Big Data Meets Survey Science. A Collection of Innovative Methods, Hardback - ***

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Publisher: University of California PressAuthor(s): Thomas RamgeNumber of pages: 220Publication date: 2022Dimensions: 148 x 217 x 24Cover type: Har...

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Presenting the latest research on the methods and applications of big data in psychology.Publisher: Hogrefe PublishingNumber of pages: 80Collection...

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DescriptionProvides an up-to-date analysis of big data and multi-agent systems The term Big Data refers to the cases, where data sets are too large...

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This book constructs the mathematical apparatus of classical mechanics from the beginning, examining basic problems in dynamics like the theory of ...

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Big Data is a big topic, based on simple principles. Guided by leading expert in the field, David Stephenson, you will be amazed at how you can tra...

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The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods a...

This practical guide covers the essential tasks in statistical data analysis encountered in high energy physics and provides comprehensive advice f...

We all have endless tools that teach us how to use Tableau software. Blogs posts, user forums, and online tutorials teach the technical skills, but...

Want to build an evolutionary tree? Here's your chance to learn how. The field of bioinformatics was born out of the need to manage, analyze, and e...

The world today is driven by Industry 4.0, which is generating data at an uncontrolled pace. This book examines Big Data and its applications. It e...

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Publisher: Elsevier Science Publishing Co IncAuthor(s): Moses Eterigho (Department of Physics, Covenant University, Nigeria) EmetereNumber of pages...

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Publisher: Institute of Physics PublishingAuthor(s): Professor Dr Ramakrishna (University of Massachusetts, USA) JanaswamyIllustration(s): With fig...

Publisher: New Dawn PressIllustration(s): tablesNumber of pages: 264Publication date: 2004Dimensions: 221 x 149 x 20Cover type: HardbackEditor(s): ...

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