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  • 数据科学书籍

    2017-12-31 11:11:46
    数据科学书籍,包含数据可视化之美,数据挖掘导论,大数据时代的信号与噪声:大数据时代预测的科学与艺术(美)纳特·西尔弗
  • app 付费数据数据科学,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....

    app 付费数据

    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 pub@towardsai.net.

    披露:我们Towards AI的编辑团队撰写真实可信的评论,并且可能会因我们选择支持Towards AI的产品而获得少量补偿。 对于本文,作为合格的Amazon Associate Towards AI可能会从合格的购买中获得少量佣金。 对于反馈,问题或疑虑,请给我们发送电子邮件pub@towardsai.net

    📚 Check out our Moment Generating Function Tutorial with Python. 📚

    📚查看我们的Python矩生成函数教程。 📚

    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

    | Best Data Science Books | Data Science Books | Best Data Science Books | Data Science Books
    Amazon 亚马逊

    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.

    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)

    Introduction to Machine Learning with Python | Source: Amazon | Best Data Science Books | Data Science Books
    Amazon 亚马逊

    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.

    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)

    Image for post
    Amazon 亚马逊

    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.

    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

    Introduction to Probability | Source: Amazon | Best Data Science Books | Data Science Books
    Amazon 亚马逊

    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.

    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

    Data Science from Scratch | Source: Amazon | Best Data Science Books | Data Science Books
    Amazon 亚马逊

    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.

    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

    Naked Statistics | Source: Amazon | Best Data Science Books | Data Science Books
    Amazon 亚马逊

    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.

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    7. Python进行数据分析(7. Python for Data Analysis:)

    Author(s): Wes McKinney

    作者:韦斯·麦金尼

    Python for Data Analysis | Source: Amazon | Best Data Science Books | Data Science Books
    Amazon 亚马逊

    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.

    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

    Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow | Source: Amazon | Best Data Science Books | Data Science
    Amazon 亚马逊

    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.

    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)

    Head First Statistics | Source: Amazon | Best Data Science Books | Data Science Books
    Amazon 亚马逊

    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.

    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

    Pattern Recognition and Machine Learning | Source: Amazon | Best Data Science Books | Data Science Books
    Amazon 亚马逊

    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.

    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)

    Inflection Point | Source: Amazon | Best Data Science Books | Data Science Books
    Amazon 亚马逊

    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.

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    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 | Source: Green Tea Press | Best Data Science Books | Data Science Books
    Green Tea Press 绿茶出版社

    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.

    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

    Python for Data Science Handbook | Source: GitHub | Best Data Science Books | Data Science Books
    GitHub GitHub

    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.

    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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  • 很棒的Python数据科学书籍 可能是Python中最好的精选数据科学书籍清单。 内容 统计数据 -彼得·布鲁斯和安德鲁·布鲁斯了解如何将各种统计方法应用于数据科学,以及如何避免滥用它们。 了解什么统计概念很重要,...
  • python 数据科学书籍As things stand, I am nowhere near where I aspire to reach as a Data Scientist. In my journey so far, I have met many helpful people and come across various useful resources.就目前...

    python 数据科学书籍

    As things stand, I am nowhere near where I aspire to reach as a Data Scientist. In my journey so far, I have met many helpful people and come across various useful resources.

    就目前情况而言,作为数据科学家,我离我想要达到的目标还很遥远。 到目前为止,在我的旅途中,我遇到了许多乐于助人的人,并且遇到了各种有用的资源。

    Whilst I pondered on where I have come from and where I am now, I instantly remembered the 4 books that had been revolutionary for my progress as a Data Scientist — Of which I will be sharing with you today.

    当我思考自己的来历和现在的位置时,我立即想起了四本书,这些书对于我作为数据科学家的发展具有革命性意义-我今天将与大家分享这些。

    Note: There are no affiliate links associated to any of my recommendations and I am in no agreement with any of the authors.

    注意:没有与我的任何建议相关的会员链接,并且我与任何作者均未达成协议。

    《 Python旅行者指南》 (The Hitchhikers Guide to Python)

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    This is an unusual book to start with! Not because it’s controversial or anything, but simply because it is not directly about Data Science.

