Road Mates EnterHow to become a Data Scientist, phase by phase: what you do in each one, how long it takes, and the proof that shows you're done.
6 phases2–3 years9 resources
Working through structured courses and exercises in Python, pandas, SQL, probability, and inferential statistics. By the end, the person can load, clean, query, and summarize a dataset and explain hypothesis tests and distributions.
You're done when you can show
A GitHub repo of completed exercise notebooks covering pandas, SQL, and statistical tests.
Resources
Stops following tutorials and starts picking messy real-world datasets, framing questions, cleaning data, visualizing, and writing up findings. Produces several self-directed analyses that tell a clear story from raw data to conclusion.
You're done when you can show
A portfolio link with 2–3 written analyses on real public datasets.
Resources
Shifts from descriptive analysis to predictive work: feature engineering, regression, tree-based models, cross-validation, and metric selection, often via Kaggle or a self-built problem. Ends with at least one project showing a model compared against baselines with honest evaluation.
You're done when you can show
A repo or Kaggle notebook showing a validated model beating a baseline with documented metrics.
Resources
Moves from notebooks to production-style work: packaging a model or analysis into an API, dashboard, or app with reproducible pipelines and version control. Proves the person can deliver something others can actually use.
You're done when you can show
A live URL to a deployed model API, dashboard, or app backed by a public repo.
Resources
Stops building new projects and focuses on tailoring the resume and portfolio, networking, practicing SQL and stats interview questions, case studies, and take-home assignments. Ends with a signed offer for a data scientist or closely related analytics role.
You're done when you can show
A signed offer letter for a data science or analytics position.
Resources
Works on real company problems: scoping ambiguous questions with stakeholders, running analyses and experiments, and shipping models into decision-making. By the end, the person owns projects that measurably changed a business metric or decision.
You're done when you can show
An internal project writeup or review showing a metric or decision your work changed.
Walk this road with people on it.
Track each phase, post your proofs, and get matched with people in the same phase.