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Jingdong book

数据科学入门

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Jingdong book

数据科学入门

Name: 数据科学入门
Brand: Jingdong book
SKU: 3118203951
Price: 36.43 USD
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介绍数据科学基本知识的重量级读本，Google数据科学家作品。

数据科学是一个蓬勃发展、前途无限的行业，有人将数据科学家称为“21世纪头号性感职业”。本书从零开始讲解数据科学工作，教授数据科学工作所必需的黑客技能，并带领读者熟悉数据科学的核心知识——数学和统计学。

作者选择了功能强大、简单易学的Python语言环境，亲手搭建工具和实现算法，并精心挑选了注释良好、简洁易读的实现范例。书中涵盖的所有代码和数据都可以在GitHub上下载。

通过阅读本书，你可以：

学到一堂Python速成课；

学习线性代数、统计和概率论的基本方法，了解它们是怎样应用在数据科学中的；

掌握如何收集、探索、清理、转换和操作数据；

深入理解机器学习的基础；

运用k-近邻、朴素贝叶斯、线性回归和逻辑回归、决策树、神经网络和聚类等各种数据模型；

探索推荐系统、自然语言处理、网络分析、MapReduce和数据库。

Content Description

本书基于易于理解且具有数据科学相关的丰富的库的Python语言环境，从零开始讲解数据科学工作。具体内容包括：Python速成，可视化数据，线性代数，统计，概率，假设与推断，梯度下降法，如何获取数据，k近邻法，朴素贝叶斯算法，等等。作者借助大量具体例子以及数据挖掘、统计学、机器学习等领域的重要概念，详细展示了什么是数据科学。

