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1.1 Python Basic.ipynb
1.1 Python Basic.ipynb
 
 
10.1 Hierarchical Clustering.ipynb
10.1 Hierarchical Clustering.ipynb
 
 
10.2 KMeans.ipynb
10.2 KMeans.ipynb
 
 
10.3 DBSCAN.ipynb
10.3 DBSCAN.ipynb
 
 
10.4 Evaluating The Number of Clusters.ipynb
10.4 Evaluating The Number of Clusters.ipynb
 
 
10.5 Topical Clustering of News.ipynb
10.5 Topical Clustering of News.ipynb
 
 
11.1 Feature Selection.ipynb
11.1 Feature Selection.ipynb
 
 
11.2 PCA.ipynb
11.2 PCA.ipynb
 
 
11.3 SVD.ipynb
11.3 SVD.ipynb
 
 
11.4 Compress Image With SVD.ipynb
11.4 Compress Image With SVD.ipynb
 
 
12.1 Association Rule.ipynb
12.1 Association Rule.ipynb
 
 
12.2 Frequent Pattern Mining.ipynb
12.2 Frequent Pattern Mining.ipynb
 
 
12.3 Market Basket Analysis.ipynb
12.3 Market Basket Analysis.ipynb
 
 
2 Data Collection.ipynb
2 Data Collection.ipynb
 
 
2.7 Web Crawler.ipynb
2.7 Web Crawler.ipynb
 
 
3.1 Pandas Introduction.ipynb
3.1 Pandas Introduction.ipynb
 
 
3.2.2 Series and DataFrame Introduction.ipynb
3.2.2 Series and DataFrame Introduction.ipynb
 
 
3.4 Detecting Missing Value.ipynb
3.4 Detecting Missing Value.ipynb
 
 
3.5 Imputing Missing Value.ipynb
3.5 Imputing Missing Value.ipynb
 
 
3.6 House Data Missing Value Detection and Imputation.ipynb
3.6 House Data Missing Value Detection and Imputation.ipynb
 
 
4.1 Data Transformation.ipynb
4.1 Data Transformation.ipynb
 
 
4.2 Time Conversion.ipynb
4.2 Time Conversion.ipynb
 
 
4.3 Reshaping Data.ipynb
4.3 Reshaping Data.ipynb
 
 
4.4 Regular Expression.ipynb
4.4 Regular Expression.ipynb
 
 
4.5 News Data Preprocessing.ipynb
4.5 News Data Preprocessing.ipynb
 
 
5.1 Descriptive Statistics.ipynb
5.1 Descriptive Statistics.ipynb
 
 
5.2 Visualization.ipynb
5.2 Visualization.ipynb
 
 
5.3 E-Commerce Website Page View Analysis.ipynb
5.3 E-Commerce Website Page View Analysis.ipynb
 
 
6.2 Store Data Into Database.ipynb
6.2 Store Data Into Database.ipynb
 
 
6.3 Analyze Structured Data With SQL Query.ipynb
6.3 Analyze Structured Data With SQL Query.ipynb
 
 
6.4 Store Currency Data Into Database.ipynb
6.4 Store Currency Data Into Database.ipynb
 
 
7.4 Regression.ipynb
7.4 Regression.ipynb
 
 
7.5 Regression Validation.ipynb
7.5 Regression Validation.ipynb
 
 
7.6 Predict House Price With Regression Analysis.ipynb
7.6 Predict House Price With Regression Analysis.ipynb
 
 
8.1 Decision Tree.ipynb
8.1 Decision Tree.ipynb
 
 
8.2 Logistic Regression.ipynb
8.2 Logistic Regression.ipynb
 
 
8.3 SVM.ipynb
8.3 SVM.ipynb
 
 
8.4 Neural Network.ipynb
8.4 Neural Network.ipynb
 
 
8.5 Random Forest.ipynb
8.5 Random Forest.ipynb
 
 
8.6 Build Customer Churn Prediction Model.ipynb
8.6 Build Customer Churn Prediction Model.ipynb
 
 
9.1 Confusion Matrix.ipynb
9.1 Confusion Matrix.ipynb
 
 
9.2 Cross Validation.ipynb
9.2 Cross Validation.ipynb
 
 
9.3 ROC Curve.ipynb
9.3 ROC Curve.ipynb
 
 
9.4 Validate Customer Churn Prediction Model.ipynb
9.4 Validate Customer Churn Prediction Model.ipynb
 
 
README.md
README.md
 
 

Repository files navigation

Python与数据科学实战

==================================

课程影片与投影片

课程大纲

1 Python与数据科学应用

  • 数据科学简介与应用
  • Python与数据科学
  • 安装Anaconda
  • 使用Jupyter Notebook
  • Python 3语法快速简介
  • 数据科学步骤详解
  • 实战范例 - 使用Python计算文章中的字词频率

2 搜集实务

  • 数据型态
  • 结构化vs半结构化vs非结构化数据
  • Python IO与档案处理
  • 处理CSV, Excel格式资料
  • 处理JSON, XML格式数据
  • 撰写网路爬虫搜集网路资料
  • 实战范例 - 房天下(Fang.com)屋价资料搜集

3 资料清理(I)

  • 资料清理概论
  • 简介Pandas -使用Pandas处理资料
  • 资料筛选
  • 侦测遗失值
  • 补齐遗失值
  • 实战范例 - 房屋资料处理

4 资料清理(II)

  • 资料转换
  • 处理时间格式资料
  • 重塑资料
  • 学习正规表达式
  • 实战范例 - 新闻资料处理

5 资料探索与资料视觉化

  • 使用Pandas产生叙述性统计
  • 如何使用Pandas绘制统计图表
  • 实战范例 - 网页浏览纪录资料分析

6 资料储存实务

  • 关联式资料库- SQLite简介
  • 将资料储存至资料库中
  • 使用SQL Query分析结构化资料
  • 实战范例 - 汇率资讯储存与管理

7 使用机器学习建立数据模型(回归模型)

  • 机器学习基础
  • Scikit-Learn套件简介
  • 监督式学习与非监督式学习
  • 回归分析
  • 回归模型评估
  • 实战范例 - 使用回归模型分析房屋价格

8 使用机器学习建立数据模型(分类模型)

  • 资料分类-决策树
  • 资料分类- Logistic Regression
  • 资料分类- SVM
  • 资料分类-类神经网路
  • 资料分类-随机森林
  • 实战范例 - 使用分类模型预测客户流失

9 使用机器学习建立数据模型(验证模型)

  • 混淆矩阵（Confusion Matrix )与其意义
  • 交叉验证(Cross Validation)
  • 使用ROC评估不同分类模型
  • 实战范例 - 评估不同客户流失分析模型

10 使用机器学习建立数据模型(资料分群)

  • 分群方法-阶层式分群
  • 分群方法- Kmeans分群
  • 分群方法- DBScan分群
  • 分群结果评估
  • 实战范例 - 利用分群找出文章主题

11 使用机器学习建立数据模型(特征筛选与降低维度)

  • 特征筛选(Feature Selection)
  • 特征萃取-PCA
  • 特征萃取-SVD
  • 实战范例 - 使用SVD 压缩图档

12 使用机器学习建立数据模型(关联与频繁样式分析篇)

  • 关联分析(Association Rule)
  • 频繁样式探勘(Frequent Pattern Mining)
  • 实战范例 - 购物篮分析实例

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