Top 7 best statistics books for data science in 2024

1. "The Elements of Statistical Learning" by Hastie, Tibshirani, & Friedman: Comprehensive coverage of statistical methods for machine learning.

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2. "Pattern Recognition and Machine Learning" by Christopher Bishop: Essential for understanding statistical methods in pattern recognition.

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3. "Statistical Learning with Sparsity" by Trevor Hastie, Robert Tibshirani, & Martin Wainwright: Focuses on statistical methods for high-dimensional data.

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4. "Bayesian Data Analysis" by Gelman et al. In-depth exploration of Bayesian methods for data analysis.

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5. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron Practical guide to applying statistical models in machine learning.

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6. "Introduction to the Practice of Statistics" by Moore, McCabe, & Craig Excellent for foundational statistics concepts applied to real-world data.

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7. "Practical Statistics for Data Scientists" by Peter Bruce & Andrew Bruce Practical insights into applying statistical methods to data science problems.

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