The first stage in learning Python for data science step-by-step is to become proficient in the language's fundamental grammar, which includes variables, data types, loops, and functions. Next, explore libraries for data analysis and manipulation such as pandas and NumPy. Try your hand at data visualisation with Seaborn and Matplotlib. After you feel at ease, study statistical concepts, exploratory data analysis (EDA), and data cleansing. Next, investigate machine learning libraries for creating models, such as TensorFlow and scikit-learn. Finally, use your talents by working on real-world projects. You may ensure a strong foundation in Python for data science by following the procedures outlined in a structured data science course.
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