Data Science from Scratch 2e: First Principles with Python

It’s definitely from the ground up - I found it useful to revisit the maths as well as seeing the code - well with the price of the book

This book covers a lot of ground with good explanations and also good code examples. Highly recommended.

This is a good book. It's been updated for Python 3 so you won't miss out on any of the new features. It's definitely not for complete beginners. If you have a foundational knowledge of python then you'll understand some of the concepts outlined. It's also not a tutorial book either, the best way to use this book is to find a part of it and apply it to a data set.

Very good book, I highly recommend it for early stage Data scientists

There is plenty of good content but seems aimed at people with much more math background than me. Also not for python novice.

I was enjoying this book until I got to the Chapters requiring use of the Scratch files. I spend hours trying to get them to work and to no avail. If you are selling a book for £30 everything should be ready to go. Scouring GitHub for information on how to use a file required by a large portion of the book is unacceptable. Returned.

This book is actually 380 pages long, not 500 as reported

The content is good. However I do not understand why it is printed in black and white, not easy to read especially for codes. It is not a “traditional” book, look like a print from printers. Not recommend to buy print copy. If you like the content, buy an electronic copy.

Did you see something on the news about ChatGPT, Stable Diffusion, or some other big development that made you want to look into machine learning? Maybe you truly plan on entering data science as a field but don't know where to start? Or perhaps you've seen one of the author's brilliant/hilarious talks about why he doesn't like Jupyter Notebooks or how to answer the infamous "FizzBuzz" programming interview question using Tensorflow neural networks (seriously, look up Joel Grus on YouTube). If you know a little bit of Python, a little bit of relevant math, and want to go into any data science or machine learning path, then this book is a must-have. It certainly won't be the only resource you'll need, but it helps you get the most out of other content you'll likely look into later (like how to code up a machine learning pipeline, or maybe a large language model if you're really adventurous). Far too many machine learning lessons out there just tell you to import certain Python libraries (scikit-learn for example) and start using them without giving you any basic understanding of how those imported functions even work to begin with. Even to this day there are still college courses and coding bootcamps that ask you to download a Jupyter Notebook file and just hit "Shift + Enter" and look at the output. You're not going to learn how to code that way!!! Joel Grus does an excellent job of filling in this gap by teaching you more Python than what a statistics professional would usually know and more math than what a typical software developer would know. And that's key if you want to go into a field that relies on both. All the information for Python and math that you need to get started is here. It's 27 chapters that get you familiar with Python and how to use it, as well as the math used in data science and ML (linear algebra, probability and statistics, algorithms, etc). You eventually learn enough of both as you go through the chapters to start applying what you learn for some real-world usage. I've had this book for years and it's still as useful as when it first came out, but the only exception I've seen is that the Twitter API tutorial in the book no longer applies to the paid format that Twitter now uses to access that feature. The tutorial is still good for learning how API's get put to use. Once you've read this book and have gotten familiar with all it has to offer, your next step will probably involve looking into a book about how to actually use pre-built data science libraries (like what you find in the Anaconda distribution of Python). This book may turn out to be heavily responsible for my first startup, but that's a story for later.

Buen producto llego bien

Joel's method of explaining is both entertaining and very useful

The book is useful to grasp the basic concept behind data science. However it gets pretty messy as the topics become more complex, especially when the python code is shown without too much of explanations. If you need a book to learn python for data science, there are many other alternatives.

Mr. Grus' book is one of the better data science book I have set my eyes on. His writing style is friendly and informal. Despite this he covers the mathematical and Computational topics in reasonable depth and always points to further reading at the end of chapters. The fact that all code used in the book is also explained therein makes the algorithms very graspable. I would recommend this book anybody who wants to either start with data science or fill in some gaps like was the case with me. I would love read more books written by Mr. Grus'. I have become a fan.