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R data analysis projects

Author: Gopi Subramanian
Publisher: [Place of publication not identified] : Packt, [2018]
Edition/Format:   eVideo : Clipart/images/graphics : English
Summary:
"R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This video will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any  Read more...
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Material Type: Clipart/images/graphics, Internet resource, Videorecording
Document Type: Internet Resource, Computer File, Visual material
All Authors / Contributors: Gopi Subramanian
OCLC Number: 1028995403
Notes: Title from title screen (viewed March 16, 2018).
Date of publication from resource description page.
Description: 1 online resource (1 streaming video file (4 hr., 8 min., 4 sec.)) : digital, sound, color
Responsibility: Gopi Subramanian.

Abstract:

"R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This video will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You’ll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You’ll implement time-series modeling for anomaly detection and understand cluster analysis for streaming data. You’ll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow code. With the help of these real-world projects, you’ll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The video covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively. By the end of this video, you’ll have a better understanding of data analysis with R, and will be able to put your knowledge to practical use without any hassle"--Resource description page.

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