python 数据科学书籍_您必须在2020年阅读的数据科学书籍
python 數(shù)據(jù)科學(xué)書籍
“We’re entering a new world in which data may be more important than software.” — - Tim O’Reilly
“我們正在進入一個新世界，在這個世界中，數(shù)據(jù)可能比軟件更重要。” --蒂姆·奧雷利
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.
數(shù)據(jù)科學(xué)行業(yè)的應(yīng)用正在Swift增長，并提供了非常有希望的未來。 為了能夠進入這一領(lǐng)域，必須具備各種概念 ， 技術(shù)并具有豐富的經(jīng)驗，可以使用多種工具來完成這項工作。
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.
有數(shù)百種可用資源，包括在線課程，網(wǎng)站，視頻和書籍 ，可以使您牢牢掌握這一主題，因為乍一看似乎令人生畏。 在整篇文章中，我們都會提到一些學(xué)習(xí)數(shù)據(jù)科學(xué)和相關(guān)技術(shù)的最佳書籍，這將使學(xué)習(xí)變得輕而易舉。
Data science is the discipline of making data useful
數(shù)據(jù)科學(xué)是使數(shù)據(jù)有用的學(xué)科
數(shù)據(jù)科學(xué)書籍 (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.
在本節(jié)中，我們將重點介紹各種技能水平各異的數(shù)據(jù)科學(xué)書籍，以鞏固您對該領(lǐng)域的知識。 通過深入了解各種算法 ， 概念，方法以及受支持的編程語言 及其相關(guān)程序包，以充分利用它們 ，這些書將對幫助您學(xué)習(xí)這項新技能至關(guān)重要。
Disclaimer: There are no affiliate links in this post. This post is for information purposes only.
免責(zé)聲明： 這篇文章中沒有會員鏈接。 這篇文章僅供參考。
1. Python機器學(xué)習(xí)簡介：數(shù)據(jù)科學(xué)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
發(fā)行人 — O'Reilly
Difficulty Level: Beginners
難度等級：初學(xué)者
Get Book here — Amazon
在這里獲取書 — 亞馬遜
Cover of the book “Introduction to Machine Learning with Python”《 Python機器學(xué)習(xí)入門》一書的封面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.
機器學(xué)習(xí)是一種新的編程范例，是一種將您的愿望傳達給計算機的新方式。 令人興奮的是，它使您可以自動化無法完成的工作。
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.
本書以適合初學(xué)者的方式涵蓋了各種機器學(xué)習(xí)主題 ，向他們展示了如何輕松地開始構(gòu)建自己的機器學(xué)習(xí)解決方案 。 它還詳細介紹了學(xué)習(xí)和應(yīng)用機器學(xué)習(xí)來解決常見問題而無需參加高級數(shù)學(xué)課程的最佳實踐。
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.
這本介紹性書籍涵蓋了基礎(chǔ)概念 ，以及用于模型評估和scikit-learn的算法和一些高級方法 ，scikit-learn是一種經(jīng)過實踐檢驗的Python工具，可作為本書的補充，以提供更多有關(guān)機器學(xué)習(xí)實施的實際經(jīng)驗。
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
發(fā)行人 — O'Reilly
Difficulty Level: Beginners
難度等級：初學(xué)者
Get Book here — Amazon
在這里獲取書 — 亞馬遜
Read the Book Online — https://r4ds.had.co.nz/
在線閱讀書籍-https : //r4ds.had.co.nz/
Cover of the book “R for Data Science”“數(shù)據(jù)科學(xué)的R”一書的封面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是了解大量孤立數(shù)據(jù)的重要工具，該書旨在指導(dǎo)讀者如何充分利用R for Data Science。 本書的主題遵循數(shù)據(jù)科學(xué)的核心步驟，包括使用R編程語言導(dǎo)入，整理，轉(zhuǎn)換，可視化和建模數(shù)據(jù) 。
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.
