数据作为一种新型生产要素，在未来的社会发展过程中将扮演越来越重要的角色，提升数据素养将有助于促进中国人工智能后备人才的高质量发展。本书以人工智能下的大数据时代为背景，从数据素养、数据分析基础、统计分析、机器学习多个维度全面系统地介绍了如何探索数据、整理数据并分析数据。本书没有给出晦涩难懂的数学公式，也不涉及复杂烦琐的程序代码，而是在阐述基本原理的基础上，辅以简洁的Python 程序，让读者能够快速入门，提升个人的数据综合素养。

虽然本书的目标读者为具有一定数学基础与Python 编程基础的中学生，但是实际上也适合对人工智能涉及的数据分析相关内容具有强烈兴趣的学者阅读，同时还可以为所有想提升自身数据素养并逐步深入了解数据科学知识的读者提供参考。 目前，市面上已经出版了许多中小学人工智能教学用书，对于是否有必要组织老师们编撰这本书，在研讨阶段我心有疑惑。在同龚超博士及袁中果博士交流后，我了解到，对于人工智能数据素养，市面上还没有太多涉及这方面内容的图书。本书是由电子工业出版社和中国人工智能学会共同支持出版的公益书籍，我受中国人工智能学会中小学工作委员会袁中果秘书长的委托，负责本书的编撰协调工作。参与编著本书的老师既有来自云南临沧的信息技术专业的老师，也有来自北京、上海、广东、河北的信息技术专业的老师，中国人工智能学会的暑期公益课程令大家汇聚一堂。 2020 年暑假，当我接到编著本书的任务时，认为面临很多挑战。在筹备阶段，我们建立了对应的微信工作群，在龚超博士的支持下对本书的整体结构进行了讨论，梳理并确定了如下写作分工。 ? 第1 章、第4 章、第11 章、第12 章：中国人工智能学会中小学工作委员会的龚超博士。西北工业大学计算机学院助理教授王冀、深圳索信达数据技术有限公司张孜勉博士、中国人民大学附属中学佟松龄老师对该部分内容提出了 一些建设性的意见。 ? 第2 章：云南省临沧市凤庆县第三完全中学的普建敏老师和中国人民大学附属中学第二分校的乌兰老师。 ? 第3 章：云南省临沧市凤庆县营盘中学的甘新忠老师。 ? 第5 章：河北省石家庄市第四十中学的贾海法老师。 ? 第6 章：北京市未来基因人工智能研究院的袁元老师。 ? 第7 章：广东省深圳市龙岗区华中师大附属龙园学校的宋倩倩老师。 ? 第8 章：上海市上海外国语大学附属外国语学校东校的邹天翔老师。 第9 章：北京交通大学附属中学的刘学军老师。 ? 第10 章：云南省临沧市凤庆县第三完全中学的普建敏老师。 ? 第13 章：广东省深圳市龙岗区上海外国语大学附属龙岗学校的周兴权老师。 在之后两周左右的时间里，大家共同讨论并逐步完善了符合本书定位的二级及三级目录结构。在确定了目录结构、编写规范之后，大家正式进入了编撰阶段，通过将近六周的时间汇总出了初稿。其间恰逢我的“孙越名师工作室”开展线上专家讲座活动，我积极邀请了所有编写成员参与其中，了解上海信息化教育的发展现状。通过交流，我们进一步明确了中小学人工智能教育的发展方向，这对后期书稿的修改大有裨益。 由于大家来自不同的省市，教师水平参差不齐，本书初稿的不同章节在编撰水平、书写风格和编写方式上存在较大差异。于是，我们对内容再次进行梳理并制定了新的统一编写规范，同时针对错误之处进行二次修改。在修改并汇总第2 稿的过 程中，我们也发现不同省市在一些基础理论的认知上存在差异。此时，北京、上海的老师纷纷伸出援手，帮助云南、广东的老师共同进行内容改进与提升，历时1 个月形成了第2 稿。 之后，我们又对全文进行了大规模调整，调整后的书稿基本达到了出版的要求。在这一调整过程中，我们发现一些图表、代码的细节仍存在些许差异，于是又邀请了深圳中学龙岗初级中学的杨雯雯老师帮助我们深度校稿、修订，形成了第3 稿。 编撰工作持续到2020 年11 月初，我们完成了第4 稿，整本书基本形成了统一的风格。接着，我们又有幸邀请到上海市电化教育馆的秦红斌老师进行审稿，协助我们进行更准确的定位。 在4 个多月的编撰过程中，大家在面对困难的时候，有勇于挑战的，也有选择放弃的。选择勇于挑战的老师在编撰工作中提升了自己的信息化专业能力，对计算思维、数据思维、人工智能教学等都产生了新的认知。同时，在这些日子里，编撰老师们也结成了深厚的友谊。 读者在阅读本书时可能会发现不同章节的内容在难度上有较大差别，这是由于之前我们所提到的，编撰教师来自不同省市，在专业能力上存在一定差别，还请各位读者见谅。 本书是中国人工智能学会和电子工业出版社共同出版的公益书籍，能完成本次书稿编撰，对于很多老师来说就是成功。我们希望本书能够促使更多东西部地区的老师参与中国中小学人工智能教育，并且通过互联网教研、在线专家讲座等低成本方式，进一步提高自己的学科专业能力。中西部地区、沿海地区与北上广的老师共同进步，为中国中小学人工智能教育贡献自己的力量。 本书中引用的图片，除特别标注外，均来自网络，鉴于编著者的水平参差不齐，而且在很多情况下无法得知引用照片的原出处，在此统一对原创作者表示感谢。 最后，这本书献给所有参与此书编写工作的老师们，谢谢你们的精诚合作，让我们能够更好地培养孩子们的数据素养。 孙 越 上海外国语大学附属龙岗学校

第1 章 人工智能下的大数据时代 ...................................................................................... 001 1.1 大数据时代和人工智能 ····································································.001 1.1.1 一切皆为数据 ······································································.001 1.1.2 数据高速增长时代 ································································.002 1.1.3 利用人工智能掘金大数据························································.003 1.2 人工智能三要素 ·············································································.004 1.2.1 数据——AI 之源 ···································································.005 1.2.2 算法——AI 之核 ···································································.006 1.2.3 算力——AI 之驱 ···································································.007 1.3 数据素养 ······················································································.007 1.3.1 何为数据素养 ······································································.007 1.3.2 数据素养为何重要 ································································.010 1.3.3 如何提升数据素养 ································································.011 1.4 本章小结 ······················································································.012 第2 章 Python 数据分析基础 ............................................................................................. 013 2.1 Python 基础 ··················································································.013 2.1.1 Python 简介 ·········································································.013 2.1.2 Python 数据类型 ···································································.017 2.1.3 常用的操作、函数和方法························································.021 2.1.4 列表、元组、字典 ································································.024 2.1.5 顺序结构 ············································································.027 2.1.6 分支结构 ············································································.027 2.1.7 循环结构 ············································································.030 2.2 Python 数据分析环境 ······································································.032 2.2.1 使用pip 安装数据分析相关库 ··················································.032 2.2.2 安装Anaconda ·····································································.033 2.3 Python 数据分析相关库 ···································································.033 2.3.1 NumPy 库 ··········································································.033 2.3.2 Matplotlib 库 ·······································································.034 2.3.3 SciPy 库·············································································.035 2.3.4 Pandas 库 ···········································································.036 2.3.5 xlrd 库 ···············································································.036 2.3.6 PyMySQL 库 ·······································································.037 2.3.7 其他数据分析相关库 ·····························································.037 2.4 本章小结 ·····················································································.038 第3 章 Jupyter 环境的使用 ................................................................................................. 