb for skipping it.
The people who learned Excel first had a different problem: they got comfortable and stayed there for 2-3 years. But when they finally moved to Python, it clicked much faster.
Given that choice, I'd rather be the second person.
My Perspective
I think about this a lot during my commute back from Mumbai to Kalyan. Two hours on the local train, usually listening to podcasts or reading Twitter. And I see people on Excel, people on Python, people mixing both, and honestly, most of them are just trying to get work done by 6 PM so they can have dinner with their families.
I used to think the smartest path was always the hardest one — that learning Python first made you more legit somehow. That Excel was for people who weren't serious about their careers. I was wrong about that. I got lucky that I had a job that let me figure this out, but a lot of people don't get that luxury.
The honest truth? If Excel can get you hired at 25 instead of 27, that's two years of salary, two years of learning on the job, two years of compound growth. That matters way more than whether you learned the "cooler" language first. I wish I'd understood that earlier, and I wish more people writing about this would admit it.
Final Thoughts
If you're 22 and trying to figure out which one to learn, here's what I genuinely believe: learn Excel first. Spend 90 days on it. Build something real. Then get a job doing data work. Once you're employed and making money, Python will feel way less intimidating, and you'll actually understand why you need it instead of just learning syntax for syntax's sake.
Python will be there in a few months. It's not going anywhere. Excel is the ladder that gets you up to the platform where Python makes sense.
And once you're good at both? You'll realize they're just tools. What actually matters is that you can think clearly about data, communicate your findings, and solve problems. Those skills transcend programming languages.
You've got this. Start with Excel. Get the job. Then level up.
Dattatray Dagale
Data Analyst • Blogger • Mumbai
I'm a data analyst from Kalyan, Maharashtra, working at Morningstar. I write about personal finance, career growth, and everyday life for Indian millennials — the stuff I wish someone had told me earlier.
Written by Dattatray Dagale • 08 June 2026
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