數(shù)據(jù)結(jié)構(gòu)入門最佳書籍

Introduction

介紹

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.

我被很多人問到了我為想要開始數(shù)據(jù)科學(xué)之旅的人們推薦哪些資源。 本節(jié)列出了一些書，我建議您作為數(shù)據(jù)科學(xué)家一生中至少應(yīng)閱讀一遍。

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.

您需要閱讀這些書才能學(xué)習(xí)成為一名數(shù)據(jù)科學(xué)家嗎？ 答案是不。 在線上有很多教程和免費資料，與這些書籍一樣好。 但是，如果您有能力購買它們并可以閱讀它們作為補充材料，那么它們可以成為學(xué)習(xí)的很好資源。 與在線教程不同，這些書具有結(jié)構(gòu)化和以有組織和結(jié)構(gòu)化的方式講授概念。 這意味著您可以花時間學(xué)習(xí)，而不是浪費時間在互聯(lián)網(wǎng)上尋找好的教程。

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.

我在這里推薦的書涵蓋了您作為數(shù)據(jù)科學(xué)家需要掌握的主要主題：編程(python)，數(shù)據(jù)分析和機器學(xué)習(xí)(包括深度學(xué)習(xí))。 我知道每個主題都有很多書，但是這些都是我在學(xué)習(xí)過程中使用的書，我可以真正推薦它們。

Python Programming

Python編程

Amazon (affiliate link)亞馬遜 (會員鏈接)

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.

作為數(shù)據(jù)科學(xué)家，您應(yīng)該首先是一名優(yōu)秀的程序員，或者至少要努力實現(xiàn)至少一種語言的編程能力。 我建議學(xué)習(xí)python，以了解它在數(shù)據(jù)科學(xué)中的常用用法以及相對簡單的學(xué)習(xí)曲線。

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

這本書就像Python圣經(jīng)。 它大約有1600頁，涵蓋了所有基本和更高級的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.

對于從python開始的人來說，這是一本好書，因為它對語言和編程概念有深入的說明，并且內(nèi)容以簡單易懂的方式呈現(xiàn)。

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.

對于已經(jīng)使用python一段時間但想要更好地使用它，提高對語言和通用概念(尤其是面向?qū)ο缶幊?的理解的人來說，這將是一個很好的修訂。

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

您可以從這里獲得這本書(會員鏈接)。

Data Analysis

數(shù)據(jù)分析

Amazon (affiliate link)亞馬遜 (會員鏈接)

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?

本書涵蓋了幾乎所有涉及數(shù)據(jù)分析，數(shù)據(jù)清理以及使用熊貓進行數(shù)據(jù)預(yù)處理的內(nèi)容。 數(shù)據(jù)科學(xué)在大多數(shù)情況下會做什么？

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，幸運的是，我們大部分時間都在準備數(shù)據(jù)以適合機器學(xué)習(xí)算法。 本書涵蓋了所有內(nèi)容，并且足夠供數(shù)據(jù)分析人員或初級數(shù)據(jù)科學(xué)家使用python，以熟悉流行于數(shù)據(jù)分析的程序和庫。

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，這本書是由熊貓包裝的作者韋斯·麥金尼(Wes McKinney)撰寫的。 如果不是創(chuàng)建的最受歡迎的python數(shù)據(jù)分析庫之一的作者，誰將是學(xué)習(xí)數(shù)據(jù)分析的最佳人選。

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

您可以從這里獲得這本書(會員鏈接)。

Machine Learning

機器學(xué)習(xí)

Amazon (affiliate link).亞馬遜 (會員鏈接)。

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

如果您只購買一本有關(guān)機器學(xué)習(xí)的書，那將是我的選擇。

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.

它可能是一本針對初學(xué)者數(shù)據(jù)科學(xué)家的書，該書希望概述機器學(xué)習(xí)算法以及如何使用scikit-learn在實際示例中實現(xiàn)它們。

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.

對于已經(jīng)熟悉機器學(xué)習(xí)概念并且想要一本書以便快速參考和復(fù)習(xí)的人來說，這也是一個很好的修訂。

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

此外，它還有一個精彩的第二部分，重點介紹了使用Keras和TensorFlow進行深度學(xué)習(xí)。

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

您可以從這里獲得這本書(會員鏈接)。

Other topics in Data Science

數(shù)據(jù)科學(xué)中的其他主題

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.

成為數(shù)據(jù)科學(xué)家不僅僅涉及python編程，數(shù)據(jù)分析和機器學(xué)習(xí)。 在這個專業(yè)中，您還應(yīng)該掌握其他主題。 我想到的第一個領(lǐng)域是數(shù)學(xué)和統(tǒng)計學(xué)。

?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.

``我不推薦任何有關(guān)這些主題的書，因為我一直依賴于我的高中和大學(xué)知識，并向這些知識提供在線教程和資源。 如果我閱讀了有關(guān)這些主題的好書，則將更新此列表。

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

本來在發(fā)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：我寫的文章在 aboutdatablog.com 上以簡單易懂的方式解釋了基本的數(shù)據(jù)科學(xué)概念 。 如果您喜歡這篇文章，您可能還會喜歡其他一些文章：

翻譯自: https://towardsdatascience.com/best-data-science-books-be1ab472876d

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