“We’re entering a new world in which data may be more important than software.” — - Tim O’Reilly

“我们正在进入一个新世界，在这个世界中，数据可能比软件更重要。” --蒂姆·奥雷利

The Data Science industry is seeing a rapid increase in its application and offers a very promising future. To be able to enter this domain, one must be equipped with the various concepts, techniques and have sufficient experience with a wide range of tools available for the job.

数据科学行业的应用正在Swift增长，并提供了非常有希望的未来。 为了能够进入这一领域，必须具备各种概念 ， 技术并具有丰富的经验，可以使用多种工具来完成这项工作。

There are hundreds of resources available, including online courses, websites, videos, and books, to get the hang of the subject, as it may seem daunting at first. Throughout this article, we will mention some of the best books for learning Data Science and related technologies that will make learning a breeze.

有数百种可用资源，包括在线课程，网站，视频和书籍 ，可以使您牢牢掌握这一主题，因为乍一看似乎令人生畏。 在整篇文章中，我们都会提到一些学习数据科学和相关技术的最佳书籍，这将使学习变得轻而易举。

Data science is the discipline of making data useful

数据科学是使数据有用的学科

数据科学书籍 (Data Science Books)

In this section, we will highlight a variety of books on Data Science across all skill levels to solidify your knowledge about the domain. These books will prove to be crucial in helping you learn this new skill by giving you a deep dive into the various algorithms, concepts, approaches, as well as supported programming languages and their related packages to make the most out of them.

在本节中，我们将重点介绍各种技能水平各异的数据科学书籍，以巩固您对该领域的知识。 通过深入了解各种算法 ， 概念，方法以及受支持的编程语言 及其相关程序包，以充分利用它们 ，这些书将对帮助您学习这项新技能至关重要。

Disclaimer: There are no affiliate links in this post. This post is for information purposes only.

免责声明： 这篇文章中没有会员链接。 这篇文章仅供参考。

1. Python机器学习简介：数据科学家指南 (1. Introduction to Machine Learning with Python: A Guide for Data Scientists)

Author: Andreas C. Müller and Sarah Guido

作者： Andreas C.Müller和Sarah Guido

Publisher — O′Reilly

发行人 — O'Reilly

Difficulty Level: Beginners

难度等级：初学者

Get Book here — Amazon

在这里获取书 — 亚马逊

Machine learning is a new programming paradigm, a new way of communicating your wishes to a computer. It’s exciting because it allows you to automate the ineffable.

机器学习是一种新的编程范例，是一种将您的愿望传达给计算机的新方式。 令人兴奋的是，它使您可以自动化无法完成的工作。

This book covers a variety of Machine Learning topics in a style that is suited for beginners by showing them how easily they can get started with building their own Machine Learning solutions. It also goes into detail about the best practices for learning and applying Machine Learning to solve common problems without undertaking advanced mathematical courses.

本书以适合初学者的方式涵盖了各种机器学习主题 ，向他们展示了如何轻松地开始构建自己的机器学习解决方案 。 它还详细介绍了学习和应用机器学习来解决常见问题而无需参加高级数学课程的最佳实践。

This introductory book covers the fundamentals concepts, along with the algorithms and a few advanced methods for model evaluation and scikit-learn, a tried and tested Python tool that complements this book for a more hands-on experience of the implementation of Machine Learning.

这本介绍性书籍涵盖了基础概念 ，以及用于模型评估和scikit-learn的算法和一些高级方法 ，scikit-learn是一种经过实践检验的Python工具，可作为本书的补充，以提供更多有关机器学习实施的实际经验。

2. R for Data Science (2. R for Data Science)

Author: Hadley Wickham, Garrett Grolemund

作者： Garrett Grolemund的Hadley Wickham

Publisher — O′Reilly

发行人 — O'Reilly

Difficulty Level: Beginners

难度等级：初学者

Get Book here — Amazon

在这里获取书 — 亚马逊

Read the Book Online — https://r4ds.had.co.nz/

在线阅读书籍-https : //r4ds.had.co.nz/

R is a crucial tool for making sense of the vast amount of siloed data, and this book aims to guide the readers on how to make the most out of R for Data Science. The topics of the book covered follow the core steps in Data Science including, importing, tidying, transforming, visualizing, and modeling of data using the R programming language.

