Starting Python for data science can feel overwhelming, but with a structured approach, success is achievable. Begin with Python fundamentals—data types, loops, and functions—then move to libraries like pandas and NumPy for handling data. Data visualization skills are essential, so learn Matplotlib and Seaborn. Statistical knowledge enhances data analysis, while machine learning concepts, taught through Scikit-learn, bring your models to life. Practice by working on real datasets, using platforms like Kaggle and Google Colab. Build projects, create a portfolio, and stay updated with industry trends. With continuous learning, mastering Python for data science becomes a rewarding journey.