One of the biggest myths about working in IT is that you must have a technical degree — computer science, engineering, mathematics, or something “serious.”
I used to believe that too.
But the truth is, I studied literature. I graduated from Polish philology, spent my early career years working in customer service, and today I work as a data analyst — helping others get into the world of data as well.
This is the story of how a humanities student with zero technical background ended up in IT. And more importantly, what I learned along the way.
Studying Literature, Not Databases
Let’s start at the beginning. I studied Polish Philology at the Jagiellonian University in Kraków — more specifically, comparative literature with an anthropological focus.
I was fascinated by stories, cultures, symbols, and how we humans make sense of the world through language. Not exactly SQL queries or dashboards, right?
Back then, I didn’t even know what Excel really did. I could open a spreadsheet, but that was about it.
Looking back, I can say this: your field of study doesn’t define your career path. Sure, studying computer science can help if you want to start as a data scientist right away. But if you don’t have that background, nothing’s lost. You can learn what you need later. What truly matters is curiosity and a way of thinking analytically about the world.
Erasmus, Portugal, and My First “Data Problem”
During my final year, I had the chance to go on an Erasmus exchange program in sunny Portugal. Those were some of the best months of my student life — new experiences, travel, people, and culture.
But something unexpected happened there. I ran into my first data problem.
It wasn’t work-related data, of course. It was personal — my finances.
I was planning a road trip from Portugal to Morocco with my roommate. I opened my bank account one day and realized… I didn’t have enough money. I couldn’t understand where it had gone.
After some thought (and a little shock), my roommate said: “You’re probably spending it all on cigarettes.”
I was stunned. I thought I smoked only occasionally — one cigarette every few days. In reality, I was burning through a pack every three days.
That moment stuck with me. I wasn’t tracking what I was spending or doing, and because of that, I had no control over my situation. It was the first time I realized how important it is to measure things — even something as simple as your spending habits.
The Excel Budget That Changed Everything
When I came back to Poland, I was working part-time at a hostel and didn’t earn much. Money was tight. I decided I needed to understand where it was going.
So, I opened Excel and started tracking my expenses. Every single one. Groceries, transport, cigarettes (which I secretly labeled as “chips” in case someone ever saw my file).
Then I added income. Then monthly balances. Eventually, I had a full personal budget built in Excel.
And that little spreadsheet completely changed how I thought.
For the first time, I could see how my decisions affected my financial situation. That awareness helped me quit smoking, save money, and start thinking more logically about life.
It wasn’t just about money. It was about the power of data.
“If It’s Not Measured, It Doesn’t Exist”
Around that time, I watched a TV interview where a guest said something that stuck with me for years:
“If it’s not measured, it doesn’t exist.”
I started applying this mindset to everything — my time, habits, and even physical activity.
I didn’t know it yet, but that was my first step into analytical thinking.
I wasn’t a data analyst by title, but I was already thinking like one.
Entering the Corporate World
After graduation, I decided to get a “real job.” I joined a big corporation — Capita — as a customer service agent. It was my first office job.
I had a contract, benefits, private healthcare — the whole corporate package.
But soon enough, I felt something was missing. Every day looked the same. I answered emails, picked up phone calls, and filled in reports. I overheard people saying things like “Oh no, it’s Monday again” or “Finally, Friday!”
I didn’t want to live for weekends. I wanted a job that made me feel curious and fulfilled.
At the time, I didn’t know what that job was. But I knew I needed to find it.
Finding Analytics Inside My Job
I started noticing something interesting. From time to time, I had to prepare small reports — maybe track performance, organize information, or check patterns in customer requests.
That’s where Excel came back into play.
I started building simple tools and templates to make our team’s work faster. I wasn’t doing it because someone told me to. I was doing it because I enjoyed the logic behind it.
Gradually, my daily tasks began to shift. I still did customer service, but I was also analyzing what was happening.
I was discovering analytics from the inside — without even realizing it.
That’s something I often tell people now:
You don’t have to quit your job to start learning data. Look for opportunities to analyze something in what you already do. Every company has data — even if it’s just an Excel sheet or a list of clients.
Automating the Boring Stuff
One day, a colleague joked:
“Excel is great, but emails don’t send themselves!”
And that got me thinking — what if they could?
I started experimenting with formulas, IF statements, and lookups to generate automatic email templates based on customer requests.
At first, it was clunky and slow. But it worked. Excel was literally writing my emails for me.
That experience taught me something crucial: automation starts with curiosity.
I wasn’t using any fancy tools yet, but I was learning the same logical patterns that real data analysts and engineers use every day.
The Turning Point: Choosing Data
After a few years, I realized that I didn’t just want to make my job easier. I wanted a completely different career.
I didn’t want to be a manager or a “senior customer service specialist.”
I wanted to work with data.
So I started studying seriously — in the evenings, after work. I took online courses on edX, mostly Excel and data analysis courses from Microsoft.
I didn’t jump straight into Python or SQL; I focused on building a solid foundation.
That’s the best advice I can give anyone switching careers:
Don’t start with the hardest thing. Start with the most useful thing.
First Job Interviews: Failure, Lessons, and Persistence
When I finally felt ready, I started applying for analytical positions.
My first big interview was with UBS. I had to solve Excel exercises, interpret data, and explain my thinking. I didn’t get the job — but for the first time, I felt I belonged in that world.
A few months later, I applied to Lufthansa.
The recruitment process was intense: time-limited tasks, ratio calculations, chart analysis, and even SQL logic tests. It was stressful, but exciting.
And this time, I got the offer.
That was the moment I officially became a data analyst.
Learning SQL and the First “Join”
On my first day in the new job, a colleague introduced me to SQL. I remember him showing me how to perform my first JOIN.
It felt like magic — combining two tables into one. Suddenly, data wasn’t just numbers; it was stories waiting to be connected.
I was far from perfect. I made plenty of mistakes. There were days when I felt completely lost. But I was learning fast, and most importantly — I loved it.
When I look back, that’s what mattered most: not how much I knew, but how much I wanted to understand.
Lessons Learned From Switching Careers
After years in the industry, I can summarize what I learned in four key points.
- Your degree doesn’t define you.
You can start from any background — humanities, business, or even art. What matters is how you think, not what your diploma says. - Find data where you are.
You don’t have to quit your current job. Look for patterns, problems, or numbers you can analyze right now. - Be patient.
You can’t switch to data analytics in a week. But in a year of consistent effort — yes, absolutely. - Luck matters, but preparation matters more.
I was lucky that someone gave me a chance. But that luck only worked because I was ready for it.
From Literature to Data — and Beyond
When people see my LinkedIn today, they see “Data Architect,” “Business Intelligence,” “KajoData,” and all the professional milestones.
What they don’t see is the guy who used to track cigarette expenses in Excel, or the student who didn’t know how to open a pivot table.
Every career starts somewhere humble.
Mine started with curiosity — and a spreadsheet.
If you’re reading this and wondering whether a humanities graduate can make it in IT, the answer is simple: yes, absolutely.
It’s not about your major. It’s about your mindset.
The world of data needs people who can think critically, communicate clearly, and connect the dots — and that’s exactly what humanities teach you to do.
Final Thoughts
The path from literature to data wasn’t easy, but it was worth every step.
I learned that success doesn’t come from having the perfect plan, but from taking one deliberate step after another — guided by curiosity, patience, and the desire to understand.
If this story resonated with you, share it.
Maybe someone out there — another literature student, teacher, or language lover — just needs to hear that they, too, can become a data analyst.
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!
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