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.
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.
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. ...
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.
子珂
1 有用 子珂 2020-06-21 08:51:11
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
0 有用 阿道克 2020-05-03 10:28:23
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
1 有用 西瓜头 2020-10-10 03:16:30
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。
0 有用 Axé 2020-08-21 01:46:42
补一下时间序列的框架。感觉研究生训练的设计有点偏，所以metrics一上来讲sigma field 讲non parametrics 证明确不讲基本的forecast, 是默认我们都读了完整的经济硕士吗真是的。。。
0 有用 orange 2021-08-15 21:43:26
有一定前沿性和广度，参考资料值得看看。但深度不够。