In a world increasingly driven by data, many people ask themselves: “Where do I start if I want to get into data analysis?” And it makes perfect sense. On one hand, everyone says it’s a future-proof, well-paid, and interesting field. On the other – we feel like we need to know dozens of technologies, master Excel, SQL, Python, Power BI, and ideally something about machine learning, too.
Well… that’s not the best way to start. In this article, I’ll show you how to truly enter the world of data analysis in a way that keeps you motivated, helps you enjoy the process, and actually makes sense. I’ll share my approach – not based on technology, but on curiosity and practice.
Start with yourself, not with the tools
Too often, I get messages like: “Kajo, what should I learn first? Excel? SQL? Python?” And while these questions are completely understandable, the answer is not “Excel”. Or “SQL”. Or any specific tool.
Instead, I say: start with yourself. And I don’t mean that in a metaphorical way – I mean it literally. Start with the data that relates to you. Because that will be the most interesting to you. Because it will matter to you.
Do you have a household budget? Probably. Do you know how much you spend on groceries? Probably not. Do you have habits? Do you work out? Walk? Run? Try to drink more water? Perfect – all of that can be measured. And that’s data. Your data.
Why start with your own data?
Because it’s the best way to stay motivated. You don’t need to be a discipline master. You just need to analyze something that genuinely interests you. Suddenly, Excel isn’t boring anymore. Suddenly, tables make sense. Suddenly, you really want to know how much money you spent on Allegro last month.
And if you compare that to previous months – guess what? You just did a trend analysis.
You don’t need to start with linear regression. Start with what you already have.
Step One: Excel is a tool, not the goal
Many people believe data analysis starts with learning Excel. I believe that’s step two. Because first, you need something to analyze. And a reason to want to analyze it.
But once you’ve passed that first stage – the curiosity stage – it’s time for Excel. And you don’t need to master complex formulas. Just load your data and try to organize it.
For example: log your receipts from the past weeks. Group by “groceries,” “transportation,” “miscellaneous.” You start seeing where your money goes. Maybe you compare month to month? Maybe you create a simple chart?
At that point, Excel is no longer just a spreadsheet. It’s a tool that gives you answers.
Step Two: KPI – what are we actually measuring?
Once you have data, you need to ask yourself: what do I actually want to measure? This brings us to the world of KPIs – Key Performance Indicators.
This isn’t just corporate jargon. KPI simply means numbers that matter to you. If you want to lose weight, your KPI might be workouts per week. If you want to save money, maybe it’s the difference between income and expenses. Simple?
Well, not always. Because often, the KPIs we choose don’t tell us much. Like total sales – okay, but what does that mean? Is it from advertising? Better content? Discounts?
That’s why a good analyst isn’t someone who knows Excel. It’s someone who knows how to ask the right question and choose the right metric.
Step Three: Ask questions, not formulas
Contrary to popular belief, data analysis isn’t about typing formulas into Excel. It’s about asking questions. Like:
- Am I saving more than three months ago?
- Did I drink more water this week?
- Are my workouts consistent?
Only then comes the need to find data that can answer those questions. And only then do we look for tools to help.
Only when you know what you’re looking for – does the need for calculations arise.
Step Four: Play, explore, learn by doing
This is something I really want to emphasize. Learning data analysis shouldn’t be boring. It shouldn’t be a school checklist. It should be the joy of discovering.
Do something on your own. Make a mini-project. Gather data from three months of spending and try to predict the next month’s expenses. Or analyze your movement habits. You can measure almost anything that makes sense.
Don’t ask ChatGPT for the solution right away. Try figuring it out yourself first. That’s the only way to truly learn. And the only way to feel the satisfaction.
Step Five: How to learn and stay motivated?
I know that feeling when you start learning, everything is new and exciting, but after a few days, your motivation drops. That’s why you need two things:
- A concrete plan with micro-steps – not “learn Python,” but “write a simple script that summarizes my spending from a CSV file.”
- People around you doing the same thing – you don’t need a data club in your town. A digital community is enough to inspire you.
Without a plan, it’s easy to get lost. Without people – it’s easy to give up. Sometimes all it takes is listening to a podcast, watching videos, accessing a course, or joining a Discord server. Just be in the environment.
Technology? Yes, but later
Do you need to know SQL? Probably. Is Python worth learning? Definitely. Will Power BI help? A lot. But that’s not the starting point.
Technology is important, but it comes naturally once you’re truly interested in data. Because then learning Excel isn’t a chore – it’s a tool. Same with SQL, Python, or anything else.
First comes the question. Then the data. Then the attempt to answer. And only then, the technology.
Conclusion: What will you start with today?
Maybe you have a bank account from which you can download transaction history. Maybe you use an app to track your steps or calories. Or maybe you just have a piece of paper to log how many times you worked out this week.
That’s all data analysis. And it’s the perfect starting point.
You don’t have to be a math genius. You don’t need to know any programming language. You just need to start with yourself. And give yourself a chance.
And if you find it exciting – know that you’re in the right place. Because data analysis isn’t just a job. It’s a way of thinking. And a way of living.
Prefer to read in Polish? No problem!
Other interesting articles:
- What to Do with a Weak Resume? 5 Strategies That Actually Work
- The Modern Programmer – Interview with Kamil Brzeziński
- Tableau vs Power BI – My Detailed Comparison for Beginner Data Analysts
The article was written by Kajo Rudziński – analytical data architect, recognized expert in data analysis, creator of KajoData and polish community for analysts KajoDataSpace.
That’s all on this topic. Analyze in peace!
Did you like this article 🙂?
Share it on Social Media 📱
>>> You can share it on LinkedIn and show that you learn something new every day.
>>> You can throw it on Facebook – and perhaps help a friend of yours who is looking for this.
>>> And remember to bookmark this page, you never know if it won’t come handy in in the future.
You prefer to watch 📺 – no problem
>>> Subscribe and watch my English channel on YouTube.
