2 |
3 | 
4 | 
5 | 
6 |
7 | ---
8 |
9 | ## 简介
10 |
11 | 本项目是以 Python 为基础的数据科学入门教程，在线版本托管在 https://ds-python.leovan.tech 上。
12 |
13 | ## 参考
14 |
15 | 1. 《Python 编程从入门到实践》(Python Crash Course, A Hand-On, Project-Based Introduction to Programming)，Eric Matthes 著，袁国忠 译
16 | 2. 《流畅的 Python》(Fluent Python)，Luciano Ramalho 著，安道、吴珂 译
17 | 3. 《利用 Python 进行数据分析》(Python for Data Analysis：Data Wrangling with Pandas, Numpy and IPython)，Wes McKinney 著，徐敬一 译
18 | 4. 《机器学习实践》(Machine Learning in Action)，Petter Harrington 著，李锐、李鹏、曲亚东、王斌 译
19 | 5. 《Python 机器学习》(Python Machine Learning)，Sebastian Raschka & Vahid Mirjalili 著，陈斌 译
20 | 6. 《统计学习方法》李航 著
21 | 7. 《机器学习》周志华 著
22 | 8. 《深度学习》(Deep Learning)，Ian Goodfellow, Yoshua Bengio & Aaron Courville 著，赵申剑、黎彧君、符天凡、李凯 译
23 |
24 | ## 授权
25 |
26 | 本项目遵守 [CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/) 协议。
27 |
28 | 版权所有 © 2020-2024，范叶亮 | Leo Van
29 |
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1 | ---
2 | title: 关于
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: about
6 | disable_mathjax: true
7 | disable_prismjs: true
8 | disable_adsense: true
9 | ---
10 |
11 | ## 简介 
12 |
13 | 本项目是以 Python 为基础的数据科学入门教程。
14 |
15 | 在线版本托管在 https://ds-python.leovan.tech 上，源代码存储在 [Github](https://github.com/leovan/data-science-introduction-with-python) 中。
16 |
17 | ## 参考
18 |
19 | 1. 《Python 编程从入门到实践》(Python Crash Course, A Hand-On, Project-Based Introduction to Programming)，Eric Matthes 著，袁国忠 译
20 | 2. 《流畅的 Python》(Fluent Python)，Luciano Ramalho 著，安道、吴珂 译
21 | 3. 《利用 Python 进行数据分析》(Python for Data Analysis：Data Wrangling with Pandas, Numpy and IPython)，Wes McKinney 著，徐敬一 译
22 | 4. 《机器学习实践》(Machine Learning in Action)，Petter Harrington 著，李锐、李鹏、曲亚东、王斌 译
23 | 5. 《Python 机器学习》(Python Machine Learning)，Sebastian Raschka & Vahid Mirjalili 著，陈斌 译
24 | 6. 《统计学习方法》李航 著
25 | 7. 《机器学习》周志华 著
26 | 8. 《深度学习》(Deep Learning)，Ian Goodfellow, Yoshua Bengio & Aaron Courville 著，赵申剑、黎彧君、符天凡、李凯 译
27 |
28 | ## 授权
29 |
30 | 本项目遵守 [CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/) 协议。
31 |
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/content/doc/01-ide-installation-manual.md:
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1 | ---
2 | title: 开发环境安装配置手册
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: ide-installation-manual
6 | description: 手册主要介绍在 Windows 和 macOS 系统中安装并配置 Python 相关开发环境。
7 | weight: 1
8 | cover: /images/doc/jupyterlab-macos.png
9 | tags:
10 | - Git
11 | - Python
12 | - Jupyter
13 | ---
14 |
15 | ## Windows
16 |
17 | > 建议使用 64 位 Windows 10 以上版本系统。
18 |
19 | ### 命令行
20 |
21 | 建议安装最新版的 PowerShell 作为命令行环境，相关下载和配置详见[官网](https://learn.microsoft.com/zh-cn/powershell/)。
22 |
23 | 打开 PowerShell 命令行，输入如下命令安装 scoop：
24 |
25 | ```powershell
26 | Set-ExecutionPolicy RemoteSigned -Scope CurrentUser
27 | irm get.scoop.sh | iex
28 | ```
29 |
30 | [scoop](https://scoop.sh/) 是一个用于 Windows 的命令行包安装管理器，其可以方便用户在命令行中安装并管理应用和扩展包。
31 |
32 | 运行如下命令测试 scoop 是否成功安装：
33 |
34 | ```powershell
35 | scoop --version
36 | ```
37 |
38 | 如果能得到类似如下输出，则说明安装成功：
39 |
40 | ```
41 | Current Scoop version:
42 | v0.3.1 - Released at 2022-11-15
43 | ```
44 |
45 | 运行如下命令为 scoop 添加额外的应用清单：
46 |
47 | ```powershell
48 | scoop bucket add extras
49 | ```
50 |
51 | ### Git
52 |
53 | 输入如下命令安装 [Git](https://git-scm.com/)：
54 |
55 | ```powershell
56 | scoop install git
57 | ```
58 |
59 | 运行如下命令测试 Git 是否成功安装：
60 |
61 | ```powershell
62 | git --version
63 | ```
64 |
65 | 如果能得到类似如下输出，则说明安装成功：
66 |
67 | ```
68 | git version 2.39.0.windows.2
69 | ```
70 |
71 | ### Python
72 |
73 | 输入如下命令安装 [Python](https://www.python.org/)：
74 |
75 | ```powershell