R是了解大量孤立数据的重要工具，该书旨在指导读者如何充分利用R for Data Science。 本书的主题遵循数据科学的核心步骤，包括使用R编程语言导入，整理，转换，可视化和建模数据 。

The book demands a level of prior knowledge of R, its packages such as tidyverse accompanied by a degree of sufficient numerical literacy. Although it doesn’t cover the entirety of the Data Science domain, the author has offered plenty of additional resources that can provide extensive coverage on the included topics.

该书要求一定程度的R的先验知识，以及诸如dydyverse之类的软件包以及一定程度的数字素养。 尽管它没有涵盖整个数据科学领域，但作者提供了许多其他资源，可以广泛涵盖所包含的主题。

3. 裸体统计 (3. Naked Statistics)

Author: Charles Wheelan

作者：查尔斯·惠兰

Publisher — W. W. Norton & Company; Reprint edition

发行人 — WW Norton＆Company； 重印版

Difficulty Level: Beginners

难度等级：初学者

Get Book here — Amazon

在这里获取书 — 亚马逊

An interesting and funny take on the topic of Data Science, this book explains the core notions of the subject by linking them with real-world scenarios. The book aims to deliver the mind-boggling contents from the world of Statistics in a comedic style, and at the same time, inspires the reader to go even deeper into the subject.

本书以有趣而有趣的方式论述了数据科学这一主题， 并通过将其与实际场景联系起来，解释了该主题的核心概念。 该书旨在以喜剧的方式提供来自统计学界的令人难以置信的内容，同时也激发了读者对这一主题的深入研究。

Some of the concepts covered by the author include inference, regression analysis, central limit theorem, reverse causality, positive publication bias. Although it requires some degree of prior experience with Statistics, it succeeds at delivering the intended knowledge in a manner that is highly unique.

作者涵盖的一些概念包括推理，回归分析，中心极限定理，反向因果关系，积极的出版偏见。 尽管它需要一定程度的统计经验，但是它以非常独特的方式成功地交付了预期的知识。

“It’s easy to lie with statistics, but it’s hard to tell the truth without them.”― Charles Wheelan

“很容易撒谎统计，但是如果没有它们，很难说出真相。”- 查尔斯·惠兰

Read this too —

也阅读此书-

4. 数据科学家实用统计 (4. Practical Statistics for Data Scientists)

Author: Andrew Bruce, Peter C. Bruce, and Peter Gedeck

作者：安德鲁·布鲁斯(Andrew Bruce)，彼得·布鲁斯(Peter C. Bruce)和彼得·格德克(Peter Gedeck)

Publisher — O′Reilly

发行人 — O'Reilly

Difficulty Level: Intermediate

难度等级：中级

Get Book here — Amazon

在这里获取书 — 亚马逊

Preferably aimed at Data Science professionals with prior experience with the programming language R and Statistics, this book presents the essential notions of the subject in a handy way to facilitate learning. It also emphasizes the usefulness of the various concepts from the Data Science and Statistics world along with its purpose.

本书最好针对具有R和统计学编程语言经验的数据科学专业人员 ，以便捷的方式介绍该主题的基本概念，以促进学习。 它还强调了数据科学和统计学领域各种概念的用途及其用途。

Practical Statistics for Data Scientists explains the core notions from the subject by relating them with practical examples from the past and the more recent years that are relevant to the Data Science industry. Even though it does cover a majority of the concepts, if not all, the book recommends additional reading.

面向数据科学家的实用统计资料通过将其与过去和最近与数据科学行业相关的实用示例相关联 ，从而解释了该主题的核心概念。 即使本书涵盖了大多数概念，即使不是全部，它也建议您阅读其他内容。

5. 用于数据分析的Python (5. Python for Data Analysis)

Author: Wes McKinney

作者：韦斯·麦金尼

Publisher — O′Reilly

发行人 — O'Reilly

Difficulty Level: Intermediate

难度等级：中级

Get Book here — Amazon

在这里获取书 — 亚马逊

As the title of the book suggests, it focusses heavily on the practical implementations of Python for Data Analysis, to primarily analyze structured data stored in a variety of forms. It goes into the details about the role of Python, its broad collection of libraries for Data Analysis related tasks, and the benefits it provides for Data Science.

就像这本书的书名所暗示的那样，它主要关注Python for Data Analysis的实际实现 ，主要分析以各种形式存储的结构化数据。 它详细介绍了Python的作用，它广泛的用于数据分析相关任务的库以及它为数据科学提供的好处。

Essential Python libraries covered in this book include NumPy, pandas, matplotlib, IPython, and SciPy. The author starts with IPython and includes the rest of the libraries along the way.

