In this exciting era of AI, I rather classify myself as a practical data enthusiast with deep roots in ETL, data modelling, harmonization, analytics, and visualization. My favorite schema is Star, though a Snowflake can cheer me up too.
Before reaching the Star, I leverage Python and SQL. Pandas handles the basics; SciPy and Scikit-learn take over for clustering and statistical modelling. Matplotlib, Seaborn, and Plotly fill in the narrative.
When stories grow bigger, I reach for self-assembled dashboards, Apache Superset, or Power BI. Learning the basic concepts of DAX and M was a long run — but the joy of using measures as dynamic parameters makes every second worth it. On the other hand, some semi-vibe scripting brings reliable results too.
Maps have always been my anchor. Whether exploring multiple data layers in QGIS or computing intersections between layers with different SRIDs via PostGIS, I am grateful for open-source GIS communities every day. I must note that the open-source data analytics and GIS community has brought so many new tools in the last two years — or even months — that staying up to date with the latest toys is becoming a challenge. But the time invested is worth it.
Teaching Basic Statistics and Destination Management at TUKE reinforced that Excel and QGis, taught well, can help even the least technical learners overcome their fear of data and analysis. That conviction now drives my latest work — the design, development, and delivery of three new courses at TUKE: Open Data and Reporting in Tourism, Open Spatial Data, and Data Visualization in Tourism — all built to help raise a generation without fear of data-driven
