two of the most useful books for data science

"The Elements of Statistical Learning: Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. This book provides a comprehensive overview of statistical learning, including both supervised and unsupervised learning methods. It covers topics such as linear regression, decision trees, random forests, support vector machines, neural networks, clustering, and more. The book also includes practical examples and exercises to help you apply the concepts in real-world situations.



"Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython" by Wes McKinney. This book is an excellent resource for learning how to use Python for data analysis. It covers the basics of Python programming and then delves into using the popular data analysis libraries such as Pandas, NumPy, and IPython. The book includes practical examples and exercises to help you master the skills needed to work with data in Python.