In 2026, data science and machine learning are no longer reserved for researchers and large tech companies. Thanks to Python and a mature open-source library ecosystem, any developer or analyst can build predictive models, automate data analysis and extract value from massive datasets. But where to start? And how to progress beyond online tutorials? The answer often lies in a few good books. We have selected 5 ebooks available on Amazon that cover the full spectrum, from Jupyter Notebook to the fundamentals of machine learning, through SQL, advanced Python and quantum computing.
1. Machine Learning with Scikit-Learn — The essential reference
If you can only read one book on machine learning, make it this one. Machine Learning with Scikit-Learn (3rd edition) is the definitive reference for learning to implement ML algorithms with Python. The book covers the fundamentals — regression, classification, clustering, dimensionality reduction — with concrete examples drawn from real cases. The 3rd edition incorporates the latest Scikit-Learn updates and covers advanced topics like hyperparameter tuning and processing pipelines. Rated 4.6/5 stars on Amazon, it is a solid, well-structured book for both ML beginners and developers looking to industrialize their models. Available at €30.99.
2. Jupyter Notebook User Guide — Master your working environment
Before even writing your first model, you need to master your tools. Jupyter Notebook is the central working environment of every data scientist: it lets you combine code, visualizations and text in a single interactive document. The Jupyter Notebook User Guide takes you from installation through advanced features — extensions, analysis automation, developing real projects. This guide is particularly useful for structuring your workflow and avoiding hours lost on configuration problems. A €9.99 investment that will save you dozens of frustrating hours.
3. Python and SQL Bible — Data manipulation from A to Z
In data science, 80% of the work consists of preparing, cleaning and manipulating data before even running an algorithm. That is where Python and SQL come in. Python and SQL Bible is a comprehensive guide that takes you from beginner to expert: you will learn to query relational databases with SQL, manipulate DataFrames with Pandas, and combine both for powerful analyses. The book is particularly valuable for profiles working with structured data in business — it bridges the often-overlooked gap between a Python course and the daily reality of the data analyst. Rated 4/5 and available at €44.90, an investment justified by the density of content.
4. Advanced Python Programming (2nd ed.) — Write professional-grade code
Mastering ML algorithms is good. Writing clean, maintainable and performant Python code is better — especially when joining teams or deploying models to production. Advanced Python Programming (2nd edition) guides you toward an elegant and efficient practice of the language: generators, decorators, memory management, functional programming, performance optimization. This guide is aimed at Python developers who already have the basics and want to reach the next level. In data science, this mastery makes the difference between a fragile prototype and a robust pipeline. Rated 3.7/5 and available at €24.99.
5. Quantum Computing Explained — Prepare for the next computing revolution
To end this selection on a forward-looking note: in 2026, quantum computing is no longer science fiction. The first hybrid quantum computers are already being used to optimize complex problems in logistics, finance and pharmaceuticals — fields where data science plays a central role. Quantum Computing Explained offers a clear and accessible dive into this technology: how qubits work, why computing power grows exponentially, and what the first concrete applications are. This book does not replace your classical ML training, but it gives you a head start in understanding what is coming in the next few years. Available at just €5.99.
How to build your data science path?
These 5 ebooks form a coherent path to progress in data science in 2026: start by mastering your environment with the Jupyter Notebook Guide, strengthen your foundations with Advanced Python Programming, add data manipulation with Python and SQL Bible, then launch into model implementation with Machine Learning with Scikit-Learn. Finish with Quantum Computing Explained to keep an eye on the future. Total budget: under €117 for training that opens doors to well-paid positions or freelance missions. Hard to find a better return on investment.
Ready to get started? Start with Machine Learning with Scikit-Learn and lay the foundations of your data expertise.