10 Python Data Science Books You Need to Read in 2024

Master Python Data Science with these 10 Must-Read Books in 2024

Python stands out as a widely embraced and versatile programming language in the realm of data science. Whether you are a beginner or an expert, many books can help you learn new skills, explore new topics, and improve your performance. Here are 10 Python data science books that you need to read in 2024, covering various aspects of data analysis, machine learning, deep learning, natural language processing, computer vision, and more.

1. Python Data Science Handbook:

This book is a comprehensive guide to the core tools and techniques of data science with Python. It covers the basics of data manipulation, visualization, and exploration with NumPy, pandas, Matplotlib, and Seaborn, as well as the fundamentals of machine learning with Scikit-learn, TensorFlow, and PyTorch.

2. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow:

This book is a practical and hands-on introduction to machine learning with Python. It teaches you how to use Scikit-learn, Keras, and TensorFlow to build and train various types of machine learning models, such as linear regression, classification, clustering, dimensionality reduction, neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and reinforcement learning.

3. Python for Data Analysis:

This book is a detailed and practical guide to data analysis with Python. It teaches you how to use pandas, NumPy, and IPython to clean, transform, and manipulate data, as well as how to perform exploratory data analysis, statistical analysis, and data visualization.

4. Deep Learning with Python (2nd Edition) by François Chollet

This book is a comprehensive and in-depth introduction to deep learning with Python. It teaches you how to use Keras and TensorFlow to build and train various types of deep learning models, such as feedforward neural networks, convolutional neural networks, recurrent neural networks, attention mechanisms, transformers, and autoencoders.

5. Natural Language Processing with Python:

This book is a comprehensive and practical introduction to natural language processing (NLP) with Python. It teaches you how to use the Natural Language Toolkit (NLTK) to analyze and process text, such as tokenizing, tagging, parsing, stemming, lemmatizing, chunking, named entity recognition, sentiment analysis, text classification, and text generation.

6. Python Machine Learning:

This book is a comprehensive and practical introduction to machine learning and deep learning with Python. It teaches you how to use Scikit-learn and TensorFlow to build and train various types of machine learning and deep learning models, such as linear models, tree-based models, ensemble models, support vector machines, neural networks, convolutional neural networks, recurrent neural networks, and autoencoders.

7. Python for Computer Vision with OpenCV and Deep Learning by Jose Portilla

This book is a comprehensive and practical introduction to computer vision with Python. It teaches you how to use OpenCV and TensorFlow to perform various tasks and applications of computer vision, such as image processing, face detection, object detection, segmentation, tracking, optical character recognition, face recognition, and style transfer.

8. Data Science from Scratch:

This book is a unique and fun introduction to data science with Python. It teaches you how to implement the core concepts and algorithms of data science from scratch, using only Python and its standard library. You will learn how to work with data, such as vectors, matrices, statistics, probability, linear algebra, and calculus, as well as how to build various types of machine learning models, such as k-nearest neighbors, linear regression, logistic regression, decision trees, neural networks, and clustering.

9. Python Crash Course:

This book is a fast and easy introduction to programming with Python. It teaches you the basics of Python, such as variables, data types, functions, classes, modules, files, exceptions, testing, and debugging, as well as how to use Python for various tasks, such as web development, data visualization, and game development.

10. Automate the Boring Stuff with Python:

This book is a fun and practical introduction to programming with Python. It teaches you how to use Python to automate various tasks and chores, such as searching and downloading files, renaming and organizing files, sending emails and texts, filling out forms, scraping web pages, controlling the keyboard and mouse, and more.