Are you excited to explore the amazing field of data science? You’re in the proper location! The top 10 Free Data Science eBooks are included below, and they cover a wide range of subjects from machine learning and statistics to advanced themes. These eBooks are a great resource for anyone wishing to improve or advance their abilities since they provide insightful and useful guidance. Come along on this fascinating voyage through the world of data science eBooks, where you will acquire the skills and resources you need for your projects with data!
Key Factors
These 10 Free Data Science eBooks were narrowed down using a number of factors, such as:
- Relevance: Every eBook was chosen based on how well it addressed data science. They go over important subjects like machine learning methods, statistics, data visualization, and Python programming for data analysis.
- Popularity and Reviews: In the data science community, these eBooks are well-known and highly recommended. Readers have given them positive ratings, which is evidence of how well they can teach and clarify difficult subjects.
- Author Credibility: A large number of these eBooks are written by renowned authorities in machine learning and data science. Their knowledge guarantees the correctness and caliber of the information offered.
- Thoroughness: Designed to accommodate novices, intermediate students, and seasoned professionals, the eBooks provide in-depth discussion of a wide range of data science topics. They offer a strong basis in the fundamentals and real-world uses of data science.
- Practicality: Real-world examples, case studies, and interactive activities are all included in each eBook to assist readers in putting the knowledge they have gained into practice. To acquire useful data science abilities, one must adopt this pragmatic approach.
- Availability: Accessible to a broad spectrum of students without regard to budgetary limitations, these eBooks are freely available.
- Diversity of Topics: The shortlisted eBooks cover a broad range of topics within data science, including programming languages like Python and R, machine learning, deep learning, statistics, data visualization, and more.
By considering these factors, the goal was to create a well-rounded list of 10 Free Data Science eBooks that cater to different levels of expertise and interests within the data science community. Whether you’re a beginner looking to get started or an experienced data scientist aiming to deepen your knowledge, these eBooks offer valuable insights and resources to support your learning journey.
10 Best Free Data Science eBooks
Here’s the list of 10 free eBooks for Data Science:
- “Python Data Science Handbook” by Jake VanderPlas
- “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron
- “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- “R for Data Science” by Garrett Grolemund and Hadley Wickham
- “Data Science from Scratch” by Joel Grus
- “Machine Learning Yearning” by Andrew Ng
- “Bayesian Methods for Hackers” by Cameron Davidson-Pilon
- “The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
- “Probabilistic Programming & Bayesian Methods for Hackers” by Cameron Davidson-Pilon
- “Introduction to Statistical Learning” by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Lets us dive into the description of each book.
1. “Python Data Science Handbook” by Jake VanderPlas
This handbook serves as a comprehensive guide to Python’s data science libraries, including NumPy, Pandas, Matplotlib, and Scikit-Learn. What sets it apart is its emphasis on practicality, providing real-world examples and tutorials that make it ideal for beginners and experienced Python users alike. If you’re looking to master data manipulation, visualization, and machine learning in Python, this is the book for you.
Who Should Read It: Beginners and experienced Python users looking to dive into data science with a focus on practical applications.
Where to find: GitHub
2. “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron
The practical approach to machine learning taken by this eBook, which covers both simple and complex algorithms utilizing Scikit-Learn, Keras, and TensorFlow, makes it stand out. It has a ton of useful activities and examples that let users create and train machine learning models from scratch. This book gives you the information and resources to advance your machine learning abilities, regardless of your level of experience.
Who Should Read It: Anyone interested in practical machine learning, from beginners to intermediate learners, who want to build and train models using popular libraries.
Where to find: GitHub
3. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
This eBook, which is regarded as a core text in deep learning, is ideal for anyone wishing to learn more about advanced deep learning ideas and neural networks. It provides a comprehensive grasp of the theory underlying deep learning in addition to useful examples and applications. This book is crucial if you’re interested in cutting edge technology and want to become an expert in deep learning.