    这是一本不寻常的书! 不是因为它有争议或其他原因,而是因为它与数据科学无关。

    Python is a popular language among Data Scientist (as well as R), and this book quite literally can take you to expert levels. The book describes the best practices to follow when programming in Python and if you read Data Scientist should know Software Engineering best practices, you’d know this is more important than ever.

    Python是数据科学家(以及R)中一种流行的语言,而这本书确实可以带您进入专家水平。 这本书描述了使用Python进行编程时应遵循的最佳实践,如果您阅读了《数据科学家》一书,就应该了解软件工程的最佳实践,那么您就会知道这比以往任何时候都重要。

    More and more Data Science projects are being taken into production which creates a heavy demand on Data Scientist to have more end-to-end skills. This book is a great starter.

    越来越多的数据科学项目正在投入生产,这对数据科学家提出了更高的要求,要求他们拥有更多的端到端技能。 这本书是一个很好的开始。

    You may download this book for free in the link below!

    您可以在下面的链接中免费下载这本书!

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

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    Jake VanderPlas hit the nail on it’s head with the naming of this book ; It’s one of those “Never throw this book away” books.

    杰克·范德普拉斯(Jake VanderPlas)用这本书的名字打在了他的头上。 这是那些“永不丢掉这本书”的书之一。

    From NumPy, to Pandas, to Matplotlib and Machine Learning, Mr. VanderPlas gives a comprehensive overview for how we can face day-to-day challeneges such as manipulating, transforming, and cleaning data; visualizing various kinds of data; and using data to build statistical or machine learning models.

    从NumPy到Pandas,再到Matplotlib和机器学习,VanderPlas先生全面概述了我们如何面对诸如处理,转换和清理数据之类的日常挑战。 可视化各种数据; 并使用数据建立统计或机器学习模型。

    The most Ideal, easy to read reference book!

    最理想,易读的参考书!

    使用Scikit-Learn和Tensorflow进行机器学习 (Hands On Machine Learning With Scikit-Learn and Tensorflow)

    Note: It has been brought to my attention that this book is copyrighted, hence technically not making it free.

    注意:引起我注意的是这本书是受版权保护的,因此从技术上讲不会免费提供它。

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    The explanations of concepts and coding examples are very intuitive and useful people that want to begin working on personal projects.

    概念和编码示例的说明非常直观并且非常有用,他们希望开始从事个人项目。

    In fact, in my opinion, I believe that after reading (or whilst you read) this book it is imperative that you go and build something with your new knowledge.

    实际上,我认为,在阅读(或阅读时)读完这本书之后,您必须去使用新知识来构建新书。

    It is a very practical book and you should most definitely take your time to answer the questions!

    这是一本非常实用的书,您绝对应该花时间回答问题!

    The free link to Hands on Machine Learning with Scikit-Learn and TensorFlow.

    免费链接到使用Scikit-Learn和TensorFlow进行动手学习

    统计学习的要素 (The Elements of Statistical Learning)

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    Personal Advice? Don’t read this before bed! You want to read it when you have the most energy.

    个人建议? 睡前不要读这篇! 您想在能量最大的时候阅读它。

    This book is by far the most complex of all the books I’ve suggested and also one that you will be referring back to very often. It dives deep into statistics and is written in quite a complicated manner since it’s heavy on the Math behind many things we will encounter as a Data Scientist.

    到目前为止,这本书是我所建议的所有书籍中最复杂的一本书,也是您将经常引用的一本书。 它深入统计资料,并且以相当复杂的方式编写,因为它把重点放在了数学上,这是我们作为数据科学家将遇到的许多事情的背后。

    I’ve put this book here not to show off as if I am smart — I am not, but I can learn anything I want to, just like you! — but to set the standard. If we want to become top Data Scientist, we have to be willing to do things that most people aren’t willing to do such as diving deep into the underpinnings of what holds our field together.

