https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_130.png掌握了基础并分析了案例后接下来我们需要规划如何深化知识和技能。基础知识补充https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_132.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_134.png如果感觉数学、编程或金融基础有待加强以下资源非常适合https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_136.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_138.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_140.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_142.png金融学原理耶鲁大学公开课《Financial Markets》。这门课能帮助你建立正确的金融思维框架。概率论寻找以直觉和思维培养著称的课程例如一位法国教授的课程重在理解“何时以及为何”使用某种方法而非死记公式。多变量微积分MIT或同类高质量公开课巩固数学基础。微分方程有助于培养数学建模能力在金融工程中应用广泛。编程入门MIT《Introduction to Computer Science and Programming Using Python》6.0001。培养计算思维。编程进阶斯坦福《Programming Paradigms》。学习C语言有助于写出更高效的Python代码理解不同编程范式。线性代数Gilbert Strang教授的课程常看常新。量化交易专项如华盛顿大学在Coursera上的《Machine Learning for Trading》系列课程提供了另一个完整的量化学习体系包含大量编程作业。https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_144.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_146.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_148.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_150.png持续学习与信息获取https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_152.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_154.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_156.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_158.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_160.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_162.png跟踪前沿关注新浪微博博主“爱可可-爱生活”他每日整理分享机器学习领域的最新论文和资讯。重点阅读论文摘要理解其“解决了什么问题”以此建立自己的知识索引库。书籍推荐编程《High Performance Python》、《Python Cookbook》。机器学习《Python Machine Learning》、《Machine Learning for Hackers》、《图解机器学习》、《The Elements of Statistical Learning》进阶。金融与量化《主动投资组合管理》“小红书”、《Python金融大数据分析》、《打开量化投资的黑箱》。https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_164.png求职准备与个人品牌建设 学习最终要应用于实践无论是求职还是独立交易都需要做好充分准备。https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_166.png技术面试准备https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_168.png机器学习相关岗位几乎必问的问题如何理解与解决过拟合Overfitting正则化Regularization的意义是什么逻辑回归Logistic Regression的权重是如何学习出来的要求数学推导随机梯度下降SGD的原理是什么支持向量机SVM的原理与支持向量的意义回答时需结合具体模型和实例并准备好应对面试官的深入追问。https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_170.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_172.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_174.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_176.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_178.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_180.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_182.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_184.png非技术面试与简历https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_186.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_188.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_190.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_192.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_194.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_196.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_198.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_200.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_202.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_204.png行为面试准备用STAR法则Situation, Task, Action, Result回答关于领导力、团队合作、失败经历等问题。在自我介绍中巧妙埋下“引子”引导面试官提问你准备充分的方向。简历维护一份详细的“主简历”Master Resume作为个人数据库。针对不同岗位从主简历中提取相关内容定制化投递。简历格式应专业、简洁、左对齐合理使用项目符号Bullet Points用量化成果如“提升性能XX%”代替模糊描述。附上GitHub、个人博客等链接。打造个人品牌https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_206.png在当今时代线上个人品牌至关重要GitHub维护一个活跃的账号包含高质量的开源项目或代码片段。头像、简介要专业。技术博客建立个人网站或使用GitHub Pages定期分享原创技术文章。内容重于形式确保在手机端阅读体验良好。社交媒体在新浪微博、知乎等平台积极进行有营养的原创分享或评论吸引同好关注。可以将博客文章链接分享到微博并相关领域的大V。持续输出坚持写作和分享这不仅是积累影响力的过程更是倒逼自己深入学习、梳理知识体系的最佳方式。不要担心分享会“泄露秘诀”初期的信任积累更重要。课程总结 本节课中我们一起回顾了整个量化交易项目班的学习旅程。我们从数据爬取开始历经数据清洗、金融基础、传统技术与机器学习方法最终将整个流程集成到一个统一的框架和系统中。通过分析正反案例我们明确了高质量量化策略应具备的要素。最后我们为大家规划了未来的学习路径并强调了在求职和个人发展中夯实基础、持续学习以及用心经营个人品牌的重要性。https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_208.png量化交易是一个将知识直接转化为价值的迷人领域。十次课程只是起点希望大家以此为契机保持学习的热情和习惯在接下来的六个月乃至更长时间里沿着自己规划的道路持续精进成为更好的自己。课程有终点学习无止境。感谢大家一路以来的坚持与信任祝大家未来一切顺利 七月在线机器学习量化交易笔记（二）
