Data science is expected to have a very exciting year in 2024 due to the increasing demand for additional specialists in this discipline. To fill this requirement, a number of beginner-friendly data science books are available for free.
These data science books include a plethora of knowledge, from advanced machine learning techniques to the foundations of data science. They provide newcomers with a comprehensive, reasonably priced, and user-friendly introduction to the fascinating topic of data science. Let’s now look at these beginner-friendly data science books that are available for free in 2024.
Andrew and Peter Bruce’s Practical Statistics for Data Science: This book covers the basic statistical concepts and techniques used in data science, such as hypothesis testing, data visualization, regression, classification, clustering, and exploratory data analysis.
The robust Python data analysis toolkit known as Pandas was developed by the Pandas Development Team, under the direction of Wes McKinney: This book contains the official documentation for the popular Python data manipulation and analysis program Pandas. It provides a comprehensive overview of all of Pandas’ features and functionalities along with examples and tutorials.
Aurelien Geron: Practical Machine Learning using Tensorflow, Keras, and Scikit-learn for: This book offers a useful introduction to machine learning, deep learning, and artificial intelligence using well-known Python libraries like Scikit-learn, Keras, and Tensorflow. Theory and practice in machine learning include a wide range of topics, including natural language processing, computer vision, neural networks, recurrent networks, convolutional networks, and linear models.
Jake Vanderplas, The Handbook of Python Data Science: This book offers a comprehensive introduction to data science using some of the core Python libraries, including NumPy, SciPy, Matplotlib, Pandas, and Scikit-learn. In addition to covering machine learning, data analysis, manipulation, and visualization, it covers the foundations of Python programming.
Deep Learning for Coders with Fastai and Pytorch by Jeremy Howard and Sylvain Gugger: This book offers a hands-on introduction to deep learning using the Pytorch and Fastai libraries. With little to no code or math knowledge, it describes how to build and train a wide range of deep learning models, such as photo classifiers, recommendation systems, and text generators.
TechGig offers free books on data science that are essential reading for all newcomers: This book includes five free data science books that cover machine learning, deep learning, statistics, Python, and natural language processing, among other topics. There is a synopsis and a link for every book.
365 Data Science’s “The Best Free Data Science Resources for Beginners” (2024): This book provides a range of free data science resources, such as blogs, podcasts, YouTube channels, courses, and projects, that can be used to introduce beginners to the fundamentals of the discipline.
The Top Data Science Books for Novices and Experts, as compiled by KnowledgeHut (2024): This book is a thoughtfully curated collection of fifteen data science works appropriate for readers of all experience levels, from beginner to master. Topics covered include natural language processing, deep learning, machine learning, data visualization, and analysis. It provides a link and an overview of every book.
A reading list titled “12 Data Analytics Books for Beginners” is available on Coursera for 2024: This book contains twelve beginner-friendly data analytics books that cover a wide range of topics, from general overviews to specific recommendations on big data, artificial intelligence, statistical programming languages, and more. There is a synopsis and a link for each book.
Joel Grus:Data Science from Scratch: First priciples using Python: This book offers an introduction to data science using the built-in data structures and capabilities of Python. It covers the fundamental concepts and techniques of data science, including machine learning, probability, statistics, linear algebra, and more, via its implementation from scratch.
