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"Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking" by Foster Provost and Tom Fawcett: This book focuses on the practical aspects of data science and how it can be applied to business problems.
"Python for Data Analysis" by Wes McKinney: A comprehensive guide to data analysis and manipulation using Python's pandas library, widely used in the data science community.
"Introduction to Statistical Learning: with Applications in R" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani: This book provides an accessible introduction to machine learning techniques and their applications, with examples in the R programming language.
"The Elements of Statistical Learning: Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman: A more advanced treatment of machine learning techniques, covering both theoretical foundations and practical applications.
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