該書要求一定程度的R的先驗知識，以及諸如dydyverse之類的軟件包以及一定程度的數(shù)字素養(yǎng)。 盡管它沒有涵蓋整個數(shù)據(jù)科學(xué)領(lǐng)域，但作者提供了許多其他資源，可以廣泛涵蓋所包含的主題。
3. 裸體統(tǒng)計 (3. Naked Statistics)
Author: Charles Wheelan
作者：查爾斯·惠蘭
Publisher — W. W. Norton & Company; Reprint edition
發(fā)行人 — WW Norton＆Company； 重印版
Difficulty Level: Beginners
難度等級：初學(xué)者
Get Book here — Amazon
在這里獲取書 — 亞馬遜
Cover of the book “Naked Statistics”《裸體統(tǒng)計》一書的封面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.
本書以有趣而有趣的方式論述了數(shù)據(jù)科學(xué)這一主題， 并通過將其與實際場景聯(lián)系起來，解釋了該主題的核心概念。 該書旨在以喜劇的方式提供來自統(tǒng)計學(xué)界的令人難以置信的內(nèi)容，同時也激發(fā)了讀者對這一主題的深入研究。
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.
作者涵蓋的一些概念包括推理，回歸分析，中心極限定理，反向因果關(guān)系，積極的出版偏見。 盡管它需要一定程度的統(tǒng)計經(jīng)驗，但是它以非常獨特的方式成功地交付了預(yù)期的知識。
“It’s easy to lie with statistics, but it’s hard to tell the truth without them.”― Charles Wheelan
“很容易撒謊統(tǒng)計，但是如果沒有它們，很難說出真相。”- 查爾斯·惠蘭
Read this too —
也閱讀此書-
4. 數(shù)據(jù)科學(xué)家實用統(tǒng)計 (4. Practical Statistics for Data Scientists)
Author: Andrew Bruce, Peter C. Bruce, and Peter Gedeck
作者：安德魯·布魯斯(Andrew Bruce)，彼得·布魯斯(Peter C. Bruce)和彼得·格德克(Peter Gedeck)
Publisher — O′Reilly
發(fā)行人 — O'Reilly
Difficulty Level: Intermediate
難度等級：中級
Get Book here — Amazon
在這里獲取書 — 亞馬遜
Cover of the book “Practical Statistics for Data Scientists”《數(shù)據(jù)科學(xué)家實用統(tǒng)計學(xué)》一書的封面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.
本書最好針對具有R和統(tǒng)計學(xué)編程語言經(jīng)驗的數(shù)據(jù)科學(xué)專業(yè)人員 ，以便捷的方式介紹該主題的基本概念，以促進學(xué)習(xí)。 它還強調(diào)了數(shù)據(jù)科學(xué)和統(tǒng)計學(xué)領(lǐng)域各種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.
面向數(shù)據(jù)科學(xué)家的實用統(tǒng)計資料通過將其與過去和最近與數(shù)據(jù)科學(xué)行業(yè)相關(guān)的實用示例相關(guān)聯(lián) ，從而解釋了該主題的核心概念。 即使本書涵蓋了大多數(shù)概念，即使不是全部，它也建議您閱讀其他內(nèi)容。
5. 用于數(shù)據(jù)分析的Python (5. Python for Data Analysis)
Author: Wes McKinney
作者：韋斯·麥金尼
Publisher — O′Reilly
發(fā)行人 — O'Reilly
Difficulty Level: Intermediate
難度等級：中級
Get Book here — Amazon
在這里獲取書 — 亞馬遜
Cover of the book “Python for Data Analysis”《用于數(shù)據(jù)分析的Python》一書的封面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.
就像這本書的書名所暗示的那樣，它主要關(guān)注Python for Data Analysis的實際實現(xiàn) ，主要分析以各種形式存儲的結(jié)構(gòu)化數(shù)據(jù)。 它詳細介紹了Python的作用，它廣泛的用于數(shù)據(jù)分析相關(guān)任務(wù)的庫以及它為數(shù)據(jù)科學(xué)提供的好處。
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.
本書涵蓋的基本Python庫包括NumPy，pandas，matplotlib，IPython和SciPy 。 作者從IPython開始，并在此過程中包括了其余的庫。
It also covers the fundamentals of Python programming as a quick refresher for readers with little to no Python programming experience.
