I'm a data analyst who learns by doing – literally.
As a kinesthetic learner, I absorb knowledge best when I can physically interact with a task, break it down, and rebuild it from scratch. That’s why I approach learning through hands-on projects based on real-world data – I test, iterate, learn from mistakes, and keep moving forward.
My journey into data started with learning Python – I was especially drawn to topics like machine learning and artificial intelligence.
But the deeper I went, the more I realized the importance of the foundations: cleaning data, exploring patterns, visualizing results.
I understood that without strong analytical groundwork, there are no meaningful models – and this part of the process became my main focus.
On a daily basis, I work with Python, SQL, Excel, Power BI, and Tableau – choosing the tools based on the problem, not the other way around.
Tools I Use
- Python (Pandas, NumPy, Matplotlib, Seaborn): loading data from CSV and JSON files, cleaning, transforming, preparing for analysis, visualizing, and documenting the process in Jupyter Notebook while applying good coding practices.
- SQL: writing queries with SELECT, JOIN, GROUP BY, ORDER BY, WHERE, aggregate functions, CTEs, subqueries, and window functions.
- Excel: advanced formulas (including nested and conditional), Power Query, pivot tables, simple dashboards, and data preparation.
- Power BI & Tableau: building interactive dashboards based on well-structured data models. I work with DAX, table relationships, sets, parameters, and multiple data sources. I focus on clarity, logic, and usability.
- GIT: version control, project organization, and tracking progress.
Beyond Data
After hours, I look at data less – and at two energetic boys much more.
I’m a mom of twins, and I have to admit – no course has ever taught me time management, adaptability, or pressure handling like they have.
Daily life with them taught me how to quickly adapt to changing conditions, stay calm when everything goes wrong, and find creative solutions when plans A, B, and C have already failed.
These skills translate directly to working with data – where flexibility, decision-making with incomplete information, and staying focused despite obstacles are just as important.
Licencje i certyfikaty
PyStart – Learning Python Programming
Dokodu sp. z o.o. | Issued maj 2025
PyStart Certificate – a comprehensive introduction to Python covering programming fundamentals, data handling, loops, functions, and basic automation. The course prepares for further learning in data analysis and applying Python in analytical work.