039 3.1 Jupyter Notebook 概述 ·····································································.039 3.1.1 Jupyter Notebook 简介及优点 ···················································.039 3.1.2 Jupyter Notebook 开发环境的搭建 ·············································.039 3.1.3 使用pip 命令安装 ································································.044 3.2 认识Jupyter Notebook ·····································································.044 3.2.1 认识Files、Running、Clusters 页面 ··········································.044 3.2.2 认识Jupyter Notebook 的主页面 ···············································.046 3.3 新建、运行、保存Jupyter Notebook 文件 ·············································.048 3.3.1 新建一个Jupyter Notebook ······················································.048 3.3.2 运行代码 ···········································································.049 3.3.3 重命名Jupyter Notebook 文件 ··················································.049 3.3.4 保存Jupyter Notebook 文件 ·····················································.050 3.4 处理不同类型的数据 ······································································.050 3.4.1 处理txt 文件 ·······································································.050 3.4.2 处理CSV 文件 ····································································.052 3.4.3 处理Excel 文件 ···································································.053 3.4.4 处理sql 文件 ······································································.053 3.5 在Markdown 中使用LaTeX 输入数学公式 ············································.054 3.5.1 使用LaTeX 输入一个数学公式 ·················································.054 3.5.2 LaTeX 的2 种公式格式 ··························································.055 3.5.3 常用数学公式的写法 ·····························································.056 3.6 Jupyter Notebook 应用实例解析 ··························································.058 3.6.1 实例1：能力六维雷达图的绘制 ···············································.058 3.6.2 实例2：词频统计 ·································································.059 3.7 本章小结 ······················································································.060 第4 章 探索数据 .................................................................................................................. 062 4.1 走进数据的世界 ·············································································.062 4.1.1 定义数据 ············································································.062 4.1.2 数据的分类 ·········································································.063 4.1.3 深挖数据的4 种能力 ·····························································.065 4.1.4 善用指标分析问题 ································································.067 4.2 数据的评估 ···················································································.069 4.2.1 指标真的可靠吗 ···································································.069 4.2.2 统计数据会“说谎” ·····························································.071 4.3 数据怎么用 ···················································································.072 4.3.1 数据清洗 ············································································.072 4.3.2 数据的标准化 ······································································.076 4.4 本章小结 ······················································································.078 第5 章 描述统计 .................................................................................................................. 079 5.1 数据集中趋势 ················································································.079 5.1.1 均值的定义与应用 ································································.079 5.1.2 中位数的定义与应用 ·····························································.081 5.1.3 众数的定义与应用 ································································.083 5.1.4 案例分析 ············································································.085 5.2 数据离散程度 ················································································.087 5.2.1 极差的定义与应用 ································································.088 5.2.2 方差的定义与应用 ································································.090 5.3 本章小结 ·····················································································.091 第6 章 推断统计 ................................................................................................................... 092 6.1 基础知识要点 ···············································································.092 6.1.1 排列与组合 ········································································.092 6.1.2 随机事件及其概率 ································································.095 6.2 概率分布及其特征 ·········································································.095 6.2.1 二项分布 ···········································································.096 6.2.2 正态分布 ···········································································.098 6.3 统计量 ························································································.104 6.3.1 总体与样本 ········································································.105 6.3.2 参数估计 ···········································································.109 6.3.3 假设检验 ···········································································.112 6.4 本章小结 ·····················································································.116 第7 章 数据可视化 ............................................................................................................... 