它還涵蓋了Python編程的基礎(chǔ)知識，可以幫助那些幾乎沒有Python編程經(jīng)驗的讀者快速復(fù)習(xí)。
“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
“不采取行動； 毫不費力地工作。 想想大小一樣，少則多。 面對困難，卻仍然容易； 通過一系列小動作來完成偉大的任務(wù)。 -老子”- 韋斯·麥金尼
6.深度學(xué)習(xí) (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 —麻省理工學(xué)院出版社
Difficulty Level: Beginners
難度等級：初學(xué)者
Get Book here — Amazon
在這里獲取書 — 亞馬遜
Cover of the book “Deep Learning”《深度學(xué)習(xí)》一書的封面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.
本質(zhì)上針對的是學(xué)習(xí)機器學(xué)習(xí)，深度學(xué)習(xí)和人工智能的大學(xué)生以及那些想快速學(xué)習(xí)機器學(xué)習(xí)的程序員。 本書涵蓋了機器學(xué)習(xí)的所有入門部分，包括數(shù)學(xué)部分 ，并深入到深度網(wǎng)絡(luò) ，涵蓋了深度學(xué)習(xí)和深度生成模型。 作者提到了大量的見解，以了解什么是機器學(xué)習(xí)以及如何將其實施以解決現(xiàn)代問題。
“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject”
“深度學(xué)習(xí)由該領(lǐng)域的三位專家撰寫，是關(guān)于該主題的唯一綜合性書籍”
? — Elon Musk, cofounder and CEO of Tesla and SpaceX
?— Elon Musk，Tesla和SpaceX的聯(lián)合創(chuàng)始人兼首席執(zhí)行官
7. 使用Scikit-Learn和TensorFlow進行動手機器學(xué)習(xí) (7. Hands-On Machine Learning with Scikit-Learn and TensorFlow)
Author: By Aurélien Géron
作者： AurélienGéron
Publisher — O’Reilly Media
發(fā)行人 — O'Reilly Media
Difficulty Level: Beginners
難度等級：初學(xué)者
Get Book here — Amazon
在這里獲取書 — 亞馬遜
Cover of the book “Hands-On Machine Learning with Scikit-Learn and TensorFlow”“使用Scikit-Learn和TensorFlow進行動手機器學(xué)習(xí)”一書的封面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.
如果您對機器學(xué)習(xí)的知識為零，那么本書將是您的正確選擇，因為它承擔(dān)著為您配備正確的工具，概念，知識和思維方式的任務(wù)，以了解什么是機器學(xué)習(xí)。 作者介紹了本主題中包含的各種技術(shù)，并在許多可用于生產(chǎn)的工具和環(huán)境(例如Python的TensorFlow，Scikit-Learn和Keras)的幫助下進行了解釋。
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.
該書旨在通過各種示例提供有關(guān)該主題的更多動手經(jīng)驗 ，同時減少對理論內(nèi)容的關(guān)注，并鼓勵其讀者更深入地研究實際實現(xiàn) 。
“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
“在機器學(xué)習(xí)中，這被稱為過度擬合：這意味著該模型在訓(xùn)練數(shù)據(jù)上表現(xiàn)良好，但不能很好地泛化。”-AurélienGéron
查看本書的第二版- (Check out the 2nd edition of the book —)
8.統(tǒng)計學(xué)習(xí)導(dǎo)論 (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
發(fā)行人 —施普林格
Difficulty Level: Beginners
難度等級：初學(xué)者
Get Book here — Amazon
在這里獲取書 — 亞馬遜
Cover of the book “Introduction to Statistical Learning”《統(tǒng)計學(xué)習(xí)入門》一書的封面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.
本書可作為統(tǒng)計學(xué)習(xí)的指南，從本質(zhì)上講 ，它轉(zhuǎn)換為用于建模和理解數(shù)據(jù)的一組工具。 涵蓋了本主題中的各種技術(shù)，該書更加強調(diào)了幾個概念的實際應(yīng)用 ，而不是其數(shù)學(xué)實現(xiàn)。
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.
