base · analyst from scratch

Roadmap: 6 levels, every one measurable

no-code start skill track finish

Not “covered the topic” but “closed concrete criteria”. Expand a level to see the input, what you ship, the done-checklist and the practice buttons.

0

Just ask questions

a couple of evenings

Analytics in plain terms: a question to the data + an answer. No code — Google Sheets and any CSV.

tool Google Sheets dataset any CSV you like
what you do A Google Sheet with 5 of your own questions and answers (filter / pivot / 1 chart).
Ready for the next level when:
  • Loaded a CSV you find interesting into Google Sheets
  • Framed 5 of your own questions about the data
  • Answered at least 3 with filters and sorting
  • Built 1 pivot table
  • Made your first chart
1

SQL through your own questions

2–4 weeks

SQL as a language of questions to data. No cramming ahead — you google exactly the piece you need for the question.

tool DuckDB · zero install dataset any dataset you like
what you do queries.sql — 10+ queries on data you chose, with a short takeaway for each.
Ready for the next level when:
  • Loaded a CSV into DuckDB in one line (read_csv_auto)
  • Wrote SELECT … WHERE … for a concrete question of yours
  • Computed an aggregate with GROUP BY
  • JOINed two tables and got a meaningful result
  • Answered 10+ of your own questions with your queries
2

Python: pull data yourself

3–5 weeks

Collect data from the web (APIs), clean it, compute. Python + pandas in Colab.

tool Python + pandas · Colab dataset any free API you like
what you do A Colab notebook: a dataset collected from an API + a mini-EDA with takeaways.
Ready for the next level when:
  • Called a free API and got JSON back
  • Built a pandas table from the response
  • Cleaned the data: types, missing values, duplicates
  • Computed 3+ aggregates/groupings in pandas
  • Made 2 charts and wrote down takeaways
3

Think like a product analyst

3–5 weeks

Metrics, funnels, cohorts, retention, A/B basics. A BI dashboard.

tool SQL + Metabase dataset a product dataset of your choice (Olist, your own event stream…)
what you do A dashboard of product metrics + a hypothesis you tested.
Ready for the next level when:
  • Computed key metrics: revenue, MoM growth, average order value
  • Built a funnel: order → payment → delivery
  • Made a cohort or retention by user
  • Assembled a dashboard in BI (Metabase)
  • Framed and tested 1 product hypothesis
4

A real end-to-end pet project

3–6 weeks

One story “like at work”: question → data → cleaning → analysis → dashboard → README.

tool everything above + Git/GitHub dataset your choice
what you do A GitHub repo: data → cleaning → analysis → dashboard → README.
Ready for the next level when:
  • Picked a question and a data source
  • Took the pipeline from raw data to conclusions
  • Built a dashboard or visualization of the result
  • Wrote a clear README with conclusions
  • Published the project on GitHub
5

Real experience & competitiveness

track finish

2–3 finished projects, a portfolio, confidence in interviews.

what you do A portfolio: 2–3 finished projects + your interview story.

Finish: you take any raw data and drive it to a conclusion. Then you pick a branch.

  • 2–3 finished projects on different topics
  • Each has a README and business-facing conclusions
  • Assembled a portfolio (GitHub or a site)
  • Passed a mock interview task
  • Landed freelance/internship or an offer