October 2018
Beginner to intermediate
238 pages
6h 32m
Chinese
Content preview from 面向数据科学家的实用统计学
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第 1 章
探索性数据分析
在过去的一个世纪中，统计学作为一门学科得到了长足的发展。概率论是统计学的数学基
础，它基于托马斯
•
贝叶斯、皮埃尔
•
西蒙
•
拉普拉斯和卡尔
•
高斯等人的工作，在
17
世
纪至
19
世纪期间形成并发展。与概率论的纯理论本质不同，统计学是一门应用科学，关
注的是数据的分析和建模。现代统计学是一门严谨的科学，其根源可上溯至
19
世纪末的
弗朗西斯
•
高尔顿和卡尔
•
皮尔逊。
20
世纪初，罗纳德
•
艾尔默
•
费希尔成为现代统计学的
先驱之一，他提出了
实验设计法
和
最大似然估计
等重要概念。不少其他统计学概念在很大
程度上也深深地植根于数据科学中。本书的主要目标就是帮助你理解这些概念，并阐明这
些概念在数据科学和大数据的背景下是否依然重要。
本章的重点是探索数据，这是所有数据科学项目的第一步。
探索性数据分析
（
EDA
）是统
计学中一个相对新的领域。经典统计学几乎只注重
推断
，即从小样本得出关于整体数据的
结论，这往往是一个复杂的过程。
1962
年，约翰
•
图基（图
1-1
）发表了一篇著名的论文
“
The Future of Data Analysis
”，由此引发了对统计学的重构。在论文中，图基提出了他称
之为
数据分析
的一门新学科，并将统计推断包括于其中，由此建立了与工程和计算机科学
界的联系［他提出了术语
比特
和
软件
，其中“比特”（
bit
）是“二进制数字”（
binary digit
）
的缩写］。出乎意料的是，这一初始理念被延续了下来，并成为了数据科学的基础之一。
图基编著并在
1977
年出版了
Exploratory Data Analysis ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.