它以更簡單和動手的方式成功交付了一些復(fù)雜的主題，通過包含R編程語言來促進學(xué)習(xí)過程。 確實需要了解統(tǒng)計術(shù)語和概念才能充分利用本書。
9. Python數(shù)據(jù)科學(xué)手冊 (9. Python Data Science Handbook)
Author: Jake VanderPlas
作者：杰克·范德普拉斯
Publisher — O’Reilly Media
發(fā)行人 — O'Reilly Media
Difficulty Level: Intermediate
難度等級：中級
Get Book here — Amazon
在這里獲取書 — 亞馬遜
Git Hub — https://github.com/jakevdp/PythonDataScienceHandbook
Git Hub- https://github.com/jakevdp/PythonDataScienceHandbook
Cover of the book “Python Data Science Handbook”《 Python數(shù)據(jù)科學(xué)手冊》的封面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.
這本便捷的書的目的不是將數(shù)據(jù)科學(xué)中的各種概念呈現(xiàn)為一個全新的領(lǐng)域，而不僅僅是一個全新的領(lǐng)域。 根據(jù)作者的說法，數(shù)據(jù)科學(xué)可以最好地解釋為黑客技能 ，某個領(lǐng)域的豐富專業(yè)知識以及該領(lǐng)域的數(shù)學(xué)和統(tǒng)計知識之間的交叉點。
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.
該書假定讀者具有Python的基本經(jīng)驗，可以創(chuàng)建和管理Python程序的流程，因此，主要側(cè)重于講授Python的實現(xiàn)及其在Data Science中值得注意的庫堆棧。
10. Scratch的數(shù)據(jù)科學(xué) (10. Data Science from Scratch)
Author: Joel Grus
作者：喬爾·格魯斯(Joel Grus)
Publisher — O’Reilly Media
發(fā)行人 — O'Reilly Media
Difficulty Level: Beginners
難度等級：初學(xué)者
Get Book here — Amazon
在這里獲取書 — 亞馬遜
Cover of the book “Data Science from Scratch”《從零開始的數(shù)據(jù)科學(xué)》一書的封面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.
如果您想了解各種算法 ， 庫，框架和其他一般工具包如何在Data Science中正常工作，那么這本適合您的書。 本書并沒有先講授數(shù)據(jù)科學(xué)的核心方面，而是采取了相反的方法，從使數(shù)據(jù)科學(xué)成為可能的工具的最基本基礎(chǔ)開始，并逐步觸及了數(shù)據(jù)科學(xué)的各種概念。 本書的先決條件包括對數(shù)學(xué)和編程技能的事先了解。
“Just run: pip install ipython and then search the Internet for solutions to whatever cryptic error messages that causes.”― Joel Grus
“只需運行：pip安裝ipython，然后在Internet上搜索導(dǎo)致任何隱秘錯誤消息的解決方案。”- Joel Grus
11.思考統(tǒng)計 (11. Think Stats)
Author: Allen B. Downey
作者：艾倫·唐尼
Publisher — O’Reilly Media
發(fā)行人 — O'Reilly Media
Difficulty Level: Beginners
難度等級：初學(xué)者
Get Book here — Amazon
在這里獲取書 — 亞馬遜
Cover of the book “Think Stats”《思考統(tǒng)計》一書的封面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為探索性數(shù)據(jù)分析提供了實用工具的介紹，并遵循了作者的數(shù)據(jù)處理方式。 這本書遵循了計算方法，而不是傳統(tǒng)的數(shù)學(xué)方法，其主要原因是鼓勵讀者使用Python代碼來提高可讀性和清晰度。
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.
本書的思想是提出一種基于項目的方法 ，讀者可以選擇一個統(tǒng)計問題，一個數(shù)據(jù)集，并將所學(xué)的每種技術(shù)應(yīng)用于該數(shù)據(jù)集。
The author has also mentioned numerous freely available external references for the topics that require them, such as Wikipedia.