    我把这本书放在这里并不是要炫耀自己,好像我很聪明-我不是,但是我可以学到任何想要的东西,就像你一样! -但要设定标准。 如果我们想成为顶尖的数据科学家,我们必须愿意做大多数人不愿意做的事情,例如深入研究将我们的领域团结在一起的基础。

    Note: If you aren’t from a Mathematical background, I highly recommend you gain some exposure to Machine Learning algorithms in simpler ways, such as implementing them, before trying to read this one.

    注意:如果您不是数学背景的人,我强烈建议您在尝试阅读机器学习算法之前,以更简单的方式(例如实现它们)来学习一下机器学习算法。

    Don’t fear the Math, Read The Elements of Statistical Learning.

    不要害怕数学,请阅读《统计学习的要素》

    机器学习向往 (Machine Learning Yearning)

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    I am quite surprised this book isn’t spoken of as much as some of the other books on this list. Andrew Ng, the Founder of Coursera and the instructor of some of the best courses on Machine Learning and Deep Learning, curated his most valuable lessons from working at companies such as Google and Baidu and put them on paper for us to leech from.

    我很惊讶这本书没有像清单上的其他书籍那样多被提及。 Coursera的创始人,一些最佳的机器学习和深度学习课程的讲师Ng的安德鲁·伍兹(Andrew Ng)总结了他在Google和百度等公司工作的最宝贵的经验教训,并将其写在纸上供我们借鉴。

    In my personal opinion, after reading a book like ____ which provides us with a practical way of using machine learning, this book is the best book for a super-set. It is about being a great team member which includes being productive, efficient and a great team player (in-case it wasn’t obvious).

    我个人认为,在阅读____这样的书之后,该书为我们提供了一种使用机器学习的实用方法,这本书是超级集的最佳书。 这是关于成为一个出色的团队成员,包括高效,高效和出色的团队合作者(以防万一)。

    Note: To get the free book, you will be asked to fill in some details and it will be emailed to you upon completion.

    注意:要获取免费书籍,系统会要求您填写一些详细信息,并在完成后通过电子邮件发送给您。

    奖励书 (Bonus Book)

    As agreed, I’ve given you 4 free books that will take your Data Science skills to the next level. However, as a bonus I will provide __ more books that will take your Data Science skills to the next level — the only difference with th(ese)is book(s) is that they aren’t free. But hey! There is nothing wrong with paying for good quality.

    按照约定,我为您提供了4本免费书籍,这些书将使您的数据科学技能更上一层楼。 但是,作为奖励,我将提供__更多本书,这些书将使您的数据科学技能更上一层楼–与这些语言的唯一区别是书不是免费的。 但是,嘿! 支付高质量没有错。

    解决(几乎)任何机器学习问题 (Approaching (Almost) Any Machine Learning Problem)

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    Purchase on Amazon 在亚马逊上购买

    If I had a girlfriend, she’d probably be jealous of my admiration of Abhishek Thakur’s work. One of the most hands on people I have ever come across, it makes sense why he is a Kaggle 4x Grandmaster!

    如果我有女朋友,她可能会嫉妒我对Abhishek Thakur的工作的钦佩。 我遇到过的最忙碌的人之一,为什么他是Kaggle 4x Grandmaster很有意义!

    He has released a book that gives us an insight into his world and honestly, it has been one of the best reads — in terms of the amount of practical knowledge I derived from it — this year for me.

    他发行了一本书,使我们对他的世界有一个深刻的了解。老实说,这是我今年最好的读物之一(就我从中获得的实践知识而言)。

    Note: The book requires you have good knowledge of Machine Learning so get yourself up to scratch and pick up this bad boy!

    注意:这本书要求您具有良好的机器学习知识,因此请抓紧时间来学习这个坏男孩!

    Purchase Approaching (Almost) Any Machine Learning Problem on Amazon.

    购买(几乎)在亚马逊上遇到任何机器学习问题

    结语 (Wrap Up)

    I’ve summed up books that have taken me to new heights. I have not read all the books surrounding the Data Science field so it’s possible I’ve missed a very important book. If you happen to think this is the case, leave a response with the books title and the free link to the pdf. Many Thanks!

