Free Data Science Books for Beginners in 2024

Start your data science journey in 2024 with the best free data science books for beginners

The year 2024 is turning up to be a very exciting one for the field of data science, with a growing need for more expertise in this area. There are several free data science books for beginners available to meet this need.

A wealth of information, ranging from the fundamentals of data science to sophisticated machine learning techniques, may be found in these data science books. They offer a thorough, affordable, and easily navigable introduction to the exciting field of data science for beginners. Now, let us examine these data science books for beginners that are accessible for free in 2024.

• Andrew and Peter Bruce's Practical Statistics for Data Science. The fundamental statistical ideas and methods for data science, including hypothesis testing, data visualization, regression, classification, clustering, and exploratory data analysis, are covered in this book.

• The Pandas Development Team, led by Wes McKinney, has created a powerful Python data analysis toolkit called Pandas. The official documentation for the well-known Python data manipulation and analysis package Pandas is contained in this book. With examples and lessons, it offers a thorough rundown of all of Pandas' features and functionalities.

• Aurelien Geron: Practical Machine Learning Using Scikit-learn, Keras, and Tensorflow. This book uses popular Python libraries like Scikit-learn, Keras, and Tensorflow to provide a practical introduction to machine learning, deep learning, and artificial intelligence. Natural language processing, computer vision, neural networks, recurrent networks, convolutional networks, and linear models are just a few of the machine learning subjects that are covered in both theory and practice.

• The Handbook of Python Data Science by Jake Vanderplas. This book provides a thorough introduction to data science with NumPy, SciPy, Matplotlib, Pandas, and Scikit-learn some of the fundamental libraries for Python. It goes over the fundamentals of Python programming as well as machine learning, data analysis, manipulation, and visualization.

• Jeremy Howard and Sylvain Gugger, Deep Learning for Coders with Fastai and Pytorch: AI applications without a Ph.D. This book uses the Pytorch and Fastai libraries to provide a practical introduction to deep learning. It explains how to create and train a variety of deep learning models with little to no code or arithmetic, including recommendation systems, text generators, picture classifiers, and more.

• Free Data Science books from TechGig that are a must-read for all beginners. Five free data science books are included in this book, which covers subjects including machine learning, deep learning, statistics, Python, and natural language processing. Each book has a link and a synopsis provided.

• Best Free Data Science Resources for Beginners (2024) by 365 Data Science. This book offers a selection of free data science resources that may be used to teach newcomers the basics of the field, including blogs, podcasts, YouTube channels, courses, and projects.

• KnowledgeHut's list of the Top Data Science Books for Novices and Experts (2024). This book is a carefully chosen selection of fifteen data science works suitable for readers with varying skill levels, from novice to expert. Data analysis, data visualization, deep learning, machine learning, natural language processing, and other subjects are covered. It offers a synopsis of each book along with a link.

• Coursera offers a reading list for 2024 called 12 Data Analytics Books for Beginners. Twelve beginner-friendly data analytics books covering a variety of subjects are included in this book, ranging from broad overviews to particular choices on big data, artificial intelligence, statistical programming languages, and more. For every book, there is a summary and a link provided.

• Joel Grus: Data Science from Scratch: First Principles using Python. Python's built-in data structures and functions are used in this book to provide an introduction to data science. Through its implementation from scratch, it covers the basic ideas and methods of data science, including machine learning, probability, statistics, linear algebra, and more.