How learning to think with data launched a career in Analytics

Get valuable insights and hard-won lessons from our Talent Spotlight: Daniel Čulák

Data Science graduate Daniel Čulák discovered early that data tells a story. Through hands-on experience with SAS, various projects, and a passion for analytical thinking, he transformed classroom knowledge into a successful career in banking analytics. We spoke with him about his academic journey, his thesis experience, and the lessons he's learned along the way.

What did you study at university, and what inspired you to choose that field?

I studied the Data Science in Economics program at the Department of Statistics, Faculty of Economic Informatics at the University of Economics in Bratislava. What attracted me most was the versatility of the field. Data can be analyzed in almost any industry - from banking and finance to healthcare, sports, and marketing. The program prepared us for a profession rather than a specific job title, which I found very appealing.

From the beginning, the learning experience was highly practical. We worked with real-world data and learned how analytical insights can support important decisions in companies and institutions. Rather than focusing on memorization, our professors emphasized analytical thinking and understanding how data-driven decision-making works in practice.

As I gained experience, I began to see data analysis as a story. Every dataset presents a question, and every analysis uncovers part of the answer. That curiosity kept me engaged throughout my studies and made the learning journey both enjoyable and meaningful.

What led you to select your thesis topic, and why did you decide to use SAS?

When choosing my thesis topic, I wanted to work on something that offered both academic value and practical relevance. The project was developed in collaboration with a local company specializing in statistical studies, which gave me an opportunity to work on a real-world challenge rather than a purely theoretical one. I was also looking for a topic that had not been extensively explored before. I wanted to contribute something original while gaining experience that could be useful in my future career.

Using SAS was a natural choice. Throughout my studies, SAS was one of the primary analytical tools we worked with, and by that time I was already using it professionally as a data analyst. This allowed me to connect academic knowledge with practical experience and further strengthen skills that I knew would be valuable in my career.

How would you describe your experience with SAS?

My experience with SAS has been very positive. Because I was already using SAS in my professional role, the transition between university projects and real business tasks felt seamless. One aspect I particularly appreciated was the consistency between what I learned at university and what I applied at work. The knowledge flowed in both directions. Professional experience helped me during my studies, and academic concepts strengthened my work as an analyst.

At university, we also worked with Python and R, which allowed me to compare different analytical environments. While each tool has its strengths, I found SAS especially intuitive because of my practical experience with it. Over time, it became a natural and efficient part of my analytical workflow.

When did you graduate, and what are you doing now?

I graduated in May 2024 and continued working for the same company where I had been employed during my studies. Today, I work as a data analyst in the banking sector. My daily work involves SAS, Python, SQL, and a range of statistical methods, many of the same tools and concepts I learned at university.

In many ways, my career has matched what I hoped for when I began studying Data Science. I wanted to work in a field where I could apply my academic knowledge in practice, and that's exactly what happened. What surprised me most was that I remained with the same company after graduation. However, data analytics is a field where the work continuously evolves. Even within the same organization, new questions emerge, new patterns appear, and new insights can be discovered. That's what makes the profession so dynamic and rewarding.

Which skills from university have proven most valuable in your career?

The most valuable skills have been statistical thinking, analytical problem-solving, and the ability to work with tools such as SAS, Python, and SQL. Equally important is understanding which tool is best suited for a particular problem. Having experience with multiple technologies provides flexibility and allows analysts to choose the most effective approach.

Another critical skill is communication. Producing accurate analysis is only part of the job; explaining results clearly to different audiences is just as important. In practice, analysts often need to adapt their message depending on the audience's level of technical understanding and focus on the insights that matter most.

If I could suggest one area where universities could provide additional preparation, it would be business communication and presentation skills. However, I also recognize that this is something that develops significantly through professional experience.

What advice would you give to students preparing a thesis with SAS or conducting data-driven research?

Don't underestimate the value of simplicity. The principle of parsimony, often referred to as Occam's Razor, is highly relevant in data analysis. Simpler models are often more robust, easier to explain, and more practical to implement.
Be patient and trust the analytical process. Early results may seem confusing or incomplete, but as the analysis develops, the overall picture often becomes much clearer. Every step contributes to the final outcome.
Invest time in learning how to communicate your findings. Even the most sophisticated analysis has a limited impact if the results cannot be explained clearly and confidently to others.
Most importantly, don't become discouraged if your topic initially seems difficult, unclear, or insignificant. Some of the most meaningful insights emerge from questions that appear unremarkable at first.
Finally, don't judge SAS too quickly. At first glance, it may seem more structured than some open-source alternatives, but beneath the surface, it is an extremely powerful and versatile analytical platform. The deeper you explore its capabilities, the more value you'll discover.

Meet Daniel live

Want to hear more from Daniel?

Daniel will be one of the speakers at the upcoming SAS Finance Analytics School for Students, where he will join the Young Talent Panel, and he will share how he transitioned from studying Data Science to working as a data analyst in the banking sector.

Students interested in analytics, AI, and finance are very welcome to attend. Registration is free, so reserve your seat and join us online for an inspiring learning experience on June 23-25. Click here for more information and free registration.

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