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<!DOCTYPE HTML>
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<title>Introduction · 大数据技术与算法Checklist</title>
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Introduction
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<li class="chapter " data-level="1.2" data-path="GradientDescent.html">
<a href="GradientDescent.html">
梯度下降
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<li class="chapter " data-level="1.3" data-path="LinearRegression.html">
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线性回归
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<li class="chapter " data-level="1.4" data-path="LogisticRegression.html">
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逻辑回归
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<li class="chapter " data-level="1.5" data-path="DecisionTree.html">
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决策树
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A/B实验
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时间序列
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维度建模
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用户行为分析平台
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SQL要点
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SQL案例
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Kafka
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Spark
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Flink
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ClickHouse
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概率论与数理统计
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Python虚拟环境
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Spark本地安装部署
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Git Cheat Sheet
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<h1>
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<h1 id="introduction">Introduction</h1>
<p>工作中涉及到了数据领域的方方面面，包括大数据技术、机器学习、深度学习、推荐系统、数据产品、数据分析等非常多的领域。然而由于工作重心不断变换，一段时间不接触，很多领域的基础知识非常容易遗忘，非常可惜。重新查阅相关资料非常耗时，有些基于自身理解的概念想法，即使查阅资料也并不容易找回。</p>
<p>对于查阅资料。网络上各种资料教程非常多，也不乏高质量的课程。但是问题也很明显，多数教程面向初学者，极其细致，看完往往需要很长时间，学习效率较低。尤其视频教程，甚至很难快速直接找到关注点。对于在该领域已经建立整体概念框架的人来说，需要的往往是提纲挈领，直击要害，把书读薄。</p>
<p>故做此Checklist，帮助自己总结归纳知识，即使后面忘记了也不再需要手忙脚乱的到处查阅资料。也帮助需要复习相关领域知识点的小伙伴快速恢复记忆。务必做到言简意赅，突出重点。</p>
<p>如果可能，希望能写成一本数据工作者实用手册。</p>
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