    我总结了使我达到新高度的书。 我尚未阅读有关数据科学领域的所有书籍,因此有可能错过了一本非常重要的书。 如果您碰巧认为是这种情况,请在回复中附上书籍标题和pdf的免费链接。 非常感谢!

    Let’s continue the conversation on LinkedIn…

    让我们继续在LinkedIn上进行对话…

    翻译自: https://towardsdatascience.com/5-free-books-to-take-your-data-science-skills-to-the-next-level-a2026c8cad71

    python 数据科学书籍

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  • 数据科学导论 书籍 详情 书名 数据科学理论与实践(第2版) 出版社 清华大学出版社 作者 朝乐门 ISBN 9787302531913 下载地址 ------------ 书籍 详情 ...

    Data Science
    Book
    author:zoxiii


    数据科学导论

    书籍详情
    书名数据科学理论与实践(第2版)
    出版社清华大学出版社
    作者朝乐门
    ISBN9787302531913
    下载地址------------
    书籍详情
    书名数据科学导论:Python语言(原书第3版)
    出版社机械工业出版社
    作者阿尔贝托·博斯凯蒂,卢卡·马萨罗
    ISBN9787111646693
    下载地址------------

    时间序列分析

    书籍详情
    书名时间序列分析(上下两册)
    出版社中国人民大学出版社
    作者詹姆斯·D·汉密尔顿
    ISBN9787300202136
    下载地址------------
    书籍详情
    书名应用时间序列分析(第五版)
    出版社人民大学出版社
    作者王燕
    ISBN9787300270272
    下载地址------------

    云计算

    书籍详情
    书名云计算概念、技术与架构
    出版社机械工业出版社
    作者(美)埃尔
    ISBN9787111461340
    下载地址------------
    书籍详情
    书名云计算原理与实践
    出版社人民邮电出版社
    作者王伟
    ISBN9787115483034
    下载地址------------

    Hadoop大数据

    书籍详情
    书名Hadoop大数据技术原理与应用
    出版社清华大学出版社
    作者黑马程序员
    ISBN9787302524403
    下载地址------------
    书籍详情
    书名从零开始学hadoop大数据分析
    出版社机械工业出版社
    作者温春水、毕洁馨
    ISBN9787111619314
    下载地址------------
    书籍详情
    书名hadoop大数据技术与应用
    出版社人民邮电出版社
    作者杨治明等
    ISBN9787115503534
    下载地址------------
    书籍详情
    书名Hadoop权威指南-大数据的存储与分析(第4版)-
    出版社清华大学出版社
    作者汤姆·怀特
    ISBN9787302465133
    下载地址------------
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  • 数据结构入门最佳书籍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 ...

    数据结构入门最佳书籍

    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

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    Amazon (affiliate link) 亚马逊 (会员链接)

    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

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    Amazon (affiliate link) 亚马逊 (会员链接)

    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

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    Amazon (affiliate link). 亚马逊 (会员链接)。

    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:

    翻译自: https://towardsdatascience.com/best-data-science-books-be1ab472876d

    数据结构入门最佳书籍

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  • 数据科学图书馆练习 该存储库用于完成和正在进行的与数据科学相关的练习。
  • 数据科学家 VS 真数据科学

    千次阅读 2017-06-07 09:58:23
    如今数据科学书籍、认证和文凭,如雨后春笋般层出不穷。但许多仅仅是镜花水月:许多人钻了这一新名词的空子,将旧酒(比如统计学和R编程)放在了“数据科学”这个新瓶里。 本文选自《数据天才:数据科学家修炼之道》...
  • 数据科学作为一个新兴领域,植根于其他学科:统计推断、算法、统计模型、机器学习、实验设计、优化理论、概率论、人工智能、数据可视化和探索性数据分析等。每门学科都值得花好几门课或好几本书专门讲解,这正是写作...
  • 数据科学图书馆(LibDS) 为初学者和专家准备的以数据科学为主题的书籍清单(主要使用Python)。 显然,没有人可以阅读所有书籍,但是此清单可以帮助您决定探索哪本书以获得真正有用​​的知识。 :fire: 必读 ...
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