Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics

This book breaks down the often-intimidating world of data science into something approachable. The explanations are clear, and the examples are practical, making it perfect for beginners or anyone brushing up on their math skills. It’s a great resource for tackling the math side of data science with confidence

Easy to read and extremely informative!

My team references this book often when explaining why they formed a conclusion from data. It's core to our business and this book outlines so comprehensively the methods of doing it right, identifying bias, and ensuring our conclusions are accurate. This is not a how-to book. It's not even a book on math, really. This is the "why" behind working with data. It is the definitive handbook on data that every data scientist, analyst, business manager should understand before working with data. If you work with data - and just as importantly - if you rely on a team that works with data, this needs to be on your bookshelf.

This book gives a simple and methodical background to the use of math in DS. The author makes sure that there are no gaps in the introduction of math knowledge of the reader when explaining the algorithms. This book makes you appreciate the theories behind DS and ML. The final chapter discloses the reality in the demand vs hype of DS skills, which is honest and valuable. Few errors in the narrations which should be fixed in the next revision.

Presents mathematical concepts in an approachable way that is actually applicable to DS. When they told you the math would be useful some day, this book actually points out why in terms of DS problems.

I came to this with very little stats and linear algebra knowledge and no calculus. The author goes into just enough detail to be able to understand the math without getting overwhelmed, and the Python implementations really help break up the content and stick the math into your mind. The chapters build on each other with a final chapter on Neural Networks integrating everything you have learned previously. This chapter was a bit hard for me to completely follow and I plan to revisit it after some additional math training. This is a book I think I will go back to again and again through the next couple of years.

This is one of the best books I have read, really explaining the fundamentals of math like never explained before. I am also making by teenagers read this book to clarify some concepts and develop more interest in mathematics. I highly recommend this book.

Since I retired over 20 years ago, I lost much of my familiarity with mathematical and statistical concepts. This book provides a good review of those concepts and made me happy to visit them again.

Desde hace tiempo, buscaba una guía que me permitiera adentrarme en el mundo de la ciencia de datos utilizando Python de manera autodidacta, aprovechando mi formación en ingeniería. Finalmente, encontré "Essential Math for Data Science", y resultó ser justo lo que necesitaba. A lo largo del libro, no solo revisé los conceptos fundamentales de álgebra, cálculo, probabilidad, estadística, álgebra lineal, redes neuronales, sino que también pude aplicar cada uno de ellos mediante ejercicios prácticos en Python. Además, complementé mi aprendizaje con el apoyo de DeepSeek, una herramienta de IA que me ayudó a profundizar en los temas más desafiantes. Recomiendo este libro especialmente a quienes, como yo, desean refrescar sus conocimientos matemáticos y aplicarlos de manera directa y efectiva en el ámbito de la ciencia de datos. Es un recurso claro, accesible y muy bien orientado hacia la práctica.

Book is brand new and is inside a plastic sleeve. I got the book at a very reasonable price. Item arrived earlier than expected. Have not gone through the whole book yet, but the it seems to be a good refresher for Data Science.

Excelente

Aux premières lectures, le livre semble bien, les explications sont claires. En revanche la version kindle n'affiche pas tous les symboles ce qui rend la lecture des équations difficile.

Essential Math for Data Science by Thomas Nield is exactly what the title suggests. It covers the most important math concepts that are needed to work in data and analytics related jobs. The topics range from basic math, to probability, stats, linear algebra, and calculus. By focusing on the most important aspects and by providing very manageable examples in Python, one can grasp the intuition behind these topics very fast. Even if you are already a seasoned vet, you might learn new things or at least see them from a different perspective (loved the explanation of statistical significance using the CDF). However, keep in mind that this a very dense book. A lot of content is packed into very few packages. This might be even too dense if you have never been exposed to these topics. Maybe grab a good stats, linear algebra, and calculus intro before jumping into this book.