Who Should Read It: Intermediate to advanced learners seeking a deep dive into neural networks, deep learning theory, and real-world applications.
Where to find: Website
4. “R for Data Science” by Garrett Grolemund and Hadley Wickha
Designed for those using R for data science, this eBook is an excellent resource for learning data manipulation and visualization with packages like dplyr and ggplot2. What makes it special is its focus on the tidyverse ecosystem, making data science tasks in R more efficient and intuitive. If you’re an R user looking to improve your data wrangling and visualization skills, this book is invaluable.
Who Should Read It: R users interested in mastering the tidyverse ecosystem for efficient data manipulation and visualization.
Where to find: RStudio’s website
5. “Data Science from Scratch” by Joel Grus
This eBook begins, as the title implies, with the fundamentals of data science and progresses to more complex ideas. What makes it special is that it starts with fundamental concepts and works its way up to areas like machine learning, statistics, and linear algebra. Providing a hands-on approach to studying data science principles, the book also stresses real-world code examples created from scratch.
Who Should Read It: Beginners in data science who want to understand core concepts like linear algebra and machine learning algorithms from the ground up.
Where to find: GitHub
6. “Machine Learning Yearning” by Andrew Ng
Authored by the renowned Andrew Ng, this eBook is focused on the practical aspects of building and deploying machine learning systems. It stands out for its strategic approach, guiding readers on how to structure machine learning projects for success. If you’re working on real-world machine learning projects or planning to, this book provides invaluable insights and best practices.
Who Should Read It: Data scientists and machine learning practitioners looking for guidance on structuring and managing machine learning projects effectively.
Where to find: Website
7. “Bayesian Methods for Hackers” by Cameron Davidson-Pilon
Through a realistic and easy-to-understand approach, this eBook presents readers with Bayesian approaches and probabilistic programming. Those who want to learn about uncertainty and probabilistic prediction will find it especially helpful. With its practical examples and activities, the book makes Bayesian methods understandable to a broad readership.
Who Should Read It: Those interested in Bayesian inference and probabilistic programming, from beginners to intermediate learners, seeking practical applications.
Where to find: GitHub
8. “The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
As a classic text in statistical learning, this eBook stands out for its comprehensive coverage of advanced statistical learning methods. It’s perfect for those interested in the mathematical foundations behind machine learning algorithms like linear regression, classification, and clustering. The book includes theoretical explanations along with practical applications, making it suitable for both academics and practitioners.
Who Should Read It: Intermediate to advanced learners interested in the mathematical underpinnings of machine learning algorithms and statistical learning methods.
Where to find: Website
9. “Probabilistic Programming & Bayesian Methods for Hackers” by Cameron Davidson-Pilon
With its practical approach to probabilistic programming and Bayesian approaches, this eBook simplifies these difficult subjects for a broad readership. It stands out for emphasizing practical applications, enabling users to use Bayesian methods to address real-world issues. This book offers a practical manual for anyone interested in learning about uncertainty and creating probabilistic forecasts.
Who Should Read It: Those looking to apply Bayesian methods and probabilistic programming to real-world problems, from beginners to intermediate learners.
Where to find: GitHub
10. “Introduction to Statistical Learning” by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Covering subjects including linear regression, classification, and resampling techniques, this eBook acts as an approachable introduction to statistical learning methodologies. For novices and intermediate students who want to apply statistical learning methods to data analysis, this course is ideal. Because the book contains R examples, it is useful for anyone using R for data science.
Who Should Read It: Beginners and intermediate learners interested in understanding and applying statistical learning methods, especially those using R for data analysis.
Where to find: Website
End Note
These eBooks offer valuable insights and practical guidance for anyone interested in data science, covering essential topics from Python programming and machine learning to statistical learning methods and Bayesian inference. Whether you’re a beginner looking to build a strong foundation or an experienced data scientist seeking to deepen your understanding, these eBooks provide a wealth of knowledge at no cost.
If you’re eager to delve deeper into Data Science, enroll in our course today for comprehensive learning!
My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.
