Data analysis & Statistics
Data rarely speaks for itself. It becomes useful through structure, comparison, interpretation and context.
This page is therefore not just about methods, but also about how data is gathered, cleaned and understood in the first place.
From raw values to useful conclusions
Between a measured value and a meaningful conclusion lie several steps: collection, cleaning, structure, feature selection and an evaluation that does not distort the context.
Statistics as a tool
To me, statistics is less a world of formulas than a practical tool for understanding uncertainty, distributions, frequencies and relationships.
Structure before visualisation
Charts and dashboards can be useful, but they do not replace a sound data foundation. Categories, periods and relationships need to be clear before visualisation becomes meaningful.
Tools & mindset
Practical work may involve spreadsheets, databases, SQL queries, small programs or more specialised tools. Depending on the question, that can also include R with RStudio or KNIME.
What matters most, however, is not the tool itself but the ability to organise data clearly, build understandable analyses and assess results critically.
Notes & entry points
- Clean data before interpreting it
- Treat statistics as a practical tool
- Use visualisation only once structure is clear