Data Scientist Roadmap
Data Scientist Roadmap: From Statistics to Applied Machine Learning
Data science sits between statistics, programming, and domain knowledge. This roadmap builds the statistical and programming foundation first, since machine learning tools change faster than the math underneath them.
Foundations
6-10 weeks- Python fundamentals plus NumPy and pandas for data manipulation
- Statistics and probability: distributions, hypothesis testing, regression
- SQL fundamentals for pulling and shaping data
- Data visualization for exploratory analysis (matplotlib/seaborn or similar)
Core skills
3-5 months- Classical machine learning (scikit-learn): regression, classification, clustering, evaluation metrics
- Feature engineering and handling real, messy datasets
- Experiment design and A/B testing fundamentals
- Communicating results and uncertainty to non-technical stakeholders
Specialize and build
2-4 months- Deep learning fundamentals if your target domain needs it (NLP, computer vision)
- Model deployment basics - what it takes to get a model beyond a notebook
- A domain specialization (product analytics, forecasting, recommendation systems, etc.)
Job-ready polish
2-4 weeks- A portfolio with 2-3 end-to-end projects: problem framing, data, model, and a clearly stated result
- Practice explaining model trade-offs and limitations, not just accuracy numbers
- Mock interviews covering statistics, coding (Python/SQL), and past project deep-dives
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