117 7.1 什么是数据可视化 ·········································································.117 7.1.1 数据可视化的定义和意义 ·······················································.117 7.1.2 数据可视化的发展历史 ··························································.118 7.2 图形对象与元素 ············································································.119 7.2.1 如何建立坐标系···································································.121 7.2.2 如何设置坐标轴的文本和图例 ·················································.122 7.3 可视化色彩的运用原理 ···································································.123 7.3.1 RGB 颜色模式 ·····································································.123 7.3.2 HSL 颜色模式 ·····································································.124 7.3.3 颜色搭配的技巧和案例 ··························································.124 7.4 图表的基本类型 ············································································.126 7.4.1 如何绘制柱形图···································································.126 7.4.2 如何绘制散点图···································································.127 7.4.3 如何绘制饼形图···································································.129 7.4.4 如何绘制折线图 ···································································.130 7.5 数据分析及可视化案例 ····································································.132 7.5.1 数据可视化经典案例 ·····························································.132 7.5.2 非结构化数据的可视化案例 ·····················································.132 7.6 常见的数据可视化流程 ····································································.133 7.7 本章小结 ······················································································.133 第8 章 NumPy 数组 ............................................................................................................ 134 8.1 NumPy 库简介 ···············································································.134 8.2 NumPy 数组的生成 ·········································································.134 8.2.1 生成一般数组 ······································································.135 8.2.2 生成特殊数组 ······································································.136 8.2.3 生成随机数组 ······································································.138 8.3 NumPy 数组基础 ············································································.140 8.3.1 NumPy 数组的基本属性 ··························································.140 8.3.2 数组索引：获取单个元素························································.141 8.3.3 数组切片：获取子数组 ··························································.141 8.4 NumPy 数组重塑 ············································································.143 8.4.1 NumPy 数组的变形 ·······························································.144 8.4.2 NumPy 数组的转置和换轴 ·······················································.144 8.4.3 NumPy 数组的拼接与分裂 ·······················································.146 8.5 NumPy 库中的线性代数 ···································································.147 8.5.1 矩阵乘法 ············································································.147 8.5.2 行列式 ···············································································.148 8.5.3 求线性方程的解 ···································································.148 8.6 通用函数 ······················································································.150 8.6.1 一元通用函数 ······································································.150 8.6.2 二元通用函数 ······································································.150 8.6.3 广播 ··················································································.152 8.7 常用的数据分析函数 ·······································································.154 8.7.1 条件函数 ············································································.154 8.7.2 聚合函数 ···········································································.154 8.7.3 快速排序 ···········································································.156 8.7.4 唯一值与其他集合逻辑 ··························································.157 8.8 本章小结 ·····················································································.158 第9 章 时间序列数据 ........................................................................................................... 159 9.1 时间序列的定义及分类 ···································································.159 9.1.1 时间序列的定义···································································.159 9.1.2 时间序列的分类···································································.159 9.2 时间序列的描述性分析 ···································································.162 9.2.1 图形描述 ···········································································.162 9.2.2 增长率分析 ········································································.163 9.3 时间序列的预测 ············································································.164 9.3.1 确定时间序列成分 ································································.165 9.3.2 选择预测方法 ·····································································.166 9.3.3 预测方法评估 ·····································································.167 9.4 平稳时间序列的预测 ······································································.169 9.4.1 简单平均法 ········································································.169 9.4.2 移动平均法 ········································································.170 9.4.3 指数平滑法 ········································································.171 9.5 趋势型和复合型时间序列的预测 ························································.172 9.5.1 线性趋势预测 ·····································································.172 9.5.2 非线性趋势预测···································································.173 9.5.3 复合型时间序列的分解预测 ····················································.174 9.6 使用Python 处理时间序列数据 ··························································.174 9.6.1 时间序列数据处理工具的选择 ·················································.175 9.6.2 时间序列数据的导入 ·····························································.175 9.6.3 时间序列数据预处理 ·····························································.176 9.6.4 时间序列数据处理 ································································.177 9.7 本章小结 ·····················································································.185 第10 章 文本数据 ................................................................................................................ 