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数据科学，编辑(Data Science, Editorial)
Over the last decade, data science has become one of the most paid and highly reputed domains for professionals in the information technology field.
在过去的十年中，数据科学已成为信息技术领域专业人员中报酬最高且享有盛誉的领域之一。
Nowadays, data science applications have become inevitable for most (if not all) businesses. Hence, there is a surge of proficient data science professionals.
如今，数据科学应用已成为大多数(如果不是全部)企业的必然选择。 因此，涌现出一批精通数据科学专业人士。
Therefore, if you plan to move into this domain, you may find a wide variety of data-science-related books available online, which in turn, can be an arduous task to pick out the most notable books to get into data science.
因此，如果您打算进入这个领域，您可能会在网上找到各种各样与数据科学相关的书籍，这反过来可能是一项艰巨的任务，要挑选出最著名的书籍来进入数据科学。
This article aims to solve this conundrum by providing you with our editorial recommendations on the best and high-quality books for data science.
本文旨在通过为您提供有关数据科学的最佳和高质量书籍的编辑建议来解决这个难题。
Disclosure: Our editorial team at Towards AI writes authentic and trustworthy reviews and may receive a small compensation on products we select to support Towards AI’s efforts. For this article, as an Amazon Associate Towards AI may receive a small commission from qualifying purchases made from it. For feedback, questions, or concerns, please email us [email protected].
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1.数据科学家实用统计： (1. Practical Statistics for Data Scientists:)
Author(s): Peter Bruce, Andrew Bruce, Peter Gedeck
作者：彼得·布鲁斯，安德鲁·布鲁斯，彼得·格德克
This book is ideal for absolute beginners. It covers a basic overview of all the prerequisite concepts to get deeper into the domain of data science. In this book, you will learn concepts of exploratory data analysis, random sampling, regression analysis, classification techniques, statistical machine learning methods, and much more. Other than theoretical concepts, it encompasses code examples in R as well as Python programming language. We find this a great resource to learn data science, as it’s all about getting you familiar with data science without diving into much depth. Other than that, you will also find additional resources that will lead you to understand some more advanced topics in data science. In conclusion, this is an excellent resource for data science beginners.
这本书是绝对初学者的理想选择。 它涵盖了所有必备概念的基本概述，以更深入地介绍数据科学领域。 在本书中，您将学习探索性数据分析，随机抽样，回归分析，分类技术，统计机器学习方法等概念。 除了理论概念外，它还包含R语言以及Python编程语言中的代码示例。 我们发现这是学习数据科学的绝佳资源，因为这一切都是为了让您熟悉数据科学而无需深入。 除此之外，您还将找到其他资源，这些资源将使您了解数据科学中的一些更高级的主题。 总之，对于数据科学初学者来说，这是一个极好的资源。
Grab a copy on Amazon.
在Amazon上获取副本。
2. Python机器学习简介： (2. Introduction to Machine Learning with Python:)
Author(s): Andreas C. Muller, Sarah Guido
作者：萨拉·吉多(Andrews C. Muller)，萨拉·圭多(Sarah Guido)
This book is an ideal option for those who want to kick start their journey in Data Science. With a friendly tone and illustrative examples, this book provides a clear explanation of fundamental concepts in data science and machine learning. The best thing about this book is that the reader does not require any prior knowledge of data science, machine learning, and Python. This book contains the — fundamental concepts and application of machine learning, advanced techniques for model evaluation, representation of data, the concept of the pipeline, suggestions for improving your data science and machine learning skills, and many more things. This book is probably one of the best for learning data science with Python.
对于那些想开始数据科学之旅的人来说，这本书是理想的选择。 本书以友好的语气和示例性示例清晰地解释了数据科学和机器学习中的基本概念。 关于这本书的最好的事情是，读者不需要任何有关数据科学，机器学习和Python的先验知识。 本书包含机器学习的基本概念和应用，模型评估的高级技术，数据表示，管道的概念，有关改善数据科学和机器学习技能的建议，以及更多其他内容。 这本书可能是用Python学习数据科学的最好的书之一。
Grab a copy on Amazon.
在Amazon上获取副本。
3.商业数据科学： (3. Business Data Science:)
Author(s): Matt Taddy
作者：马特·塔迪(Matt Taddy)
This book by Matt Taddy, Ph.D. from Amazon Science focuses on the business perspective of data science. It covers topics that impact real business environments. It contains theory with appropriate coding exercises that help readers to gain useful insights from it. Applying our knowledge in the business domain can be challenging, as models, in theory, make different kinds of assumptions, and when they are applied in practice, sometimes we see surprising results than those presented on paper.