76 | scoop install python
77 | ```
78 |
79 | 运行如下命令测试 Python 是否成功安装：
80 |
81 | ```powershell
82 | python --version
83 | ```
84 |
85 | 如果能得到类似如下输出，则说明安装成功：
86 |
87 | ```
88 | Python 3.11.1
89 | ```
90 |
91 | ### Jupyter
92 |
93 | 输入如下命令安装 [JupyterLab](https://jupyter.org/)：
94 |
95 | ```bash
96 | pip install jupyterlab
97 | ```
98 |
99 | 运行如下命令启动 JupyterLab：
100 |
101 | ```bash
102 | jupyter-lab
103 | ```
104 |
105 | 系统会自动打开浏览器并显示 JupyterLab 页面：
106 |
107 | 
108 |
109 | ## macOS
110 |
111 | > 建议使用 macOS 11 以上版本系统。
112 |
113 | ### 命令行
114 |
115 | 打开终端，输入如下命令安装 homebrew：
116 |
117 | ```bash
118 | /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
119 | ```
120 |
121 | [Homebrew](https://brew.sh/) 是一个用于 macOS 的命令行包安装管理器，其可以方便用户在命令行中安装并管理应用和扩展包。
122 |
123 | 运行如下命令测试 scoop 是否成功安装：
124 |
125 | ```bash
126 | brew --version
127 | ```
128 |
129 | 如果能得到类似如下输出，则说明安装成功：
130 |
131 | ```
132 | Homebrew 3.6.19
133 | ```
134 |
135 | ### Git
136 |
137 | 输入如下命令安装 [Git](https://git-scm.com/)：
138 |
139 | ```bash
140 | brew install git
141 | ```
142 |
143 | 运行如下命令测试 Git 是否成功安装：
144 |
145 | ```bash
146 | git --version
147 | ```
148 |
149 | 如果能得到类似如下输出，则说明安装成功：
150 |
151 | ```
152 | git version 2.39.1
153 | ```
154 |
155 | ### Python
156 |
157 | 输入如下命令安装 [Python](https://www.python.org/)：
158 |
159 | ```bash
160 | brew install python
161 | ```
162 |
163 | 运行如下命令测试 Python 是否成功安装：
164 |
165 | ```bash
166 | python --version
167 | ```
168 |
169 | 如果能得到类似如下输出，则说明安装成功：
170 |
171 | ```
172 | Python 3.10.9
173 | ```
174 |
175 | ### Jupyter
176 |
177 | 输入如下命令安装 [JupyterLab](https://jupyter.org/)：
178 |
179 | ```bash
180 | pip install jupyterlab
181 | ```
182 |
183 | 运行如下命令启动 JupyterLab：
184 |
185 | ```bash
186 | jupyter-lab
187 | ```
188 |
189 | 系统会自动打开浏览器并显示 JupyterLab 页面：
190 |
191 | 
192 |
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/content/doc/_index.md:
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1 | ---
2 | title: 文档
3 | author: 范叶亮
4 | date: 2023-01-21
5 | ---
6 |
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/content/lecture/01-data-science-introduction.md:
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1 | ---
2 | title: 数据科学简介
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: data-science-introduction
6 | description: 数据科学是一门专门处理数据的科学，数据的真正价值只有在进行深度加工处理并形成产品之后才能够被体现出来。
7 | weight: 1
8 | cover: /images/lecture/01-data-science-introduction.png
9 | tags:
10 | - 数据科学
11 | - 数据产品
12 | - CRISP-DM
13 | ---
14 |
15 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/01-data-science-introduction.pdf" title="01 - 数据科学简介">}}
16 |
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/content/lecture/02-python-language-introduction.md:
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1 | ---
2 | title: Python 简介
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: python-language-introduction
6 | description: Python 是一种支持多种编程范型广泛使用的解释型、高级和通用的编程语言。
7 | weight: 2
8 | cover: /images/lecture/02-python-language-introduction.png
9 | tags:
10 | - Python
11 | ---
12 |
13 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/02-python-language-introduction.pdf" title="02 - Python 简介">}}
14 |
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/content/lecture/03-data-analytics-introduction-part-1.md:
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1 | ---
2 | title: 数据分析基础 (上)
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: data-analytics-introduction-part-1
6 | description: 基于 NumPy 扩展包的多维数组对象和面向数据编程。
7 | weight: 3
8 | cover: /images/lecture/03-data-analytics-introduction-part-1.png
9 | tags:
10 | - NumPy
