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Open source software for statistical...

Niu, Gao, (1987-)

Open source software for statistical analysis of big dataemerging research and opportunities /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Open source software for statistical analysis of big dataRichard S. Segall and Gao Niu, editors.
其他題名:	emerging research and opportunities /
其他作者:	Segall, Richard,
出版者:	Hershey, Pennsylvania :IGI Global,2020.
面頁冊數:	1 online resource (xiv, 237 p.)
標題:	StatisticsData processing.
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-2768-9
ISBN:	9781799827702 (ebk.)

Open source software for statistical analysis of big dataemerging research and opportunities /
Open source software for statistical analysis of big dataemerging research and opportunities /[electronic resource] :Richard S. Segall and Gao Niu, editors. - Hershey, Pennsylvania :IGI Global,2020. - 1 online resource (xiv, 237 p.)

Includes bibliographical references and index.

Chapter 1. What is open source software (OSS) and what is big data? -- Chapter 2. Open source software (OSS) for big data -- Chapter 3. Introduction to the popular open source statistical software (OSSS) -- Chapter 4. Cluster analysis in R with big data applications -- Chapter 5. Generalized linear model for automobile fatality rate prediction in R -- Chapter 6. Introduction to python and its statistical applications -- Chapter 7. A comparison of machine learning algorithms of big data for time series forecasting using python.

"This book explores topics in the field of open source software for big data"--

ISBN: 9781799827702 (ebk.)Subjects--Topical Terms:

183693
Statistics
--Data processing.

LC Class. No.: QA276.4 / .O64 2020

Dewey Class. No.: 005.7

Open source software for statistical analysis of big dataemerging research and opportunities /
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505 0 $a Chapter 1. What is open source software (OSS) and what is big data? -- Chapter 2. Open source software (OSS) for big data -- Chapter 3. Introduction to the popular open source statistical software (OSSS) -- Chapter 4. Cluster analysis in R with big data applications -- Chapter 5. Generalized linear model for automobile fatality rate prediction in R -- Chapter 6. Introduction to python and its statistical applications -- Chapter 7. A comparison of machine learning algorithms of big data for time series forecasting using python.
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