What’s the best way to learn Python for Data Science?

The best way to learn Python for Data Science is to combine theory with hands-on practice. Start with the basics — variables, data types, loops, and functions — using beginner-friendly resources like W3Schools, or YouTube tutorials. Once you’re comfortable, focus on libraries essential for data science such as NumPy (for numerical operations), Pandas (for data manipulation), and Matplotlib/Seaborn (for visualization).
Work on small, real datasets from platforms like Kaggle to apply what you learn. Follow structured courses on Coursera, DataCamp, or freeCodeCamp, as they guide you from fundamentals to advanced topics like data cleaning and analysis.
Practice daily — even 30 minutes a day can build your skills faster. Try replicating existing data projects and then create your own. Finally, join online communities and forums to ask questions, share progress, and learn from others. Consistency and real-world application are the keys to mastering Python for Data Science.

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