这本书由Matt Taddy博士撰写。 来自Amazon Science的研究专注于数据科学的业务角度。 它涵盖了影响实际业务环境的主题。 它包含理论和适当的编码练习，可以帮助读者从中获得有用的见解。 在模型中将我们的知识应用于业务领域可能具有挑战性，因为模型在理论上会做出不同类型的假设，而在实践中应用这些假设时，有时我们会看到比纸上呈现的结果更令人惊讶的结果。
Taddy’s background and expertise in academia and industry make him the perfect author to write this book. We are confident that you will feel sure of applying your data science skills and knowledge in real-world scenarios after reading this book.
Taddy在学术界和工业界的背景和专业知识使他成为撰写本书的完美作者。 我们相信，阅读本书后，您一定会在实际场景中应用数据科学技能和知识。
Grab a copy on Amazon.
在Amazon上获取副本。
4.概论： (4. Introduction to Probability:)
Author(s): Joseph K. Blitzstein, Jessica Hwang
作者：Joseph K. Blitzstein，Jessica Hwang
Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. Making it, perhaps, the best book to learn probabilities. This book is recommended for both beginners and experts as it starts with basic concepts and moves its way through the core concepts of probability that will help you build a solid foundation in the domain of data science. This book includes intuitive explanations, examples, diagrams, and practice problems. Each chapter of this book ends with its relevant code examples in R programming language. In the new edition, they have included online supplements that include interactive visualization and animations. The book has been one of the most popular books for about five decades, and that is one more reason why it should definitely be on your bookshelf.
源自哈佛大学著名的统计学讲座《概率概论》 提供必要的语言和工具 了解统计信息，随机性和不确定性。 使其成为学习概率的最佳书。 无论是初学者还是专家，都建议使用这本书，因为它从基本概念入手，并贯穿了概率的核心概念，将帮助您在数据科学领域打下坚实的基础。 本书包括直观的解释，示例，图表和实践问题。 本书的每一章都以R编程语言结尾其相关的代码示例。 在新版本中，它们包括在线补充，其中包括交互式可视化和动画。 这本书已经成为大约五十年来最受欢迎的书籍之一，这也是为什么它绝对应该出现在书架上的又一个原因。
Grab a copy on Amazon.
在Amazon上获取副本。
5. Scratch的数据科学： (5. Data Science from Scratch:)
Author(s): Joel Grus
作者：Joel Grus
In this book, you will learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have a strong aptitude for mathematics and some necessary programming skills, this book will help you get into the core of data science in a satisfying way. There are many books available online, which gives you the basic idea of the implementation of statistical models by using libraries. But after all, these libraries are made from scratch. So if you want to learn data science from scratch and enhance your knowledge in this domain, then this book will definitely help you achieve your goal. The topics of this book are — basics of statistics, cleaning and manipulating data, diving deep into fundamentals of machine learning algorithms, implementation of machine learning algorithms from scratch, exploration of natural language processing, recommender system, network analysis, and many more. So if you really want to study data science the hard way, this is the book for you.
在本书中，您将从头开始实施，以了解有多少最基本的数据科学工具和算法可以工作。 如果您具有较强的数学能力和一定的编程技能，则本书将帮助您以令人满意的方式进入数据科学的核心。 在线上有很多书籍，这些书籍为您提供了使用库实现统计模型的基本思想。 但是毕竟，这些库是从头开始构建的。 因此，如果您想从头开始学习数据科学并增强在该领域的知识，那么本书无疑将帮助您实现目标。 本书的主题包括-统计基础，清理和处理数据，深入学习机器学习算法的基础知识，从头开始实施机器学习算法，探索自然语言处理，推荐系统，网络分析等等。 因此，如果您真的想以艰苦的方式学习数据科学，那么这本书就是您的理想选择。
Grab a copy on Amazon.
在Amazon上获取副本。
6.裸体统计： (6. Naked Statistics:)
Author(s): Charles Wheelan
作者：查尔斯·惠兰
This book gives us a lot of real-life examples of how statistical concepts apply in the real world. The tone of the book is witty and conversational. The author of this book does not go deep into the theories, but instead, he uses pretty compelling examples to help you understand even some of the complex statistical concepts. This book starts with fundamental concepts of statistics like a normal distribution, central limit theorem, and goes on to complex real-world problems and correlating data analysis and machine learning. All in all, if you are new to data science, this book will make you laugh while understanding statistical concepts.
