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28 Practical Data Science

Practical Data Science

This folder is the transition from studying concepts to performing applied work under interview-style and project-style constraints. It is broad by design, so the main job of this README is to keep the breadth from turning into noise.

What This Phase Is For

  • Consolidating ML, statistics, SQL, forecasting, and applied deep learning
  • Practicing end-to-end workflows instead of isolated techniques
  • Preparing for interviews, take-homes, and portfolio projects
  1. 01_START_HERE.ipynb
  2. 02_INTERVIEW_PREP.ipynb
  3. Choose one or two tracks below based on your target role

Subtracks

  • python-data-science/: data cleaning, EDA, feature work
  • machine-learning/: pipelines, validation, ensembles, interpretability
  • statistics-mlops/: experimentation, metrics, monitoring thinking
  • sql-data-engineering/: data extraction and transformation habits
  • time-series-forecasting/: forecasting and anomaly detection
  • computer-vision/: practical CV workflows
  • deep-learning-nlp/: applied NLP and deep learning practice
  • recommender-causal/: recommendation and causal framing
  • solutions/: answer keys or worked examples where available

How To Use This Folder Well

  • Pick a role first, then limit yourself to the most relevant subtracks.
  • Prefer one complete project over sampling ten notebooks shallowly.
  • Write up your assumptions, metrics, and trade-offs as if someone else will review your work.

Capstone Ideas

  • Churn prediction with a full validation and monitoring plan
  • Forecasting dashboard with anomaly alerts
  • Recommendation prototype with offline evaluation
  • SQL-to-model pipeline with a short stakeholder-facing writeup

What Comes Next

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27 Causal Inference01 Start Here