I studied science in school, but it has been so long since I last studied math that I sometimes struggle when I need to calculate formulas at work. I've managed each crisis (?) with a quick Google search, but the real problem came when I started doing data-driven work in Python or R. I needed statistics and probability to process large datasets, as well as concepts such as the null hypothesis, p-values, and different tests. I could learn those to some extent, but when I got to matrices,

A first step into data science, starting with foundational math. An Amazon bestseller in calculus and linear algebra. Includes a supplement with practice problems and solutions. When my book was selected for review, I was startled: a math book? But after unwrapping it, reading the preface, and starting the first chapter, I thought, 'Oh, this is easy.' It is easy—some parts are challenging, but still accessible. The English title is... for Data Science. It is for developers who work with data

At first, I opened this book thinking it might be difficult, but it starts with very basic number concepts, including integers and prime numbers. It covers everything from the order of operations—which anyone beyond elementary school should know, and developers especially need—to topics that have always seemed difficult to me, such as Euler's function and natural logarithms, calculus, probability and statistics, vectors, and matrices. The concepts I knew least well were distribution graphs and

If you enjoy the From the Bottom Up deep-learning and machine-learning series, this book can fill in the foundational knowledge those books left out. It uses concise code with pandas, NumPy, scikit-learn, and SymPy, modules familiar to anyone who has studied Python, and devotes a substantial part of the book to explaining concepts. It proceeds from basic ideas through the full material and includes related real-world examples, so it can be a great help even to readers whose math foundation is

A good book for building foundations in data science, computer vision, and related fields. Cover of Essential Math for Developers. I recently heard from an acquaintance on Facebook that this book was being published. Since I study data-related technologies, I really wanted to read it, and fortunately got the chance. Before reading it, I expected the book to focus on math such as calculus and linear algebra rather than connecting it to technology. The table of contents was as I expected, but the

Books with 'coding + math' in the title can be a little intimidating to open because they tend to throw challenging math at you. So I usually just skim them and think, 'Oh, right, this exists.' If I really got into one, I might not finish it this year. In that sense, this book really does cover the essential math. It does get harder as you turn the pages, but you don't have to give up from the first chapter. I read this book, which explains the math concepts and tools behind data science, with

This book covers essential mathematics and gives the knowledge needed for the math problems that arise in algorithms. It includes practice problems and solutions, and carefully explains calculus, eigenvectors, vector transformations, statistics, and career advice. I recommend it to people who want to code math, learn about neural networks, or strengthen their foundations. This review was written with a book provided by Hanbit Media's reviewer program.

For modern developers, math is an essential tool. The core principles and algorithms of data science and machine learning are rooted in mathematical concepts. Understanding foundational math such as calculus, probability, statistics, and linear algebra helps you grasp how machine-learning models and algorithms work, use mathematical tools to solve data-analysis and modeling problems effectively, and interpret and explain model results. This book explains foundational math, including calculus,

I've always been interested in data science, machine learning, and artificial intelligence, and I think these fields have become increasingly important recently. I've also come to realize anew that a mathematical foundation is essential to understanding and using them properly. I have 20 years of programming experience in IT and digital strategy at a bank, and I currently work on core banking development. Until now, I've solved a range of problems through programming without much mathematical