{"product_id":"practical-time-series-analysis-prediction-with-statistics-and-machine-learning-paperback","title":"Practical Time Series Analysis: Prediction with Statistics and Machine Learning - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eAileen Nielsen\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eTime series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. \u003c\/p\u003e\u003cp\u003e Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challenges in time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. \u003c\/p\u003e\u003cp\u003eYou'll get the guidance you need to confidently: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eFind and wrangle time series data \u003c\/li\u003e\n\u003cli\u003eUndertake exploratory time series data analysis \u003c\/li\u003e\n\u003cli\u003eStore temporal data \u003c\/li\u003e\n\u003cli\u003eSimulate time series data \u003c\/li\u003e\n\u003cli\u003eGenerate and select features for a time series \u003c\/li\u003e\n\u003cli\u003eMeasure error \u003c\/li\u003e\n\u003cli\u003eForecast and classify time series with machine or deep learning \u003c\/li\u003e\n\u003cli\u003eEvaluate accuracy and performance \u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eAileen 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.\u003c\/p\u003e\u003cp\u003eIn 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.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 497\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1 x 9.1 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e November 19, 2019\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":44278707814502,"sku":"9781492041658","price":90.19,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0599\/7255\/0758\/files\/OGFJL0hyVURqSHcxTjY1b05vYkN3UT09.webp?v=1766488171","url":"https:\/\/infinitylightwa.com\/products\/practical-time-series-analysis-prediction-with-statistics-and-machine-learning-paperback","provider":"Infinity Light","version":"1.0","type":"link"}