作者简介

Aileen has worked in corporate law, physics research labs, and, most recently, a variety of NYC tech startups. Her interests range from defensive software engineering to UX designs for reducing cognitive load to the interplay between law and technology. Aileen is currently working at an early-stage NYC startup that has something to do with time series data and neural networks. She also serves as chair of the New York City Bar Association’s Science and Law committee, which focuses on how the latest developments in science and computing should be regulated and how such developments should inform existing legal practices.
In the recent past, Aileen worked at mobile health platform One Drop and on Hillary Clinton's presidential campaign. She is a frequent speaker at machine learning conferences on both technical and sociological subjects. She holds an A.B. from Princeton University and is A.B.D. in Applied Physics at Columbia University.

内容简介

Solve the most common data engineering and analysis challenges for modern time series data. This book provides an accessible well-rounded introduction to time series in both R and Python that will have software engineers, data scientists, and researchers up and running quickly and competently to do time-related analysis in their field of interest.

Author Aileen Nielsen also offers practical guidance and use cases from the real world, ranging from healthcare and finance to scientific measurements and social science projections. This book offers a more varied and cutting-edge approach to time series than is available in existing books on this topic.


Aileen has worked in corporate law, physics research labs, and, most recently, a variety of NYC tech startups. Her interests range from defensive software engineering to UX designs for reducing cognitive load to the interplay between law and technology. Aileen is currently working at an early-stage NYC startup that has something to do with time series data and neural networks. ...

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豆瓣评论

  • 子珂
    作为介绍不错,但看完了还是"不会"使用时间序列; 感觉缺乏一些足够深入的例子06-21
  • 阿道克
    突出了时间序列预测的机器学习方法。工具上混用了R和python,其中用到的R工具太老了05-03
  • 西瓜头
    感觉真的太基础了。不过当作初学, 对于涉及的方法有一个大致的了解,还是适合的。10-10
  • 邢爱君
    R和python混用,介绍了时间序列分析常见的陷阱和原因。如果你的时间序列模型经常失效,或许能在书中找到答案。11-08

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