Five-stage roadmap
Data Analytics & BI
You do not need to collect next-stage tools before you are ready. Sequence does not slow you down; it removes random detours.
- 1 · FoundationBuild the vocabulary and problem-solving habits the rest of the path assumes.
- 2 · CoreLearn the concepts that make this career distinct from simply writing code.
- 3 · PracticalMeet the inconvenient parts tutorials often skip: setup, failure, feedback and deployment.
- 4 · Professional EngineeringMake your work readable, testable, reviewable and safe for another person to depend on.
- 5 · Later / SpecializationChoose depth because a real project or question now demands it—not because a roadmap poster had empty boxes.
Roadmap stage
1 · Foundation
Build the vocabulary and problem-solving habits the rest of the path assumes.
What to learn
- Spreadsheet reasoning, SQL, descriptive statistics and Git basics.
- Tables, joins, aggregation, missing data and data types.
- Questions, decisions, measures and ethical handling of data.
Enough for now
You can distinguish a question, metric definition, calculation and interpretation—and explain where a number could mislead.
Practical task
Define three metrics for a fictional orders table, validate its rows and calculate each result twice using two simple methods.
Roadmap stage
2 · Core
Learn the concepts that make this career distinct from simply writing code.
What to learn
- Metric definitions, grain, cohorts and comparison baselines.
- Data cleaning, validation and reproducible analysis.
- Visual encoding, accessibility and explanatory writing.
Enough for now
You can explain the main trade-offs and solve a bounded problem without a recipe.
Practical task
Rebuild a familiar exercise with one changed constraint and document your reasoning.
Roadmap stage
3 · Practical
Meet the inconvenient parts tutorials often skip: setup, failure, feedback and deployment.
What to learn
- Answer one decision question from a small documented dataset.
- Create checks for duplicates, missing values and impossible ranges.
- Present the finding, caveat and next question in one page.
Enough for now
You have one small, working artifact and can demonstrate a failure you found and fixed.
Practical task
Complete the linked career experiment, then improve it after one deliberate failure or review.
Roadmap stage
4 · Professional Engineering
Make your work readable, testable, reviewable and safe for another person to depend on.
What to learn
- Versioned metric definitions, query review and reproducible data checks.
- Accessible dashboards, decision context and uncertainty communication.
- Privacy-aware access, data lineage and monitored refresh quality.
Enough for now
A reviewer can trace each visible number to its definition and source, reproduce it and understand its caveats.
Practical task
Publish a small dashboard with a metric dictionary, validation log and refresh note. Ask a peer to reproduce one number and state the decision it supports.
Roadmap stage
5 · Later / Specialization
Choose depth because a real project or question now demands it—not because a roadmap poster had empty boxes.
What to learn
- Experiment design, causal inference and forecasting.
- Semantic layers, governed metrics and analytics engineering.
- Domain depth in product, finance, operations or public policy.
Enough for now
Choose one depth area, state why it matters now and produce one evidence-backed result.
Practical task
Write a one-page depth plan tied to a real system, research question or role requirement.
From skills to a destination
Where could you take this?
The career roadmap explains what to learn. A goal guide helps you put those skills to work. Start with one—not every route at once.
You read the roadmap—now gather evidence