本书涵盖的基本Python库包括NumPy，pandas，matplotlib，IPython和SciPy 。 作者从IPython开始，并在此过程中包括了其余的库。

It also covers the fundamentals of Python programming as a quick refresher for readers with little to no Python programming experience.

它还涵盖了Python编程的基础知识，可以帮助那些几乎没有Python编程经验的读者快速复习。

“Act without doing; work without effort. Think of the small as large and the few as many. Confront the difficult while it is still easy; accomplish the great task by a series of small acts. — Laozi”― Wes McKinney

“不采取行动； 毫不费力地工作。 想想大小一样，少则多。 面对困难，却仍然容易； 通过一系列小动作来完成伟大的任务。 -老子”- 韦斯·麦金尼

6.深度学习 (6. Deep Learning)

Author: Ian Goodfellow, Yoshua Bengio, and Aaron Courville

作者： Ian Goodfellow，Yoshua Bengio和Aaron Courville

Publisher — The MIT Press

出版社 —麻省理工学院出版社

Difficulty Level: Beginners

难度等级：初学者

Get Book here — Amazon

在这里获取书 — 亚马逊

Essentially targeted towards university students learning about Machine Learning, Deep Learning, and Artificial Intelligence and those programmers who rapidly want to learn about Machine Learning. The book covers all the introductory sections for Machine Learning, including the mathematical sections and moves on to Deep Networks, covers Deep Learning, and Deep Generative Models. The author has mentioned loads of insights to understand what Machine Learning is and how one can implement it for solving modern-day problems.

本质上针对的是学习机器学习，深度学习和人工智能的大学生以及那些想快速学习机器学习的程序员。 本书涵盖了机器学习的所有入门部分，包括数学部分 ，并深入到深度网络 ，涵盖了深度学习和深度生成模型。 作者提到了大量的见解，以了解什么是机器学习以及如何将其实施以解决现代问题。

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject”

“深度学习由该领域的三位专家撰写，是关于该主题的唯一综合性书籍”

⁠ — Elon Musk, cofounder and CEO of Tesla and SpaceX

⁠— Elon Musk，Tesla和SpaceX的联合创始人兼首席执行官

7. 使用Scikit-Learn和TensorFlow进行动手机器学习 (7. Hands-On Machine Learning with Scikit-Learn and TensorFlow)

Author: By Aurélien Géron

作者： AurélienGéron

Publisher — O’Reilly Media

发行人 — O'Reilly Media

Difficulty Level: Beginners

难度等级：初学者

Get Book here — Amazon

在这里获取书 — 亚马逊

If you have zero knowledge about Machine Learning, this book will be the right choice for you as it takes on the task of equipping you with the right tools, concepts, knowledge, and the mindset to understand what Machine Learning is. The author has covered the various techniques included in the subject and explained it with the help of many production-ready tools and environments, such as Python’s TensorFlow, Scikit-Learn, and Keras.

如果您对机器学习的知识为零，那么本书将是您的正确选择，因为它承担着为您配备正确的工具，概念，知识和思维方式的任务，以了解什么是机器学习。 作者介绍了本主题中包含的各种技术，并在许多可用于生产的工具和环境(例如Python的TensorFlow，Scikit-Learn和Keras)的帮助下进行了解释。

The book aims to deliver a more hands-on experience on the topics with a wide range of examples while giving less attention to theoretical content and encourages its readers to dive deeper into the practical implementation.

该书旨在通过各种示例提供有关该主题的更多动手经验 ，同时减少对理论内容的关注，并鼓励其读者更深入地研究实际实现 。

“In Machine Learning this is called overfitting: it means that the model performs well on the training data, but it does not generalize well.”― Aurélien Géron

“在机器学习中，这被称为过度拟合：这意味着该模型在训练数据上表现良好，但不能很好地泛化。”-AurélienGéron

查看本书的第二版- (Check out the 2nd edition of the book —)

8.统计学习导论 (8. Introduction to Statistical Learning)

Author: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

作者： Gareth James，Daniela Witten，Trevor Hastie，Robert Tibshirani

Publisher — Springer

发行人 —施普林格

Difficulty Level: Beginners

难度等级：初学者

Get Book here — Amazon

在这里获取书 — 亚马逊

This book serves as a guide to Statistical Learning, which essentially translates to a set of tools for modeling and understanding data. Covering the various techniques in the subject, the book puts more emphasis on the practical applications of the several concepts instead of its mathematical implementation.