本书为我们提供了许多现实情况的例子，说明了统计概念在现实世界中的应用。 这本书的基调是机智和对话性的。 本书的作者没有深入研究这些理论，而是使用了非常有说服力的示例来帮助您理解甚至一些复杂的统计概念。 本书从统计学的基本概念入手，例如正态分布，中心极限定理，并继续探讨复杂的实际问题以及将数据分析与机器学习相关联。 总而言之，如果您是数据科学的新手，这本书会让您在理解统计概念的同时大笑。
Grab a copy on Amazon.
在Amazon上获取副本。
7. Python进行数据分析： (7. Python for Data Analysis:)
Author(s): Wes McKinney
作者：韦斯·麦金尼
This book is another excellent read if you have some basic knowledge of data science concepts. This book covers almost every method for data analysis alongside the basics of python programming language. The book covers — use of Ipython shell and jupyter notebook for exploratory data analysis, basic and advanced features of NumPy, data analysis with pandas, how to get clean data, visualization with matplotlib, summarizing data with pandas, time series analysis, and many more. In short, we can say that the author gives you a complete idea of what you should expect by working as a data scientist. Apart from that, the book is comprehensive, easy to read, and self-paced.
如果您具有数据科学概念的一些基础知识，这本书是另一本优秀的读物。 本书涵盖了几乎所有数据分析方法以及python编程语言的基础知识。 该书涵盖了-使用Ipython shell和jupyter笔记本进行探索性数据分析，NumPy的基本和高级功能，使用熊猫进行数据分析，如何获取干净数据，使用matplotlib进行可视化，使用熊猫进行数据汇总，时间序列分析等等。 。 简而言之，我们可以说作者为您提供了一个完整的想法，即您将成为一名数据科学家。 除此之外，该书内容全面，易于阅读且自定进度。
Grab a copy on Amazon.
在Amazon上获取副本。
8.使用Scikit-Learn和TensorFlow进行动手机器学习： (8. Hands-on Machine Learning with Scikit-Learn and TensorFlow:)
Author(s): Aurélien Géron
作者：AurélienGéron
This book is probably one of the largest in data science and machine learning, which is packed with fantastic knowledge. It is recommended for both beginners and experts to gain useful insights into this domain. This book has a little theory, but it has powerful examples supporting it, which makes it in this list. The topics included in this book are — neural networks, scikit-learn for machine learning projects, training models in machine learning, TensorFlow to build and train neural networks, and many more. We can confidently say that after going through this book, you will be able to dive deeper into deep learning and solve real-world problems.
这本书可能是数据科学和机器学习中最大的一本书，其中包含了丰富的知识。 建议初学者和专家都对该领域获得有用的见解。 这本书有一点理论，但是它有强大的示例支持它，因此它在此清单中。 本书包含的主题包括-神经网络，用于机器学习项目的scikit-learn，机器学习的训练模型，用于构建和训练神经网络的TensorFlow等。 我们可以自信地说，在读完本书之后，您将能够更深入地学习深度学习并解决实际问题。
Grab a copy on Amazon.
在Amazon上获取副本。
9.头数统计： (9. Head First Statistics:)
Author(s): Dawn Griffiths
作者：黎明·格里菲思(Dawn Griffiths)
Just like the other books of headfirst, the tone of this book is amiable and conversational, so you will not get bored after reading a few pages. The book covers a range of topics covered in first-year statistics that are essential for data science. This book brings typically dry subjects to life by providing engaging and thought-provoking material full of visual-aids and real-life examples. In this book, you will start with topics of descriptive statistics — mean, median, mode, standard deviation, variance — and then move to the inferential statistics like correlation, regression, and others. It also includes a thorough explanation of normal, binomial, Poisson, geometric probability distributions. Other than that, this book is full of pictures and graphics that make statistics topics easy to understand. Overall it is a great book to brush up your concepts of statistics.
就像其他第一本的书一样，这本书的语气和可亲，易于交谈，因此您在阅读几页后不会感到无聊。 本书涵盖了第一年统计中涵盖的一系列主题，这些主题对于数据科学至关重要。 本书通过提供引人入胜的，发人深省的材料，包括视觉辅助和现实生活中的示例，将通常枯燥的主题带入生活。 在本书中，您将从描述性统计的主题(均值，中位数，众数，标准差，方差)开始，然后转向相关性，回归等推论统计。 它还包括对正态，二项式，泊松，几何概率分布的详尽解释。 除此之外，本书还包含许多图片和图形，使统计主题易于理解。 总的来说，这是一本精通统计概念的好书。
Grab a copy on Amazon.