11 | - 多维数组对象
12 | - 面向数据编程
13 | ---
14 |
15 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/03-data-analytics-introduction-part-1.pdf" title="03 - 数据分析基础 (上)">}}
16 |
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/content/lecture/04-data-analytics-introduction-part-2.md:
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1 | ---
2 | title: 数据分析基础 (下)
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: data-analytics-introduction-part-2
6 | description: 基于 pandas 扩展包的数据载入、数据存储和数据规整。
7 | weight: 4
8 | cover: /images/lecture/04-data-analytics-introduction-part-2.png
9 | tags:
10 | - pandas
11 | - 数据载入
12 | - 数据存储
13 | - 数据规整
14 | ---
15 |
16 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/04-data-analytics-introduction-part-2.pdf" title="04 - 数据分析基础 (下)">}}
17 |
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/content/lecture/05-data-visualization.md:
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1 | ---
2 | title: 数据可视化
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: data-visualization
6 | description: 数据可视化旨在借助于图形化手段，清晰有效地传达与沟通信息，数据可视化既是一门艺术也是一门科学。
7 | weight: 5
8 | cover: /images/lecture/05-data-visualization.png
9 | tags:
10 | - Matplotlib
11 | - plotnine
12 | - Plotly
13 | - pyecharts
14 | ---
15 |
16 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/05-data-visualization.pdf" title="05 - 数据可视化">}}
17 |
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/content/lecture/06-statistical-analytics-introduction.md:
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1 | ---
2 | title: 统计分析基础
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: statistical-analytics-introduction
6 | description: 统计分析方法是广泛使用的现代科学方法，是一种比较科学、精确和客观的测评方法。
7 | weight: 6
8 | cover: /images/lecture/06-statistical-analytics-introduction.png
9 | tags:
10 | - 探索性分析
11 | - 实验设计
12 | - 线性回归
13 | ---
14 |
15 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/06-statistical-analytics-introduction.pdf" title="06 - 统计分析基础">}}
16 |
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/content/lecture/07-feature-engineering.md:
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1 | ---
2 | title: 特征工程
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: feature-engineering
6 | description: 特征工程是指从实际业务发生产生的原始数据加工得到最终用于特定的挖掘算法的输入变量的过程。
7 | weight: 7
8 | cover: /images/lecture/07-feature-engineering.png
9 | tags:
10 | - 预处理
11 | - 变换
12 | - 编码
13 | - 提取
14 | - 选择
15 | - 监控
16 | ---
17 |
18 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/07-feature-engineering.pdf" title="07 - 特征工程">}}
19 |
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/content/lecture/08-model-evaluation-and-hyperparameter-optimization.md:
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1 | ---
2 | title: 模型评估 & 超参数优化
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: model-evaluation-and-hyperparameter-optimization
6 | description: 对学习器的泛化性能进行评估，不仅需要有效可行的实验评估方法，还需要有衡量模型泛化能力的评价标准。
7 | weight: 8
8 | cover: /images/lecture/08-model-evaluation-and-hyperparameter-optimization.png
9 | tags:
10 | - 性能评估
11 | - 模型生成
12 | - 模型选择
13 | - 超参数优化
14 | ---
15 |
16 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/08-model-evaluation-and-hyperparameter-optimization.pdf" title="08 - 模型评估 & 超参数优化">}}
17 |
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/content/lecture/09-classification-algorithms-part-1.md:
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1 | ---
2 | title: 分类算法（上）
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: classification-algorithms-part-1
6 | description: 逻辑回归和决策树是用于分类任务的经典监督学习算法，同时其具有较好的解释性。
