从零开始掌握数据科学：Python基础算法解析

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"《数据科学入门：从零开始》（Data Science from Scratch: FIRST PRINCIPLES WITH PYTHON）是由Joel Grus撰写的一本专业书籍，适合那些对数据科学充满好奇但缺乏基础知识的人。该书的ISBN号码是978-1-491-90142-7，定价分别为美国版US$39.99和加拿大版CAN$45.99。作者以其深入浅出的方式，引导读者从数据科学家应有的基础算法入手，带领读者在实践中学习和理解数据科学的核心数学和统计知识，以及编程技巧。在当今数据丰富的时代，数据科学的重要性日益凸显，它能帮助我们挖掘出隐藏在海量信息中的答案，解决前所未有的问题。这本书特别强调了从零开始构建数据科学工具和算法的重要性，让读者能够掌握如何通过编写原始代码来实现这些关键功能。即使没有深厚的数学背景或编程经验，只要具备一定的数学天分和编程技能，读者就能跟随作者逐步建立起对数据科学的扎实理解。书中内容包括但不限于数据清洗、预处理、模型选择、机器学习算法（如线性回归、决策树、聚类分析等）、深度学习基础，以及如何利用Python这一广泛应用于数据科学的编程语言进行实践。此外，Joel Grus还分享了他的个人见解和实用技巧，帮助读者在快速变化的数据科学领域保持竞争力。通过阅读《数据科学从零开始》，读者不仅能学会如何运用现有的数据科学工具，更能理解它们背后的原理，从而在未来的职业道路上更加游刃有余。如果你对探索数据的世界感兴趣，这本书将是你开启数据科学之旅的宝贵资源。无论你是初学者还是希望提升现有技能的专家，都能从中受益匪浅。同时，该书的出版商O'Reilly Media在社交媒体上提供支持，如datatau.com和Twitter/@oreillymedia，以及Facebook.com/oreilly，为读者提供了进一步的学习和交流平台。"

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Using Code Examples

Supplemental material (code examples, exercises, etc.) is available for download at

https://github.com/joelgrus/data-science-from-scratch.

This book is here to help you get your job done. In general, if example code is offered

with this book, you may use it in your programs and documentation. You do not

need to contact us for permission unless you’re reproducing a significant portion of

the code. For example, writing a program that uses several chunks of code from this

book does not require permission. Selling or distributing a CD-ROM of examples

from O’Reilly books does require permission. Answering a question by citing this

book and quoting example code does not require permission. Incorporating a signifi‐

cant amount of example code from this book into your product’s documentation does

require permission.

We appreciate, but do not require, attribution. An attribution usually includes the

title, author, publisher, and ISBN. For example: “Data Science from Scratch by Joel

If you feel your use of code examples falls outside fair use or the permission given

above, feel free to contact us at permissions@oreilly.com.

Safari® Books Online

Safari Books Online is an on-demand digital library that deliv‐

ers expert content in both book and video form from the

world’s leading authors in technology and business.

xiv | Preface

matter how you define data science, you’ll find practitioners for whom the definition

is totally, absolutely wrong.

Nonetheless, we won’t let that stop us from trying. We’ll say that a data scientist is

someone who extracts insights from messy data. Today’s world is full of people trying

to turn data into insight.

For instance, the dating site OkCupid asks its members to answer thousands of ques‐

tions in order to find the most appropriate matches for them. But it also analyzes

these results to figure out innocuous-sounding questions you can ask someone to

find out how likely someone is to sleep with you on the first date.

Facebook asks you to list your hometown and your current location, ostensibly to

make it easier for your friends to find and connect with you. But it also analyzes these

locations to identify global migration patterns and where the fanbases of different

football teams live.

As a large retailer, Target tracks your purchases and interactions, both online and in-

store. And it uses the data to predictively model which of its customers are pregnant,

to better market baby-related purchases to them.

In 2012, the Obama campaign employed dozens of data scientists who data-mined

and experimented their way to identifying voters who needed extra attention, choos‐

ing optimal donor-specific fundraising appeals and programs, and focusing get-out-

the-vote efforts where they were most likely to be useful. It is generally agreed that

these efforts played an important role in the president’s re-election, which means it is

a safe bet that political campaigns of the future will become more and more data-

driven, resulting in a never-ending arms race of data science and data collection.

Now, before you start feeling too jaded: some data scientists also occasionally use

their skills for good—using data to make government more effective, to help the

homeless, and to improve public health. But it certainly won’t hurt your career if you

like figuring out the best way to get people to click on advertisements.

Motivating Hypothetical: DataSciencester

Congratulations! You’ve just been hired to lead the data science efforts at DataScien‐

cester, the social network for data scientists.

Despite being for data scientists, DataSciencester has never actually invested in build‐

ing its own data science practice. (In fairness, DataSciencester has never really inves‐

ted in building its product either.) That will be your job! Throughout the book, we’ll

be learning about data science concepts by solving problems that you encounter at

work. Sometimes we’ll look at data explicitly supplied by users, sometimes we’ll look

at data generated through their interactions with the site, and sometimes we’ll even

look at data from experiments that we’ll design.

2 | Chapter 1: Introduction

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从零开始掌握数据科学：Python基础算法解析

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