Let me start with a confession: I wasted three months learning Python before I even knew how to use VLOOKUP properly.
It's 2019, I'm sitting in my apartment in Bandra, scrolling through YouTube, and every data analyst on the internet is screaming "Learn Python! Python is the future! Excel is dead!" So naturally, I bought a course, opened VS Code, and started writing print statements like my life depended on it.
Meanwhile, my manager at the time asked me to pull together a financial analysis for a board meeting. Quick turnaround. I panicked, fumbled through it in Excel (badly), and somehow got through it. But here's what hit me: I could have done it in half the time if I'd actually known Excel inside out.
That was my wake-up call. And it's probably yours too, if you're reading this wondering whether to dive into Python or master Excel first.
The answer isn't straightforward. But I'm going to walk you through exactly what I learned, what I see working for people in India, and what'll actually matter for your career.
The Real Question You Should Be Asking
Here's the thing: everyone asks "Python or Excel?" like it's a binary choice. It's not. The real question is: What are you trying to accomplish right now?
If you're a finance analyst at a bank, a startup founder trying to understand unit economics, someone working at a fintech (think Zerodha, CRED, PhonePe) — you need Excel yesterday. Not tomorrow. Yesterday.
If you're trying to build machine learning models, automate data pipelines, or work with datasets so massive they'll crash Excel, then Python is your friend.
But here's the nuance nobody talks about: Excel gets you 80% of the way there in almost every analytical role. And that 80% will make you genuinely valuable to your team immediately.
Why Excel Wins the "Learning Speed" Game
Excel has a learning cliff, not a learning curve. You can pick it up in weeks. I'm not exaggerating.
Think about it. In Excel, you see your data. You can touch it, manipulate it, see results instantly. The feedback loop is immediate. You write a formula, hit Enter, boom — you see if it works. That's psychologically powerful when you're learning.
Python? You're debugging for hours wondering why your indentation is off or why your API call returned a 403 error. It's rewarding once it clicks, but that click takes time.
I've watched junior analysts at companies I've consulted with go from "I don't know Excel" to "I'm comfortable with pivot tables and basic formulas" in about three weeks. That's real.
Excel Makes You Instantly Useful
Here's what happened when I actually sat down and learned Excel properly: I became someone people asked for help. Not for deep insights or fancy models — just for "Can you pull this data together?" and "Can you fix this spreadsheet?"
That's currency in Indian offices. I'm serious. Whether it's a consulting firm, a bank, a startup, or even your family's trading portfolio — someone who can wrangle data in Excel becomes indispensable.
And the irony? Most senior people in finance, operations, and business roles are Excel powerhouses. They built their careers on spreadsheets before Python became mainstream. That skill never goes out of style.
When Python Actually Becomes Your Superpower
But let me be straight with you: Excel has limits. Real, hard limits.
If you're working with datasets over 100,000 rows, Excel starts wheezing. If you're automating the same data cleaning task every week, you're wasting your life doing it manually. If you're building predictive models or working with messy, unstructured data from APIs — you need Python.
I realized this when I moved into a data analytics role where I was pulling data from multiple sources (bank APIs, market data feeds, user behaviour logs) every single day. Excel couldn't stitch it together elegantly. Python could. One script, automated, scheduled to run every morning.
Suddenly my Excel skills became the foundation, and Python became the tool that multiplied my impact.
The Python Advantage for Modern Careers
Here's what Python gives you that Excel never will: automation at scale, machine learning, real data engineering work, and honestly? Better career trajectory in high-growth roles.
If you're thinking about roles at data-heavy companies (fintech, e-commerce, tech startups), you'll hit a ceiling without Python. Not because Python is glamorous, but because the actual problems these companies solve require it.
And the communities, resources, and job postings for Python are exponentially larger. LinkedIn search for "Python" in India right now and you'll see 50,000+ openings. Most of them pay better than pure Excel roles too.
