Data Scientist ML Stack Cheat Sheet (2026)

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Set up an isolated Python virtual environment (e.g., python -m venv env or conda create).

Install core ML stack dependencies including pandas, scikit-learn, xgboost, and jupyter.

Perform Exploratory Data Analysis (EDA) to handle missing values and understand feature distributions.

Split the dataset carefully into training, validation, and testing sets to ensure robust evaluation.

Train a simple baseline model and log core metrics like RMSE or F1-score.

Optimize model hyperparameters using automated techniques like grid search or Bayesian optimization.

Serialize the final model using joblib or pickle and containerize it via Docker for deployment.

Data Scientist ML Stack Cheat Sheet