本书可作为统计学习的指南，从本质上讲 ，它转换为用于建模和理解数据的一组工具。 涵盖了本主题中的各种技术，该书更加强调了几个概念的实际应用 ，而不是其数学实现。

It successfully delivers several complicated topics in a more simplistic and hands-on style to facilitate the learning process by including the R programming language. It does require an understanding of the statistical terms and concepts to make full use of this book.

它以更简单和动手的方式成功交付了一些复杂的主题，通过包含R编程语言来促进学习过程。 确实需要了解统计术语和概念才能充分利用本书。

9. Python数据科学手册 (9. Python Data Science Handbook)

Author: Jake VanderPlas

作者：杰克·范德普拉斯

Publisher — O’Reilly Media

发行人 — O'Reilly Media

Difficulty Level: Intermediate

难度等级：中级

Get Book here — Amazon

在这里获取书 — 亚马逊

Git Hub — https://github.com/jakevdp/PythonDataScienceHandbook

Git Hub- https://github.com/jakevdp/PythonDataScienceHandbook

The goal behind this handy book is to present the various concepts in Data Science not as an entirely new domain, but merely as a new skill. According to the author, Data Science can be best explained as the intersection between hacking skills, substantial expertise of a domain, and the know-how of the maths and statistics in the said domain.

这本便捷的书的目的不是将数据科学中的各种概念呈现为一个全新的领域，而不仅仅是一个全新的领域。 根据作者的说法，数据科学可以最好地解释为黑客技能 ，某个领域的丰富专业知识以及该领域的数学和统计知识之间的交叉点。

The book assumes that the reader has basic experience of Python to create and manage the flow of a Python program, and therefore, focusses primarily on teaching the implementation of Python and its stack of noteworthy libraries in Data Science.

该书假定读者具有Python的基本经验，可以创建和管理Python程序的流程，因此，主要侧重于讲授Python的实现及其在Data Science中值得注意的库堆栈。

10. Scratch的数据科学 (10. Data Science from Scratch)

Author: Joel Grus

作者：乔尔·格鲁斯(Joel Grus)

Publisher — O’Reilly Media

发行人 — O'Reilly Media

Difficulty Level: Beginners

难度等级：初学者

Get Book here — Amazon

在这里获取书 — 亚马逊

If you’re curious to learn about how the various algorithms, libraries, frameworks, and other toolkits in general, work in Data Science, then this is the right book for you. Instead of teaching you about the core aspects of Data Science first, this book takes the opposite route and starts with the very fundamentals of the tools that make Data Science possible and gradually touches upon the various concepts of Data Science along the way. The prerequisites for the book include a prior understanding of mathematics and programming skills.

如果您想了解各种算法 ， 库，框架和其他一般工具包如何在Data Science中正常工作，那么这本适合您的书。 本书并没有先讲授数据科学的核心方面，而是采取了相反的方法，从使数据科学成为可能的工具的最基本基础开始，并逐步触及了数据科学的各种概念。 本书的先决条件包括对数学和编程技能的事先了解。

“Just run: pip install ipython and then search the Internet for solutions to whatever cryptic error messages that causes.”― Joel Grus

“只需运行：pip安装ipython，然后在Internet上搜索导致任何隐秘错误消息的解决方案。”- Joel Grus

11.思考统计 (11. Think Stats)

Author: Allen B. Downey

作者：艾伦·唐尼

Publisher — O’Reilly Media

发行人 — O'Reilly Media

Difficulty Level: Beginners

难度等级：初学者

Get Book here — Amazon

在这里获取书 — 亚马逊

Think Stats offers an introduction to practical tools for exploratory data analysis and follows the author’s style of data processing. The book follows the computational approach rather than the traditional mathematical approach for the primary reason for encouraging the readers to use Python code for better readability and clarity.

Think Stats为探索性数据分析提供了实用工具的介绍，并遵循了作者的数据处理方式。 这本书遵循了计算方法，而不是传统的数学方法，其主要原因是鼓励读者使用Python代码来提高可读性和清晰度。

The idea behind this book is to present a project-based approach where the readers can pick a statistical question, a dataset and apply every technique they learn to that dataset.

本书的思想是提出一种基于项目的方法 ，读者可以选择一个统计问题，一个数据集，并将所学的每种技术应用于该数据集。

The author has also mentioned numerous freely available external references for the topics that require them, such as Wikipedia.