Author Description

Joel Grus是Google的一位软件工程师，曾于数家创业公司担任数据科学家。目前住在西雅图，专注于数据科学工作并乐此不疲。

Catalogue

前言 xiii
第1章导论 1
1．1数据的威力 1
1．2什么是数据科学 1
1．3激励假设：DataSciencester 2
1．3．1寻找关键联系人 3
1．3．2你可能知道的数据科学家 5
1．3．3工资与工作年限 8
1．3．4付费账户 10
1．3．5兴趣主题 11
1．4展望 12
第2章Python速成 13
2．1基础内容 13
2．1．1Python获取 13
2．1．2Python之禅 14
2．1．3空白形式 14
2．1．4模块 15
2．1．5算法 16
2．1．6函数 16
2．1．7字符串 17
2．1．8异常 18
2．1．9列表 18
2．1．10元组 19
2．1．11字典 20
2．1．12集合 22
2．1．13控制流 23
2．1．14真和假 24
2．2进阶内容 25
2．2．1排序 25
2．2．2列表解析 25
2．2．3生成器和迭代器 26
2．2．4随机性 27
2．2．5正则表达式 28
2．2．6面向对象的编程 28
2．2．7函数式工具 29
2．2．8枚举 31
2．2．9压缩和参数拆分 31
2．2．10args和kwargs 32
2．2．11欢迎来到DataSciencester 33
2．3延伸学习 33
第3章可视化数据 34
3．1matplotlib 34
3．2条形图 36
3．3线图 40
3．4散点图 41
3．5延伸学习 44
第4章线性代数 45
4．1向量 45
4．2矩阵 49
4．3延伸学习 51
第5章统计学 53
5．1描述单个数据集 53
5．1．1中心倾向 55
5．1．2离散度 56
5．2相关 58
5．3辛普森悖论 60
5．4相关系数其他注意事项 61
5．5相关和因果 62
5．6延伸学习 63
第6章概率 64
6．1不独立和独立 64
6．2条件概率 65
6．3贝叶斯定理 66
6．4随机变量 68
6．5连续分布 68
6．6正态分布 69
6．7中心极限定理 72
6．8延伸学习 74
第7章假设与推断 75
7．1统计假设检验 75
7．2案例：掷硬币 75
7．3置信区间 79
7．4P-hacking 80
7．5案例：运行A/B 测试 81
7．6贝叶斯推断 82
7．7延伸学习 85
第8章梯度下降 86
8．1梯度下降的思想 86
8．2估算梯度 87
8．3使用梯度 90
8．4选择正确步长 90
8．5综合 91
8．6随机梯度下降法 92
8．7延伸学习 93
第9章获取数据 94
9．1stdin和stdout 94
9．2读取文件 96
9．2．1文本文件基础 96
9．2．2限制的文件 97
9．3网络抓取 99
9．3．1HTML和解析方法 99
9．3．2案例：关于数据的O’Reilly 图书 101
9．4使用API 105
9．4．1JSON（和XML） 105
9．4．2使用无验证的API 106
9．4．3寻找API 107
9．5案例：使用Twitter API 108
9．6延伸学习 111
第10章数据工作 112
10．1探索你的数据 112
10．1．1探索一维数据 112
10．1．2二维数据 114
10．1．3多维数据 116
10．2清理与修改 117
10．3数据处理 119
10．4数据调整 122
10．5降维 123
10．6延伸学习 129
第11章机器学习 130
11．1建模 130
11．2什么是机器学习 131
11．3过拟合和欠拟合 131
11．4正确性 134
11．5偏倚-方差权衡 136
11．6特征提取和选择 137
11．7延伸学习 138
第12章k近邻法 139
12．1模型 139
12．2案例：最喜欢的编程语言 141
12．3维数灾难 146
12．4延伸学习 151
第13章朴素贝叶斯算法 152
13．1一个简易的垃圾邮件过滤器 152
13．2一个复杂的垃圾邮件过滤器 153
13．3算法的实现 154
13．4测试模型 156
13．5延伸学习 158
第14章简单线性回归 159
14．1模型 159
14．2利用梯度下降法 162
14．3最大似然估计 162
14．4延伸学习 163
第15章多重回归分析 164
15．1模型 164
15．2最小二乘模型的进一步假设 165
15．3拟合模型 166
15．4解释模型 167
15．5拟合优度 167
15．6题外话：Bootstrap 168
15．7回归系数的标准误差 169
15．8正则化 170
15．9延伸学习 172
第16章逻辑回归 173
16．1问题 173
16．2Logistic函数 176
16．3应用模型 178
16．4拟合优度 179
16．5支持向量机 180
16．6延伸学习 184
第17章决策树 185
17．1什么是决策树 185
17．2熵 187
17．3分割之熵 189
17．4创建决策树 190
17．5综合运用 192
17．6随机森林 194
17．7延伸学习 195
第18章神经网络 196
18．1感知器 196
18．2前馈神经网络 198
18．3反向传播 201
18．4实例：战胜CAPTCHA 202
18．5延伸学习 206
第19章聚类分析 208
19．1原理 208
19．2模型 209
19．3示例：聚会 210
19．4选择聚类数目k 213
19．5示例：对色彩进行聚类 214
19．6自下而上的分层聚类 216
19．7延伸学习 221
第20章自然语言处理 222
20．1词云 222
20．2n-grams 模型 224
20．3语法 227
20．4题外话：吉布斯采样 229
20．5主题建模 231
20．6延伸学习 236
第21章网络分析 237
21．1中介中心度 237
21．2特征向量中心度 242
21．2．1矩阵乘法 242
21．2．2中心度 244
21．3有向图与PageRank 246
21．4延伸学习 248
第22章推荐系统 249
22．1手工甄筛 250
22．2推荐流行事物 250
22．3基于用户的协同过滤方法 251
22．4基于物品的协同过滤算法 254
22．5延伸学习 256
第23章数据库与SQL 257
23．1CREATE TABLE与INSERT 257
23．2UPDATE 259
23．3DELETE 260
23．4SELECT 260
23．5GROUP BY 262
23．6ORDER BY 264
23．7JOIN 264
23．8子查询 267
23．9索引 267
23．10查询优化 268
23．11NoSQL 268
23．12延伸学习 269
第24章MapReduce 270
24．1案例：单词计数 270
24．2为什么是MapReduce 272
24．3更加一般化的MapReduce 272
24．4案例：分析状态更新 273
24．5案例：矩阵计算 275
24．6题外话：组合器 276
24．7延伸学习 277
第25章数据科学前瞻 278
25．1IPython 278
25．2数学 279
25．3不从零开始 279
25．3．1NumPy 279
25．3．2pandas 280
25．3．3scikit-learn 280
25．3．4可视化 280
25．3．5R 281
25．4寻找数据 281
25．5从事数据科学 281
25．5．1Hacker News 282
25．5．2消防车 282
25．5．3T 恤 282
25．5．4你呢？ 283
作者简介 284
关于封面 284

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