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

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.

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

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.

This book is proving to be tremendously helpful for me, the concepts are introduced in a nugget-sized non-intimidating way. I find myself understanding more and more, and gaining confidence with concepts I previously felt shaky about. The book is hard to put down. Great job!