Practical Time Series Analysis: Prediction with Statistics and Machine Learning

Aileen Nielsen

400 pages, parution le 30/10/2019

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Résumé

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.

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.