186 10.1 文本数据的导入 ···········································································.186 10.1.1 文本数据与自然语言处理 ······················································.186 10.1.2 分词 ················································································.187 10.2 文本数据的处理 ···········································································.189 10.2.1 文本特征初探 ·····································································.189 10.2.2 文本信息的提取 ··································································.194 10.2.3 文本向量化 ········································································.198 10.3 文本分析的应用 ···········································································.200 10.3.1 文本分类 ···········································································.200 10.3.2 文本情感分析 ·····································································.201 10.4 本章小结 ····················································································.204 第11 章 回归分析 ................................................................................................................ 205 11.1 叩响人工智能之门 ·········································································.205 11.1.1 人工智能与机器学习 ····························································.205 11.1.2 工欲善其事，必先利其器 ······················································.208 11.1.3 算法，该“出道”了 ····························································.208 11.2 万朝归宗：线性回归 ······································································.209 11.2.1 一元之道 ···········································································.210 11.2.2 从一元到多元 ·····································································.216 11.2.3 学习和工作中的线性回归 ······················································.218 11.3 回归增强术 ·················································································.221 11.3.1 非线性回归 ········································································.221 11.3.2 可分类的回归 ·····································································.224 11.3.3 能降维的回归 ·····································································.227 11.4 本章小结 ····················································································.228 第12 章 聚类分析 ................................................................................................................ 229 12.1 数据之眼看聚类 ···········································································.229 12.1.1 什么是聚类 ·······································································.229 12.1.2 人工智能的未来：无监督学习 ···············································.231 12.1.3 距离产生美 ·······································································.232 12.2 K 均值聚类 ·················································································.234 12.2.1 K 均值聚类的思想 ······························································.234 12.2.2 抽丝剥茧K 均值 ·································································.234 12.2.3 鸢尾花的K 均值聚类 ···························································.238 12.3 案例：数据下的省、区、市 ····························································.240 12.3.1 提出问题 ··········································································.240 12.3.2 数据获取与处理 ·································································.241 12.3.3 建模分析与结果 ·································································.242 12.4 本章小结 ···················································································.244 第13 章 数据素养综合案例 ................................................................................................ 245 13.1 综合案例1：利用人工智能爬取大数据，轻松掌握股市动态 ·························.245 13.1.1 认识人工智能网络爬虫 ························································.245 13.1.2 爬取股市大数据，分析需求 ··················································.246 13.1.3 爬取股市大数据案例 ···························································.246 13.2 综合案例2：人工智能数据——体型分析 ···········································.251 13.2.1 K 最邻近分类算法原理·························································.251 13.2.2 使用KNN 分类算法对体型进行分类的案例 ·······························.253 13.3 其他案例集 ·················································································.254 13.3.1 计算生肖和星座 ·································································.254 13.3.2 猜数游戏 ··········································································.255 13.3.3 二维列表排序 ····································································.255 13.3.4 学生信息录入 ····································································.255 13.3.5 打印回文素数（合数） ························································.256 13.3.6 数据库加密 ·······································································.256 13.3.7 计算圆台的体积和表面积 ·····················································.256 13.4 本章小结 ···················································································.257