Data Science & Analytics with Python · Tutorial tracks

Python for data science

NumPy-first mental models, tidy pandas workflows, visualization discipline.

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Lesson outline

  1. 73
    Prepare Data for ML

    Prepare data for machine learning — Python for data science. Learn why prep matters, how to clean, split, and scale data, and apply it in a hands-on exercise. Covers common pitfalls and what to study next.

  2. 74
    Split Data into Train/Test Sets

    Learn to split data into train and test sets in Python for data science. Understand why splitting is crucial for evaluating model performance, and get hands-on with code examples, troubleshooting tips, and what to study next.

  3. 75
    Scale Features with StandardScaler

    Scale features with StandardScaler in Python for data science. Learn the core concept, hands-on steps, troubleshooting, and what to study next.

  4. 76
    Encode categorical variables

    Learn to encode categorical variables with pandas in this Python for data science tutorial — hands-on steps, troubleshooting, and what to study next.

  5. 77
    Scatter Plots & Correlations

    Visualize scatter plots and correlations — Python for data science. This concise tutorial shows you how to create informative scatter plots and interpret correlation coefficients, with hands-on steps and troubleshooting tips. Perfect for step-by-step learners.

  6. 78
    Deploy Models with Flask APIs

    Learn to deploy your machine learning models as Flask APIs — a practical Python for data science lesson with hands-on steps, troubleshooting, and next steps.

  7. 79
    Create REST Endpoints for Predictions

    Learn to create REST endpoints for predictions in this hands-on Python for data science lesson. Build a simple API to serve model outputs, handle requests, and test with curl. Perfect for developers progressing step by step.

  8. 80
    Containerize ML Models with Docker

    Learn to package ML models with Docker for reproducible, portable deployments. This tutorial covers Docker basics, writing a Dockerfile, building and running a container, and best practices for data science workflows.

  9. 81
    Model Performance Monitoring

    Monitor model performance in production — Python for data science.

  10. 82
    Use joblib for model persistence

    Learn to save and load trained models efficiently with joblib in Python for data science — hands-on steps, troubleshooting, and next steps.

  11. 83
    Build pipelines with scikit-learn

    Learn to build pipelines with scikit-learn for cleaner, reproducible data science workflows. This hands-on tutorial shows how to chain preprocessing and modeling steps, compares pipelines to manual steps, and covers common pitfalls.

  12. 84
    Grid Search for Hyperparameter Tuning

    Master grid search for hyperparameter tuning in Python for data science. Step-by-step guide, hands-on exercise, troubleshooting tips, and what to learn next.

    Python for data science — step-by-step tutorials

    What you will find here

    This track walks through python for data science in order — each lesson is server-rendered HTML you can read without JavaScript. Follow the outline, then practice in the browser IDE when a lesson links to runnable code.

    Tutorials vs quizzes and code samples

    Tutorials teach in sequence. For quick checks use quizzes. For copy-paste snippets see code samples. For deeper reading browse articles.