Role roadmaps / Data Scientist Role flowchart
Decide with data: experiments, models, and uncertainty. You still need SQL. You do not need a new architecture every sprint.
Also called: Applied Scientist, Research Scientist (industry)
You ship: An experiment, a model with a holdout, and a decision memo — not a demo notebook.
3–6 months of focused practice beyond SQL
Roadmap Coding Interview Question bank Switch roleData Analyst BI Analyst Data Engineer Analytics Engineer Data Scientist ML Engineer MLOps Engineer AI Engineer Data Architect Nearby roles:Data Analyst , ML Engineer , AI Engineer
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Start · Data Scientist Foundations SQL for features Windows, point-in-time joins, and leakage as a join bug. NumPy / pandas fluency Vectorize, groupby, and why a loop over 10M rows is a career event. Inference, not vibes Hypothesis tests, confidence intervals, and power. p-hacking is an incident. Daily craft Question-first EDA Write the decision, then the plot. Segment before you model. Classical ML that ships Linear models, trees, calibration. Deep learning when the table is wrong. Experiment design Guardrails, SRM, and CUPED as vocabulary. “We launched and revenue went up” is not science. How you ship Evaluation without self-deception Holdout, leakage, and a metric the business already uses. Handoff to engineering Features as tables, not pickle files. Online/offline parity. Career moves DS interview SQL, a modeling case, stats, and a take-home. Talk leakage out loud. Next role ML engineer to serve. Analyst lead to decide without models. DE if you fell in love with pipelines. Keep practicing Study this node
SQL for features Windows, point-in-time joins, and leakage as a join bug.
Windows, point-in-time joins, and leakage as a join bug.
Feature SQL: windows, point-in-time joins. Leakage is a join bug.
Time to learn Until leakage looks like a join bug.
Prerequisites Windows Point-in-time joins What you need SQL + Python pads A labeled dataset with as-of timestamps Concepts to study Point-in-time (as-of) joins Entity-timestamp grain of a feature table Label leakage via future aggregates Train/serve feature parity starts in SQL Tech stacks Warehouse SQL — Features as tablesPython after the extract — Not instead of SQLHow to study Write a feature that uses only data known at as_of_ts. SQL + Python problems; windows track. What to produce A PIT join A leak you can point at in SQL Pitfalls Joining today’s label to yesterday’s feature without a time predicate Notebook joins that cannot be reproduced Interview prompts This AUC looks perfect. Where is the leak in the join? Links
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