https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_130.png掌握了基础并分析了案例后接下来我们需要规划如何深化知识和技能。基础知识补充https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_132.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_134.png如果感觉数学、编程或金融基础有待加强以下资源非常适合https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_136.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_138.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_140.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_142.png金融学原理耶鲁大学公开课《Financial Markets》。这门课能帮助你建立正确的金融思维框架。概率论寻找以直觉和思维培养著称的课程例如一位法国教授的课程重在理解“何时以及为何”使用某种方法而非死记公式。多变量微积分MIT或同类高质量公开课巩固数学基础。微分方程有助于培养数学建模能力在金融工程中应用广泛。编程入门MIT《Introduction to Computer Science and Programming Using Python》6.0001。培养计算思维。编程进阶斯坦福《Programming Paradigms》。学习C语言有助于写出更高效的Python代码理解不同编程范式。线性代数Gilbert Strang教授的课程常看常新。量化交易专项如华盛顿大学在Coursera上的《Machine Learning for Trading》系列课程提供了另一个完整的量化学习体系包含大量编程作业。https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_144.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_146.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_148.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_150.png持续学习与信息获取https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_152.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_154.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_156.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_158.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_160.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_162.png跟踪前沿关注新浪微博博主“爱可可-爱生活”他每日整理分享机器学习领域的最新论文和资讯。重点阅读论文摘要理解其“解决了什么问题”以此建立自己的知识索引库。书籍推荐编程《High Performance Python》、《Python Cookbook》。机器学习《Python Machine Learning》、《Machine Learning for Hackers》、《图解机器学习》、《The Elements of Statistical Learning》进阶。金融与量化《主动投资组合管理》“小红书”、《Python金融大数据分析》、《打开量化投资的黑箱》。https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_164.png求职准备与个人品牌建设 学习最终要应用于实践无论是求职还是独立交易都需要做好充分准备。https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_166.png技术面试准备https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_168.png机器学习相关岗位几乎必问的问题如何理解与解决过拟合Overfitting正则化Regularization的意义是什么逻辑回归Logistic Regression的权重是如何学习出来的要求数学推导随机梯度下降SGD的原理是什么支持向量机SVM的原理与支持向量的意义回答时需结合具体模型和实例并准备好应对面试官的深入追问。https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_170.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_172.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_174.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_176.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_178.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_180.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_182.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_184.png非技术面试与简历https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_186.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_188.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_190.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_192.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_194.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_196.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_198.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_200.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_202.pnghttps://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_204.png行为面试准备用STAR法则Situation, Task, Action, Result回答关于领导力、团队合作、失败经历等问题。在自我介绍中巧妙埋下“引子”引导面试官提问你准备充分的方向。简历维护一份详细的“主简历”Master Resume作为个人数据库。针对不同岗位从主简历中提取相关内容定制化投递。简历格式应专业、简洁、左对齐合理使用项目符号Bullet Points用量化成果如“提升性能XX%”代替模糊描述。附上GitHub、个人博客等链接。打造个人品牌https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_206.png在当今时代线上个人品牌至关重要GitHub维护一个活跃的账号包含高质量的开源项目或代码片段。头像、简介要专业。技术博客建立个人网站或使用GitHub Pages定期分享原创技术文章。内容重于形式确保在手机端阅读体验良好。社交媒体在新浪微博、知乎等平台积极进行有营养的原创分享或评论吸引同好关注。可以将博客文章链接分享到微博并相关领域的大V。持续输出坚持写作和分享这不仅是积累影响力的过程更是倒逼自己深入学习、梳理知识体系的最佳方式。不要担心分享会“泄露秘诀”初期的信任积累更重要。课程总结 本节课中我们一起回顾了整个量化交易项目班的学习旅程。我们从数据爬取开始历经数据清洗、金融基础、传统技术与机器学习方法最终将整个流程集成到一个统一的框架和系统中。通过分析正反案例我们明确了高质量量化策略应具备的要素。最后我们为大家规划了未来的学习路径并强调了在求职和个人发展中夯实基础、持续学习以及用心经营个人品牌的重要性。https://github.com/OpenDocCN/dsai-notes-pt1-zh/raw/master/docs/julyedu-quant/img/d448b74723c629b9500412a8d9a77ed6_208.png量化交易是一个将知识直接转化为价值的迷人领域。十次课程只是起点希望大家以此为契机保持学习的热情和习惯在接下来的六个月乃至更长时间里沿着自己规划的道路持续精进成为更好的自己。课程有终点学习无止境。感谢大家一路以来的坚持与信任祝大家未来一切顺利