
Tech track · 8 steps · 87 min
Data Scientist
“I want to become a data scientist”
Python, statistics, first models and honest evaluation, ending with a portfolio project you can show. It assumes you are comfortable with spreadsheets; if you are not yet, the Data Analyst track is the gentler start.
3–4 hours a week for 6 weeks
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The steps
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- 01What data scientists actually doExplain the job in plain words and tell it apart from analyst and ML engineer roles.8 min
- 02Python and notebooks for dataLoad a dataset into pandas in a free notebook and filter, group and join it.12 min
- 03Exploring data before you trust itRun a basic exploratory analysis and spot the problems that would mislead a model.10 min
- 04Probability and statistics for decisionsRead a confidence interval, a p-value and an A/B test result without overclaiming.12 min
- 05Your first modelsTrain and compare a baseline, a regression and a decision tree with scikit-learn.12 min
- 06Evaluating models honestlyChoose the right metric, cross-validate, and check a model for leakage and unfair errors.11 min
- 07Working with AI and LLMs as a data scientistUse AI assistants for code safely and decide when an LLM is the right tool for the job.10 min
- 08Communicating results and your first portfolio projectWrite up an analysis for a busy reader and publish one small, honest portfolio project.12 min
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Quiz and certificate
Data Scientist track quiz
Tick every step, then pass the 10-question quiz with 80% or more. The certificate comes with a link anyone can check.
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- Pass the track quiz with 80%
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Where to go next
Free, reputable resources for going deeper. They open on other sites.
- Kaggle Learn: free micro-courses in Python, pandas and machine learningkaggle.com
- Python Data Science Handbook (free online)jakevdp.github.io
- An Introduction to Statistical Learning (free PDF)statlearning.com
- scikit-learn user guidescikit-learn.org
- Khan Academy: Statistics and probabilitykhanacademy.org