The titles overlap, and companies use them differently.
Direct answer
A data analyst typically cleans data, writes queries, builds reports and dashboards and explains what happened and why. A data scientist often builds statistical or machine learning models and runs experiments to predict or optimise. Both use SQL and Python, with scientists leaning more on statistics and modelling.
Comparison
| Data analyst | Data scientist | |
|---|---|---|
| Focus | reporting and insight | modelling and prediction |
| Core tools | SQL, spreadsheets, BI tools | Python, statistics, ML libraries |
| Output | dashboards, analyses | models, experiments |
| Entry path | common starting point | often after analyst or technical role |
Skills to build
- SQL.
- Spreadsheets.
- Python and pandas.
- Statistics.
- Communication.
Where Teyro fits
Teyro's Data Science for Beginners course turns this into short lessons you can play in a few minutes a day. It has 50 lessons, and you can start free.
The bottom line
Start with analysis skills. Add modelling as you grow. Check actual job descriptions in your area.
