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This classic beginner-friendly book briefly introduces the tools and concepts of data science, covering the basics of data analysis, data visualization, and Python.
Focusing on data analysis with Python, this book is ideal for beginners. It strongly emphasizes utilizing the Pandas library with practical examples and exercises.
This is a unique book in this data science books list. It offers non-technical explanations of key ideas for anyone interested in data science in the business world.
Readers with some basic science skills will benefit from this book. It uses Python to study different machine-learning techniques and gives practical examples with useful insights.
As the name suggests, this book focuses on statistics and actually bridges the gap between data science and statistics for intermediate learners.
Filled with practical knowledge and helpful advice on Python-based data science tools. This book is great for readers who want to improve their data analysis knowledge and use Jupyter Notebook.
Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
A technical book that discusses machine learning and statistical learning methods. Based on R programming language, it is suggested for people with a solid background in statistics.
This is a comprehensive guide that explores deep learning techniques. It is a good read for advanced students who wish to explore neural networks and advanced machine learning concepts.
Written by Christopher M. Bishop, this book is recommended for readers familiar with complex mathematical ideas.
This unique data science book combines three complex topics: information theory, inference, and learning algorithms.
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