Where this subject can lead
Knowledge of data science can be relevant to data analysis, analytics engineering and research support. These are directions to investigate, not promises of employment or professional eligibility. Entry requirements vary by employer, country and role.
Translate learning into evidence
A portfolio can show how you apply statistical inference and visual analytics to a practical question. Include the problem, your contribution, the method and the limitations. Explain technical choices in language a non-specialist colleague can understand.
Analyse an open dataset with missing values and communicate the uncertainty of the conclusions. This project can become a portfolio example if the work is your own and permissions allow sharing.
Investigate roles carefully
Read several current role descriptions and compare the skills they request. Distinguish essential qualifications from preferred experience. Check official professional bodies for regulated occupations and verify whether a particular qualification meets their requirements.
Plan a next step
Choose one capability from data science to strengthen over the next month. Create a small piece of work, request specific feedback and revise it. Use that experience to decide whether to pursue further study, supervised practice or a related role.