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《数据科学工程实践》一书的Jupyter Notebook库，以及交流园地。

License: GNU General Public License v3.0

Python 0.06% R 0.05% Jupyter Notebook 99.89%

data_science_industrial_practice's Introduction

数据科学工程实践

用户行为分析与建模、A/B实验、SQLFlow

第1部分（第 1～6 章） 观测数据的分析技术：

  • 第1章：如何分析用户的选择
  • 第2章：随时间可变的行为分析
  • 第3章：洞察消费者长期价值：基于神经网络的LTV建模
  • 第4章：使用体系化分析方法进行场景挖掘
  • 第5章：行为规律的发现与挖掘
  • 第6章：对观测到的事件进行因果推断

第二部分（第7～9章）实验研究探索业务边界

  • 第7章：如何比较两个策略的效果
  • 第8章：如何提高实验效能
  • 第9章：特殊场景下的实验设计和分析方法

第三部分（第10~12章） 自助式数据科学：SQLFlow

  • 第10章：SQLFlow
  • 第11章：机器学习模型可解释性
  • 第12章：基于LSTM-Autoencoder的无监督聚类模型

data_science_industrial_practice's People

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