7 | weight: 9
8 | cover: /images/lecture/09-classification-algorithms-part-1.png
9 | tags:
10 | - 逻辑回归
11 | - 决策树
12 | ---
13 |
14 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/09-classification-algorithms-part-1.pdf" title="09 - 分类算法（上）">}}
15 |
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1 | ---
2 | title: 分类算法（下）
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: classification-algorithms-part-2
6 | description: 集成学习算法的基本思想就是将多个分类器组合，从而实现一个预测效果更好的集成分类器。
7 | weight: 10
8 | cover: /images/lecture/10-classification-algorithms-part-2.png
9 | tags:
10 | - 集成学习
11 | - Bagging
12 | - Boosting
13 | - Stacking
14 | ---
15 |
16 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/10-classification-algorithms-part-2.pdf" title="10 - 分类算法（下）">}}
17 |
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1 | ---
2 | title: 聚类算法
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: clustering-algorithms
6 | description: 聚类算法是一种把相似的对象分成不同的组别或者更多的子集的非监督式学习算法。
7 | weight: 11
8 | cover: /images/lecture/11-clustering-algorithms.png
9 | tags:
10 | - K-means
11 | - 层次聚类
12 | - 基于密度的聚类
13 | ---
14 |
15 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/11-clustering-algorithms.pdf" title="11 - 聚类算法">}}
16 |
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/content/lecture/12-time-series-algorithms.md:
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1 | ---
2 | title: 时间序列算法
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: time-series-algorithms
6 | description: 时间序列分析的目的是挖掘时间序列中隐含模式，并借此对此序列进行评估以及对后续趋势进行预测。
7 | weight: 12
8 | cover: /images/lecture/12-time-series-algorithms.png
9 | tags:
10 | - 时间序列
11 | - ARIMA
12 | - 季节性
13 | - Prophet
14 | ---
15 |
16 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/12-time-series-algorithms.pdf" title="12 - 时间序列算法">}}
17 |
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1 | ---
2 | title: 深度学习算法
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: deep-learning-algorithms
6 | description: 深度学习是机器学习的分支，是一种以人工神经网络为架构，对资料进行表征学习的算法。
7 | weight: 13
8 | cover: /images/lecture/13-deep-learning-algorithms.png
9 | tags:
10 | - 人工神经网络
11 | - 卷积神经网络
12 | - 循环神经网络
13 | ---
14 |
15 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/13-deep-learning-algorithms.pdf" title="13 - 深度学习算法">}}
16 |
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1 | ---
2 | title: 可重复性研究
3 | author: 范叶亮
4 | date: 2023-01-21
5 | slug: reproducible-research
6 | description: 可重复性研究的目标是将文字说明与分析和数据联系起来，以便重新创建、更好地理解和验证逻辑。
7 | weight: 14
8 | cover: /images/lecture/14-reproducible-research.png
9 | tags:
10 | - Markdown
11 | - Jupyter
12 | - quarto
13 | ---
14 |
15 | {{< pdf-viewer url="https://ds-python-cdn.leovan.tech/lecture/14-reproducible-research.pdf" title="13 - 可重复性研究">}}
16 |
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/content/lecture/_index.md:
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1 | ---
2 | title: 讲义
3 | author: 范叶亮
4 | date: 2023-01-21
5 | ---
6 |
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/data-science-introduction-with-python.Rproj:
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1 | Version: 1.0
2 |
3 | RestoreWorkspace: Default
4 | SaveWorkspace: Default
5 | AlwaysSaveHistory: Default
6 |
7 | EnableCodeIndexing: Yes
8 | UseSpacesForTab: Yes
9 | NumSpacesForTab: 4
10 | Encoding: UTF-8
11 |
12 | RnwWeave: knitr
13 | LaTeX: XeLaTeX
14 |
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/layouts/404.html:
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1 | {{ partial "header.html" . }}
2 |
3 | 
8 | {{ .Params.description | markdownify }}
9 |