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Data Scientist Roadmap

You want to answer hard questions with statistics and models

Full mathematical and statistical foundation, then machine learning end to end.

The route

  1. Week 1 - Foundations: Strong math fundamentals are non-negotiable for rigorous data science
  2. Week 2 - Python for ML: Your daily toolkit - NumPy, pandas, matplotlib, scikit-learn
  3. Week 2 - SQL for Data: Daily tool - extract training data, build features, explore datasets at database scale
  4. Week 3 - Git & Version Control: Version your notebooks, collaborate on shared codebases, and ensure experiment reproducibility
  5. Week 3 - Jupyter & Colab: Your primary workspace for exploration, analysis, and presentation
  6. Week 3 - Probability Theory: Bayes theorem, distributions, expected value - asked in every DS interview
  7. Week 4 - Statistics: Core interview topic - hypothesis testing, confidence intervals, distributions
  8. Week 5 - Linear Algebra: PCA, SVD, embeddings - the math behind dimensionality reduction
  9. Week 6 - Calculus: Understand optimization deeply - gradients, chain rule, integrals
  10. Week 7 - Optimization: How every ML model learns - gradient descent, loss surfaces, convergence
  11. Week 8 - ML Algorithms: The core of data science - regression, trees, SVMs, ensembles, evaluation
  12. Week 9 - Deep Learning: Neural networks and transformers - increasingly required for DS roles
  13. Week 9 - Computer Vision: Scope a vision request before it becomes a labelling bill - which task, what it costs to annotate, and which metric can score it
  14. Week 10 - LLM: Modern data science uses LLMs for analysis, summarization, and augmentation
  15. Week 11 - Agentic AI & Multi-Agent Systems: Modern data science uses AI agents for automated analysis and data workflows
  16. Week 11 - ML Pipeline: End-to-end workflows - EDA, preprocessing, training, evaluation, deployment
  17. Week 12 - Information Theory: Entropy, KL divergence, mutual information - understand model decisions deeply
  18. Week 13 - AI Tools: AI coding tools speed up data exploration, notebook workflows, and model experimentation
  19. Week 13 - AI on Cloud: Run notebooks and managed ML/GenAI services in the cloud - Vertex AI, Bedrock, Azure ML basics
  20. Week 14 - Interview Q&A: Scenario-based interview prep - production problems that AI companies actually ask
  21. Week 14 - AI System Design: The round where a data scientist is asked to ship rather than to analyse. Sizing, serving and evaluation, in the vocabulary the interviewer actually uses