作者还提到了许多免费的外部参考资料，以供需要它们的主题使用，例如Wikipedia。

12.使用Python进行深度学习 (12. Deep Learning with Python)

Author: François Chollet

作者： FrançoisChollet

Publisher — Manning Publications

出版商 —曼宁出版物

Difficulty Level: Expert

难度等级：专家

Get Book here — Amazon

在这里获取书 — 亚马逊

Deep Learning with Python talks about making Machine Learning and Deep Learning available to a vast audience by using Python and its library Keras. Covering the essential background on Artificial Intelligence, Machine Learning and Deep Learning, the book then focusses on Keras’ implementation for Deep Learning.

使用Python进行深度学习讨论通过使用Python及其库Keras使广大读者可以使用机器学习和深度学习。 本书涵盖了人工智能，机器学习和深度学习的基本背景，然后重点介绍了Keras的深度学习实现 。

The author then moves on to cover the practical applications of Deep Learning and its related notions with a healthy amount of code examples. It will be a suitable choice for a majority of technically capable readers, such as data scientists, deep-learning experts, and graduate students, as it requires proficiency in Python.

然后作者继续通过大量的代码示例来介绍深度学习及其相关概念的实际应用 。 由于它需要精通Python，因此它将是大多数具有技术能力的读者(例如数据科学家，深度学习专家和研究生)的合适选择。

“Not all problems can be solved; just because you’ve assembled examples of inputs X and targets Y doesn’t mean X contains enough information to predict Y. For instance, if you’re trying to predict the movements of a stock on the stock market given its recent price history, you’re unlikely to succeed, because price history doesn’t contain much predictive information.”

“并非所有问题都能得到解决； 仅仅因为您已经组合了输入X和目标Y的示例，并不意味着X包含足够的信息来预测Y。例如，如果您要根据最近的价格历史来预测股票在股票市场的走势，您不太可能成功，因为价格历史记录没有太多的预测信息。”

― Francois Chollet,

―弗朗索瓦·乔莱特

更多数据科学书籍可供阅读— (More Data Science Books to Read —)

• Pattern recognition and machine learning模式识别和机器学习
• Practical data science with RR的实用数据科学
• Python Machine Learning By ExamplePython机器学习实例
• Think Python考虑Python
• The Elements of Statistical Learning统计学习的要素
• Think Bayes — Bayesian Statistics Made Simple贝叶斯思考—贝叶斯统计简化
• Designing Data-Intensive Applications设计数据密集型应用

结论 (Conclusion)

Data Science is a vast industry and encompasses a host of powerful and efficient tools for performing a variety of tasks on data. An aspiring Data Scientist should have the know-how of these tools to work their way around the data, to achieve performance-driven results. By drawing your attention towards a collection of some of the best Data Science books, we would like to encourage anyone looking for an entry point into Data Science and Machine Learning. These books are no doubt some of the best that will enhance your knowledge of not just mathematics, but also the several programming languages and libraries used throughout.

数据科学是一个广阔的行业，包含许多功能强大且高效的工具，可以对数据执行各种任务。 有抱负的数据科学家应具有这些工具的专业知识，可以围绕数据工作，以实现性能驱动的结果。 通过将您的注意力吸引到一些最佳的数据科学书籍上，我们希望鼓励任何正在寻找数据科学和机器学习切入点的人。 这些书籍无疑是一些最好的书籍，它们不仅可以增强您对数学的知识，而且可以增强您在本书中使用的几种编程语言和库的知识。

Note: To eliminate problems of different kinds, I want to alert you to the fact this article represent just my personal opinion I want to share, and you possess every right to disagree with it.

注意： 为消除各种问题，我谨在此提醒您，本文仅代表我要分享的个人观点，您拥有反对该观点的一切权利。

更有趣的读物— (More Interesting Readings —)

I hope you’ve found this article useful! Below are some interesting readings hope you like them too-

希望本文对您有所帮助！ 以下是一些有趣的读物，希望您也喜欢它们-

About Author

关于作者

Claire D. is a Content Crafter and Marketer at Digitalogy — a tech sourcing and custom matchmaking marketplace that connects people with pre-screened & top-notch developers and designers based on their specific needs across the globe. Connect with Digitalogy on Linkedin, Twitter, Instagram.

克莱尔·D 。 是 Digitalogy 的Content Crafter and Marketinger ，这 是一个技术采购和自定义配对市场，可根据人们在全球的特定需求，将人们与预先筛选和一流的开发商和设计师联系起来。 在 Linkedin ， Twitter ， Instagram 上 与 Digitalogy联系 。