But here's the catch: this only matters if you have the foundation.
The Time Investment Reality
Let's talk timeline because time is money, especially in your 20s and 30s.
Excel: 4-8 weeks to be genuinely useful. Maybe 3-4 months to be really strong.
Python: 3-4 months to write basic scripts. 6-12 months to be actually employed-level good. 2+ years to be truly skilled.
I'm not trying to discourage you. I'm being honest so you can plan correctly.
What I Actually Recommend (Based on Where You Are)
And honestly? I've thought about this a lot because I coach junior analysts and I see this question every week.
If You're in Finance, Operations, or Any Non-Tech Role
Learn Excel first. Seriously.
You'll see ROI in weeks. Your boss will notice. You'll get more interesting projects. You'll feel capable. These things matter psychologically, especially early in your career.
Then, after you're solid with Excel (let's say 2-3 months in), dabble with Python. Learn to write scripts that automate your Excel work. This is the dream combo — you understand the business logic in Excel, but you're smart enough to automate the boring parts.
If You're Trying to Break Into Tech or Data Engineering
Python first. But learn Excel alongside it as a secondary skill.
Why? Because even data engineers at major tech companies need to communicate with non-technical stakeholders through dashboards and reports. Excel is that bridge. Plus, when you eventually build data pipelines, someone's going to ask you to export something to a spreadsheet.
The sequence matters though: 2-3 months of structured Python learning, then spend weekends learning Excel. Don't split your focus 50-50.
If You're a Student or Between Jobs
This is where you get to play strategist.
Assess the job market in your target role. If it's anything to do with business, finance, operations — Excel. If it's engineering, analytics, data science — Python. Then learn the other one within 6 months.
The beautiful thing about being between jobs? You have time. Use it to build real projects, not just course certificates. Build an Excel model that analyzes stock picks from Zerodha. Write a Python script that scrapes real estate data and predicts prices. These matter more than any credential.
| Factor | Excel | Python |
|---|---|---|
| Time to Basic Competence | 4-8 weeks | 3-4 months |
| Immediate Career Impact | Very high | Low initially |
| Salary Multiplier | Modest (10-15%) | Significant (30-50%+) |
| Job Opportunities in India | Thousands | Tens of thousands |
| Skill Retention | Sticky | Requires regular use |
| Automation Capability | Limited | Unlimited |
| Best For | Business roles, quick wins | Tech roles, long-term growth |
The Honest Truth I Figured Out Too Late
You don't have to choose one forever. This isn't marriage.
I was thinking about it wrong. I thought mastering one meant abandoning the other. In reality, most of my successful colleagues know both, but they learned them at different times based on what their career needed.
My buddy who's now leading a data team at a fintech started with Excel at his bank job, then picked up Python when he needed to. My sister who works in operations at an e-commerce company is Excel-first and hasn't needed Python yet, but she might if she moves into analytics.
What matters is: what do you need right now? What will make you valuable to your team this quarter? That's your answer.
And then, once you're good at that, pick up the other one. The skills compound. Someone who knows both Excel and Python is genuinely rare in India, and they're worth a lot.
Final Thoughts
If I could talk to myself in 2019 when I was panicking in front of my laptop, I'd say this:
Excel first. Not because it's better or more important. But because it'll make you feel competent faster. You'll ship something your team uses. You'll get confidence. And confidence is what keeps you learning.
Then Python. Because that's where the exponential growth lives.
The real win isn't picking the "right" one. It's being willing to learn at all while most of your peers are still watching YouTube videos about how they should learn something.
You're here, thinking about this, asking the right questions. That's already ahead of the game. So pick one, commit for 8-12 weeks, and move forward. The other one will wait, and you'll be surprised how quickly you pick it up once you've got that first skill under your belt.
Good luck. Seriously. And if you're in Mumbai, let's grab coffee and I'll walk you through my learning resources. — DD
Written by Dattatray Dagale • 11 April 2026
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