作者還提到了許多免費的外部參考資料，以供需要它們的主題使用，例如Wikipedia。
12.使用Python進行深度學(xué)習(xí) (12. Deep Learning with Python)
Author: Fran?ois Chollet
作者： Fran?oisChollet
Publisher — Manning Publications
出版商 —曼寧出版物
Difficulty Level: Expert
難度等級：專家
Get Book here — Amazon
在這里獲取書 — 亞馬遜
Cover of the book “Deep Learning with Python”“用Python進行深度學(xué)習(xí)”這本書的封面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進行深度學(xué)習(xí)討論通過使用Python及其庫Keras使廣大讀者可以使用機器學(xué)習(xí)和深度學(xué)習(xí)。 本書涵蓋了人工智能，機器學(xué)習(xí)和深度學(xué)習(xí)的基本背景，然后重點介紹了Keras的深度學(xué)習(xí)實現(xiàn) 。
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.
然后作者繼續(xù)通過大量的代碼示例來介紹深度學(xué)習(xí)及其相關(guān)概念的實際應(yīng)用 。 由于它需要精通Python，因此它將是大多數(shù)具有技術(shù)能力的讀者(例如數(shù)據(jù)科學(xué)家，深度學(xué)習(xí)專家和研究生)的合適選擇。
“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.”
“并非所有問題都能得到解決； 僅僅因為您已經(jīng)組合了輸入X和目標(biāo)Y的示例，并不意味著X包含足夠的信息來預(yù)測Y。例如，如果您要根據(jù)最近的價格歷史來預(yù)測股票在股票市場的走勢，您不太可能成功，因為價格歷史記錄沒有太多的預(yù)測信息。”
― Francois Chollet,
―弗朗索瓦·喬萊特
更多數(shù)據(jù)科學(xué)書籍可供閱讀— (More Data Science Books to Read —)
- Pattern recognition and machine learning 模式識別和機器學(xué)習(xí)
- Practical data science with R R的實用數(shù)據(jù)科學(xué)
- Python Machine Learning By Example Python機器學(xué)習(xí)實例
- Think Python 考慮Python
- The Elements of Statistical Learning 統(tǒng)計學(xué)習(xí)的要素
- Think Bayes — Bayesian Statistics Made Simple 貝葉斯思考—貝葉斯統(tǒng)計簡化
- Designing Data-Intensive Applications 設(shè)計數(shù)據(jù)密集型應(yīng)用
結(jié)論 (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.
數(shù)據(jù)科學(xué)是一個廣闊的行業(yè)，包含許多功能強大且高效的工具，可以對數(shù)據(jù)執(zhí)行各種任務(wù)。 有抱負的數(shù)據(jù)科學(xué)家應(yīng)具有這些工具的專業(yè)知識，可以圍繞數(shù)據(jù)工作，以實現(xiàn)性能驅(qū)動的結(jié)果。 通過將您的注意力吸引到一些最佳的數(shù)據(jù)科學(xué)書籍上，我們希望鼓勵任何正在尋找數(shù)據(jù)科學(xué)和機器學(xué)習(xí)切入點的人。 這些書籍無疑是一些最好的書籍，它們不僅可以增強您對數(shù)學(xué)的知識，而且可以增強您在本書中使用的幾種編程語言和庫的知識。
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： 為消除各種問題，我謹在此提醒您，本文僅代表我要分享的個人觀點，您擁有反對該觀點的一切權(quán)利。
更有趣的讀物— (More Interesting Readings —)
I hope you’ve found this article useful! Below are some interesting readings hope you like them too-
希望本文對您有所幫助！ 以下是一些有趣的讀物，希望您也喜歡它們-
About Author
關(guān)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.
克萊爾·D 。 是 Digitalogy 的Content Crafter and Marketinger ，這 是一個技術(shù)采購和自定義配對市場，可根據(jù)人們在全球的特定需求，將人們與預(yù)先篩選和一流的開發(fā)商和設(shè)計師聯(lián)系起來。 在 Linkedin ， Twitter ， Instagram 上 與 Digitalogy聯(lián)系 。
翻譯自: https://towardsdatascience.com/data-science-books-you-must-read-in-2020-1f30daace1cb
python 數(shù)據(jù)科學(xué)書籍
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