Learning Python for data science requires a structured approach. Start with the fundamentals: syntax, control structures, and functions. Move on to data manipulation using pandas and NumPy, followed by data visualization with Seaborn and Matplotlib. Explore statistics and probability, as they form the foundation of data science. Learn machine learning with Scikit-learn and deep learning with TensorFlow or PyTorch. Practice through projects such as data analysis, predictive modeling, and web scraping. Use online resources, books, and coding challenges to reinforce learning. A combination of theory and hands-on experience will help you master Python for data science.