---
product_id: 113758849
title: "Data Science from Scratch: First Principles with Python"
price: "€ 76.59"
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---

# Data Science from Scratch: First Principles with Python

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Review: The BEST book for learning how many data science functions work under the hood - START HERE! - 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.
Review: Very very good book! - This book is suitable for people with basic python programming skills. It is very good for beginners and advanced users alike. The codes are very clear and without errors. This book teaches you the basics and introduce some expert level topics for you to explore further if keen. If you are a novice data analyst and some harder topics throw you off, you should probably revisit the topics after you have gain more knowledge on data science. I highly recommend this book as your first book into data science because the codes and thought processes are very clear. 70-80% of the book are data science foundation and basics for you to tackle harder topics later.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #42,967 in Books ( See Top 100 in Books ) #9 in Data Mining (Books) #10 in Data Processing #30 in Python Programming |
| Customer Reviews | 4.4 4.4 out of 5 stars (769) |
| Dimensions  | 6.9 x 0.9 x 9.1 inches |
| Edition  | 2nd |
| ISBN-10  | 1492041130 |
| ISBN-13  | 978-1492041139 |
| Item Weight  | 1.57 pounds |
| Language  | English |
| Print length  | 403 pages |
| Publication date  | June 11, 2019 |
| Publisher  | O'Reilly Media |

## Images

![Data Science from Scratch: First Principles with Python - Image 1](https://m.media-amazon.com/images/I/812I0mhF0DL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ The BEST book for learning how many data science functions work under the hood - START HERE!
*by C***T on April 17, 2023*

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.

### ⭐⭐⭐⭐⭐ Very very good book!
*by A***R on March 17, 2020*

This book is suitable for people with basic python programming skills. It is very good for beginners and advanced users alike. The codes are very clear and without errors. This book teaches you the basics and introduce some expert level topics for you to explore further if keen. If you are a novice data analyst and some harder topics throw you off, you should probably revisit the topics after you have gain more knowledge on data science. I highly recommend this book as your first book into data science because the codes and thought processes are very clear. 70-80% of the book are data science foundation and basics for you to tackle harder topics later.

### ⭐⭐⭐⭐ Good book for startes on AI/ML
*by V***A on December 26, 2020*

Good book for someone starting on learning basics of AI/ML

## Frequently Bought Together

- Data Science from Scratch: First Principles with Python
- Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics
- Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

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*Last updated: 2026-02-01*