Dear friend,
I'm writing this from the local train to Mumbai, laptop balanced on my knees (yes, the WiFi calling app makes it possible), and I'm thinking about a conversation I had with someone in Kalyan last month. They asked me straight up: "Should I learn Python or Excel first?" Their eyes had that mixture of excitement and confusion that I recognize because I had it too, back in 2019.
Let me tell you what I wish someone had told me then — not the polished LinkedIn version, but the actual truth.
The Question Nobody Asks the Right Way
Here's the thing: when people ask "Python or Excel first?", they're really asking a different question. They're asking "What's faster?" or "What looks more impressive?" or "What will get me a better job?" And honestly? The answer to all three is not the one you think.
The real question should be: What problem are you trying to solve right now?
I learned this the hard way. When I joined Morningstar in 2021 after my M.A. in Economics, I was convinced that jumping straight to Python would make me look serious. Sophisticated. Like I understood "real programming." But in my first week, I got handed a spreadsheet with 50,000 rows of mutual fund data, and my Python knowledge became as useful as a ₹10 note in a vending machine.
My senior — a guy named Vikram who's been in this industry for 12 years — watched me struggle for two hours with a VLOOKUP problem and said something I've never forgotten: "Master the tool in front of you before you build the next one." He was right. Not because he was older (age means nothing in tech), but because he understood workflow.
Why Excel First Actually Makes Sense (Even If It Feels Boring)
The Unsexy Truth About Starting with Excel
Let me be brutally honest: Excel is not cool. It's not what you see on LinkedIn posts. It's not what gets shared on Twitter. But it's also the gateway drug to data work, and I mean that in the best possible way.
When you work with Excel first, you're not just learning a tool. You're learning how to think about data. You're learning:
- Data structure: Why rows and columns matter. Why duplicate data is a nightmare. Why you should freeze headers. These aren't Excel concepts — they're data concepts.
- Logic: A VLOOKUP formula teaches you about relationships between datasets. A nested IF teaches you conditional thinking. A pivot table teaches you aggregation and summarization. These are programming concepts wearing a spreadsheet costume.
- The problem-solving instinct: When you solve something in Excel, you see the result immediately. Copy a formula down 10,000 rows? You can verify it worked in seconds. This feedback loop is powerful for learning.
I spent my first three months at Morningstar writing Excel formulas. SUMIFS. INDEX-MATCH combinations. Conditional formatting. Data validation. I thought I was wasting time. I was actually building the foundation for everything that came after.
The Career Reality (That Nobody Discusses)
Here's what surprised me: even now, two years later, I probably spend 60% of my working week in Excel. Not because it's the best tool. But because it's the shared language of business in India.
When I prepare a report for stakeholders at Morningstar, I don't send them a Python script. I send them an Excel file with formatted tables, charts, and clear logic. When a colleague wants to understand my analysis, they open Excel — not Jupyter notebooks.
In Indian companies, Excel literacy is still a superpower. It's not sexy. It won't get you featured in a tech magazine. But it'll get you hired, trusted, and promoted faster than you'd expect.
When and Why Python Becomes Essential
The Breaking Point: When Excel Stops Working
Around month four of my job, I hit a wall. I had a dataset of 2 million rows. Excel couldn't handle it efficiently. We were manually running reports every week that took 45 minutes each, and if someone spotted an error, we'd have to start over.
That's when Python stopped being theoretical and became necessary.
Python became essential because it solved problems that Excel couldn't:
- Scale: Millions of rows, multiple files, complex transformations — Excel struggles.
- Automation: Once a Python script is written, it runs the same way every single time. No accidental formula changes. No clicking mistakes.
- Integration: I could pull data directly from APIs (like Zerodha or Groww's market data), clean it, transform it, and output it — all without touching Excel.
- Reproducibility: A Python script is version control friendly. I could track changes, roll back if needed, and collaborate with teammates.
The shift from Excel to Python wasn't a replacement. It was an expansion.
The Learning Path That Actually Worked
This is crucial: I didn't learn Python in a vacuum. I learned it to solve specific problems I'd already solved in Excel.
My first Python script wasn't a "Hello World" program. It was a rewrite of a VLOOKUP + SUMIFS combo that I'd built in Excel. I already knew what I wanted to do. Python was just a new way to do it.
This matters because:
You learn faster when you're not learning new logic AND new syntax at the same time. Once you know Excel, Python's logic is just sitting on top of what you already understand. The learning curve becomes manageable instead of vertical.
I spent three weeks learning Python basics (loops, conditions, functions). Then I spent two days adapting a complex Excel workflow to Python. By day three, I was faster in Python than I'd ever been in Excel for that specific task.
The Comparison That Matters
| Aspect | Excel | Python |
|---|---|---|
| Learning Time (to competency) | 3-4 weeks | 8-12 weeks (if you know Excel) |
| Job-readiness timeline | 2-3 months | 4-6 months |
| Demand in Indian companies | 99% (universal) | 70% (depends on company size) |
| Complexity ceiling | ~100K rows efficiently | Millions of rows |
| Real-world usage frequency | Daily (80%+ of analysts) | 3-5x per week (data roles) |
| Best for beginners? | YES | No (comes after Excel) |
| Debugging difficulty | Visual, immediate | Requires problem-solving skills |
My Perspective
I need to be honest about something: in my M.A. Economics program at Kalyan, I learned econometrics using STATA and R. Our professor — Dr. Kulkarni — used to say something that stuck with me: "The tool is not the skill. The thinking is the skill. The tool just executes the thinking."
It took me three years of actual work to understand what he meant.
I used to believe that learning the "harder" tool first would make me smarter. It won't. I was wrong about that. What actually made me better was learning to think systematically about data problems first (in Excel), then learning multiple languages to solve those problems (Python, SQL, eventually R).
The thing that surprised me most? Excel formulas taught me more about logical thinking than my first month of Python ever did. Because in Excel, if your logic is wrong, you see it immediately. In Python, you get an error message, and then you're stuck debugging.
I'd do it differently now? No. I'd do exactly the same thing. Master Excel. Build confidence. Solve real problems. Then learn Python to solve problems bigger than Excel can handle. That's not second-best. That's the optimal path.
Final Thoughts
Listen, I know the pressure you might feel to jump straight into Python. LinkedIn posts make it sound like Excel is for "business people" and Python is for "real data scientists." That's nonsense. The most effective people I know at Morningstar aren't the ones who jumped to Python first. They're the ones who built unshakeable Excel fundamentals, then expanded into Python when they needed to.
Start with Excel. Spend real time with it — not a weekend course, but actual weeks. Write complex formulas. Build models. Break things and fix them. When you can't move forward because Excel is too slow or too rigid, that's when Python becomes obvious.
You'll get there faster than you think. And more importantly, you'll understand what you're doing instead of just running tutorials.
The commute to Mumbai is long enough to learn either one. Make it count by picking the right starting point.
You've got this.
— Dattatray
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 • 07 July 2026
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