Introduction

介绍

I get asked a lot what resources I recommend for people who want to start their Data Science journey. This section enlists books I recommend you should read at least once in your life as a Data Scientist.

我被很多人问到了我为想要开始数据科学之旅的人们推荐哪些资源。 本节列出了一些书，我建议您作为数据科学家一生中至少应阅读一遍。

Do you need to read these books to learn to be a Data Scientist? The answer is: no. There are plenty of tutorials and free material online that is as good as these books. However, if you can afford to buy them and can read them as supplementary material they can become a very good resource to learn. Unlike online tutorials, these books have a structure and teach concepts in an organized and structured manner. This means instead of wasting time searching the internet to find good tutorials you can spend this time learning.

您需要阅读这些书才能学习成为一名数据科学家吗？ 答案是不。 在线上有很多教程和免费资料，与这些书籍一样好。 但是，如果您有能力购买它们并可以阅读它们作为补充材料，那么它们可以成为学习的很好资源。 与在线教程不同，这些书具有结构化和以有组织和结构化的方式讲授概念。 这意味着您可以花时间学习，而不是浪费时间在互联网上寻找好的教程。

The books I recommend here cover the main topics that you will need to master as a Data Scientist: programming (python), data analysis, and Machine Learning (including deep learning). I know there are plenty of books on each topic but those are the ones that I have used in my learning journey and I can truly recommend them.

我在这里推荐的书涵盖了您作为数据科学家需要掌握的主要主题：编程(python)，数据分析和机器学习(包括深度学习)。 我知道每个主题都有很多书，但是这些都是我在学习过程中使用的书，我可以真正推荐它们。

Python Programming

Python编程

As a Data Scientist, you should be primarily a good programmer or at least work towards achieving programming proficiency at least in one language. I recommend learning python for its common usage in the Data Science and relatively simple learning curve.

作为数据科学家，您应该首先是一名优秀的程序员，或者至少要努力实现至少一种语言的编程能力。 我建议学习python，以了解它在数据科学中的常用用法以及相对简单的学习曲线。

This book is like a python bible. It has around 1600 pages and covers all basic and more advanced python concepts.

这本书就像Python圣经。 它大约有1600页，涵盖了所有基本和更高级的python概念。

It is a good book for someone starting with python as it has in-depth explanations of the language and programming concepts, and the content is presented in a simple understandable manner.

对于从python开始的人来说，这是一本好书，因为它对语言和编程概念有深入的说明，并且内容以简单易懂的方式呈现。

It will also be a very good revision for someone who has been working with python for a while but wants to get better at it, improve the understanding of the language and common concepts especially Object-Oriented Programming.

对于已经使用python一段时间但想要更好地使用它，提高对语言和通用概念(尤其是面向对象编程)的理解的人来说，这将是一个很好的修订。

You can get this book from here (affiliate link).

您可以从这里获得这本书(会员链接)。

Data Analysis

数据分析

This book covers almost everything that concerns data analysis, data cleaning, and data preprocessing with pandas. And what do Data Science do most of the time?

本书涵盖了几乎所有涉及数据分析，数据清理以及使用熊猫进行数据预处理的内容。 数据科学在大多数情况下会做什么？

Unfortunately or fortunately, we spend most of the time preparing data for fitting in Machine Learning algorithms. This book covers it all, and just enough python for data analyst or junior Data Scientist to get familiar with programming and libraries popular for data analysis.

不幸的是，幸运的是，我们大部分时间都在准备数据以适合机器学习算法。 本书涵盖了所有内容，并且足够供数据分析人员或初级数据科学家使用python，以熟悉流行于数据分析的程序和库。

Additionally, this book has been written by Wes McKinney who is the author of pandas package. And who would be the best person to learn data analysis from if not the author of one of the most popular python data analysis library that has been created.

此外，这本书是由熊猫包装的作者韦斯·麦金尼(Wes McKinney)撰写的。 如果不是创建的最受欢迎的python数据分析库之一的作者，谁将是学习数据分析的最佳人选。

You can get this book from here (affiliate link).

您可以从这里获得这本书(会员链接)。

Machine Learning

机器学习

If you were to buy only one book about Machine Learning that would be my choice.

如果您只购买一本有关机器学习的书，那将是我的选择。

It could be a book for a beginner Data Scientist wanting to have an overview of Machine Learning algorithms and how to implement them on real-life examples using scikit-learn.

它可能是一本针对初学者数据科学家的书，该书希望概述机器学习算法以及如何使用scikit-learn在实际示例中实现它们。

It is also a good revision for someone who is already familiar with Machine Learning concepts and wants a book for quick references and review.

对于已经熟悉机器学习概念并且想要一本书以便快速参考和复习的人来说，这也是一个很好的修订。

Additionally, it has a fantastic second section that focuses on od deep learning with Keras and TensorFlow.

此外，它还有一个精彩的第二部分，重点介绍了使用Keras和TensorFlow进行深度学习。

You can get this book from here (affiliate link).

您可以从这里获得这本书(会员链接)。

Other topics in Data Science

数据科学中的其他主题

Being a Data Scientist does not involve only python programming, data analysis, and Machine Learning. There are other topics that you should master in this profession. The first areas that come to my mind are Maths and Statistics.

成为数据科学家不仅仅涉及python编程，数据分析和机器学习。 在这个专业中，您还应该掌握其他主题。 我想到的第一个领域是数学和统计学。

​I am not recommending any books on those topics as I have been relying on my high school and university knowledge with those, and supplying this knowledge with online tutorials and resources. If I read any good books on those topics I will update this list.

``我不推荐任何有关这些主题的书，因为我一直依赖于我的高中和大学知识，并向这些知识提供在线教程和资源。 如果我阅读了有关这些主题的好书，则将更新此列表。

​Originally published at https://www.aboutdatablog.com on August 19, 2020.

本来在发表 https://www.aboutdatablog.com 于2020年8月19日。

PS: I am writing articles that explain basic Data Science concepts in a simple and comprehensible on aboutdatablog.com. If you liked this article there are some other ones you may enjoy:

PS：我写的文章在 aboutdatablog.com 上以简单易懂的方式解释了基本的数据科学概念 。 如果您喜欢这篇文章，您可能还会喜欢其他一些文章：