Python Data Science Tutorials

You use Python to explore, analyze, and visualize data with pandas, NumPy, SciPy, and Jupyter. Create clear charts with Matplotlib and Seaborn, clean messy datasets, and write tests so analyses are repeatable. Work through practical tasks like feature engineering, time series, and text processing while using virtual environments to keep tooling reliable.

When you are ready to model, apply scikit-learn for classification, regression, clustering, and pipelines. For deep learning, train with Keras, TensorFlow, or PyTorch and track results. Scale workloads with Dask, store data in SQLite, PostgreSQL, and deploy predictions with FastAPI and Docker.

Browse all resources below, or commit to a guided Learning Path with progress tracking: