从零开始：Python实现数据科学基础算法全解析

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《Data.Science.from.Scratch.First.Principles.with.Python》是一本由Joel Grus撰写的实用指南，专为那些希望通过实践理解数据科学基础知识的人设计。该书旨在帮助读者避免依赖现成的数据科学库、框架和工具，而是通过从头开始实现核心算法，深入了解数据科学背后的数学和统计原理。如果你具备一定的数学能力和编程技能，这本书将引导你掌握线性代数、统计学、概率论等基础知识，并通过实际操作来学习如何收集、探索、清洗和处理数据。本书内容涵盖了广泛的主题，包括Python语言的基础知识，如数据可视化、线性代数、概率与统计推断，以及关键的数据科学概念如梯度下降、机器学习算法（如k-最近邻、朴素贝叶斯、简单和多元线性回归、逻辑回归、决策树、神经网络、聚类分析等）、推荐系统、自然语言处理、网络分析、数据库操作、MapReduce技术等。作者通过逐章深入的方式，让读者逐步建立起对数据科学的扎实基础，从而具备挖掘当今海量数据中隐藏答案的能力。作者以通俗易懂的语言和实例，带你进行一次从数据好奇者到专业数据科学家的转变之旅，确保你在掌握了基础知识的同时，也培养出必要的黑客技巧。这本书适合任何想要在数据科学领域扎根并深入探索的人，特别是对于初学者而言，是一本不可多得的入门教材。目前，《Data Science from Scratch》的定价分别为US$39.99和CAN$45.99，提供了一个全面学习数据科学的绝佳起点。

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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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