在Amazon上获取副本。
10.模式识别与机器学习： (10. Pattern Recognition and Machine Learning:)
Author(s): Christopher M. Bishop
作者：Christopher M. Bishop
If you have already read a few books on Data Science and you are familiar with many machine learning algorithms, and you want to further improve your skills in this domain, then this is the book for you. This book dives deeper into machine learning algorithms and mathematics. The prerequisites for this book include familiarity with — linear and multivariate calculus, probability distributions, and a strong foundation of programming language. It is probably the best book to read if you are already familiar with machine learning and data science.
如果您已经阅读了几本有关数据科学的书籍，并且熟悉许多机器学习算法，并且想进一步提高这一领域的技能，那么这本书就是您的理想选择。 本书更深入地研究了机器学习算法和数学。 本书的前提条件包括熟悉—线性和多元演算，概率分布以及强大的编程语言基础。 如果您已经熟悉机器学习和数据科学，则可能是最好的阅读书。
Grab a copy on Amazon.
在Amazon上获取副本。
11.拐点： (11. Inflection Point:)
Author(s): Scott Stawski
作者：斯科特·斯托斯基(Scott Stawski)
If you are bored with the technical content of data science and want to know how data science is actually used in real-life businesses, then this is the perfect book for you. This book takes a break from the technical point of view of data science and focuses on the business perspective of it. If you really want to get further into the domain of Data Science and want to know how all of these things bind together, then this is a must-read for you as it encompasses the author’s experiences that show how data science actually works in real life.
如果您对数据科学的技术内容感到无聊，并且想知道数据科学在实际业务中的实际使用方式，那么这本适合您的书。 本书从数据科学的技术角度出发，重点介绍了它的业务角度。 如果您真的想进一步进入数据科学领域，并且想知道所有这些东西是如何结合在一起的，那么这对您来说是必读的，因为它涵盖了作者的经验，这些经验表明了数据科学在现实生活中实际上是如何工作的。
Grab a copy on Amazon.
在Amazon上获取副本。
最佳免费数据科学书籍： (Best Free Data Science Books:)
1.认为贝叶斯： (1. Think Bayes:)
Author(s): Allen B. Downey
作者：艾伦·唐尼
Think Bayes is an introduction to Bayesian statistics using computational methods. If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you’ll learn how to solve statistical problems with Python code instead of mathematical notation and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become more transparent, and you’ll begin to apply these techniques to real-world problems.
Think Bayes是使用计算方法的贝叶斯统计的简介。 如果您知道如何使用Python进行编程，并且对概率有所了解，那么您就可以准备处理贝叶斯统计了。 通过这本书，您将学习如何使用Python代码而不是数学符号来解决统计问题，以及如何使用离散概率分布而不是连续数学。 一旦您掌握了数学知识，贝叶斯基础知识将变得更加透明，您将开始将这些技术应用于实际问题。
Grab it for free on Green Tea Press.
在Green Tea Press上免费获取。
2. Python for Data Science手册： (2. Python for Data Science Handbook:)
Author(s): Jake VanderPlas
作者：杰克·范德普拉斯
If you are familiar with the basics of data science concepts, then this book is the best book to take your data science skills to the next notch. It includes a thorough explanation of python libraries for data analysis with code examples. Here is a few topics included in this book — utilizing Ipython and jupyter notebook in the best possible way, Numpy for efficient storage of data, pandas for manipulation and analysis of data, Matplotlib to visualize the data, scikit-learn to implement machine learning algorithms. In short, we can say that through this book, you will learn a lot about python libraries.
如果您熟悉数据科学概念的基础知识，那么这本书是使您的数据科学技能更上一层楼的最佳书。 它通过代码示例对用于数据分析的python库进行了详尽的解释。 这本书中包含一些主题-以最佳方式利用Ipython和jupyter笔记本，Numpy高效存储数据，Pandas进行数据处理和分析，Matplotlib可视化数据，scikit学习实现机器学习算法。 简而言之，我们可以说，通过本书，您将学到很多关于python库的知识。
Grab it for free on GitHub.
在GitHub上免费获取它。
结论： (Conclusion:)
We hope you love reading these books and gain some useful insights on data science out of it. If you come across any phenomenal books on data science such as the ones mentioned in this list, please let us know by emailing us.
我们希望您喜欢阅读这些书，并从中获得一些有关数据科学的有用见解。 如果您遇到任何有关数据科学的惊人书籍，例如此列表中提到的那些书籍，请给我们发送电子邮件，让我们知道。
Thank you for reading!
感谢您的阅读！
翻译自: https://medium.com/towards-artificial-intelligence/best-data-science-books-free-and-paid-data-science-book-recommendations-b519046dcca5
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