What are Python’s most widely used libraries for data science and analytics?
Python offers a rich ecosystem of libraries that make data science and analytics efficient and powerful. Pandas is widely used for data manipulation and analysis. NumPy supports numerical computations, while Matplotlib and Seaborn are preferred for data visualization. For machine learning tasks, Scikit-learn is a popular choice. TensorFlow and PyTorch are extensively used in deep learning projects. Statsmodels and SciPy assist in statistical analysis and scientific computing. These libraries simplify complex processes and enable faster, more accurate insights. Mastering them is essential for anyone aiming to excel in this field. Start your journey with a Python certification course.
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