R: Recipes for Analysis, Visualization and Machine Learning
Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning
Published on 18. November 2016
Book
Paperback/Softback
976 pages
978-1-78728-959-8 (ISBN)
Description
Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning
About This Book
* Proficiently analyze data and apply machine learning techniques
* Generate visualizations, develop interactive visualizations and applications to understand various data exploratory functions in R
* Construct a predictive model by using a variety of machine learning packages
Who This Book Is For
This Learning Path is ideal for those who have been exposed to R, but have not used it extensively yet. It covers the basics of using R and is written for new and intermediate R users interested in learning. This Learning Path also provides in-depth insights into professional techniques for analysis, visualization, and machine learning with R - it will help you increase your R expertise, regardless of your level of experience.
What You Will Learn
* Get data into your R environment and prepare it for analysis
* Perform exploratory data analyses and generate meaningful visualizations of the data
* Generate various plots in R using the basic R plotting techniques
* Create presentations and learn the basics of creating apps in R for your audience
* Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm
* Visualize associations in various graph formats and find frequent itemset using the ECLAT algorithm
* Build, tune, and evaluate predictive models with different machine learning packages
* Incorporate R and Hadoop to solve machine learning problems on big data
In Detail
The R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We'll start off with data analysis - this will show you ways to use R to generate professional analysis reports. We'll then move on to visualizing our data - this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we'll move into the world of machine learning - this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction.
This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:
* R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan
* R Data Visualization Cookbook by Atmajitsinh Gohil
* Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu)
Style and approach
This course creates a smooth learning path that will teach you how to analyze data and create stunning visualizations. The step-by-step instructions provided for each recipe in this comprehensive Learning Path will show you how to create machine learning projects with R.
About This Book
* Proficiently analyze data and apply machine learning techniques
* Generate visualizations, develop interactive visualizations and applications to understand various data exploratory functions in R
* Construct a predictive model by using a variety of machine learning packages
Who This Book Is For
This Learning Path is ideal for those who have been exposed to R, but have not used it extensively yet. It covers the basics of using R and is written for new and intermediate R users interested in learning. This Learning Path also provides in-depth insights into professional techniques for analysis, visualization, and machine learning with R - it will help you increase your R expertise, regardless of your level of experience.
What You Will Learn
* Get data into your R environment and prepare it for analysis
* Perform exploratory data analyses and generate meaningful visualizations of the data
* Generate various plots in R using the basic R plotting techniques
* Create presentations and learn the basics of creating apps in R for your audience
* Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm
* Visualize associations in various graph formats and find frequent itemset using the ECLAT algorithm
* Build, tune, and evaluate predictive models with different machine learning packages
* Incorporate R and Hadoop to solve machine learning problems on big data
In Detail
The R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We'll start off with data analysis - this will show you ways to use R to generate professional analysis reports. We'll then move on to visualizing our data - this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we'll move into the world of machine learning - this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction.
This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:
* R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan
* R Data Visualization Cookbook by Atmajitsinh Gohil
* Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu)
Style and approach
This course creates a smooth learning path that will teach you how to analyze data and create stunning visualizations. The step-by-step instructions provided for each recipe in this comprehensive Learning Path will show you how to create machine learning projects with R.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 52 mm
Weight
1780 gr
ISBN-13
978-1-78728-959-8 (9781787289598)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Viswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in Artificial Intelligence, Viswa spent a decade in academia and then switched to a leadership position in the software industry for another decade during which he worked for Infosys, Igate, and Starbase. He embraced academia once again in 2001.
Viswa has taught extensively in fields ranging from operations research, computer science, software engineering, management information systems, and enterprise systems. In addition to university teaching, Viswa has conducted training programs for industry professionals and has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education. He has authored a book titled Data Analytics with R:A hands-on approach.
Viswa thoroughly enjoys hands-on software development and has single-handedly conceived, architected, developed, and deployed several web-based applications.
Apart from his deep interest in technical fields such as data analytics, artificial intelligence, computer science, and software engineering, Viswa harbors a deep interest in education with special emphasis on the roots of learning and methods to foster deeper learning. He has done research in this area and hopes to pursue the subject further.
Viswa would like to express deep gratitude to professors Amitava Bagchi and Anup Sen, who were inspirational forces during his early research career. He is also grateful to several extremely intelligent colleagues, notable among them being Rajesh Venkatesh, Dan Richner, and Sriram Bala, who significantly shaped his thinking. His aunt, Analdavalli; his sister, Sankari; and his wife, Shanthi, taught him much about hard work, and even the little he has absorbed has helped him immensely. His sons, Nitin and Siddarth, have helped with numerous insightful comments on various topics. Shanthi Viswanathan is an experienced technologist who has delivered technology management and enterprise architecture consulting to many enterprise customers. She has worked for Infosys Technologies, Oracle Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE, among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling. When she is not in front of her Mac, Shanthi spends time hiking in the suburbs of NY/NJ, working in the garden, and teaching yoga.
Shanthi would like to thank her husband, Viswa, for all the great discussions on numerous topics during their hikes together and for exposing her to R and Java. She would also like to thank her sons, Nitin and Siddarth, for getting her into the data analytics world. Atmajitsinh Gohil works as a senior consultant at a consultancy firm in New York City. After graduating, he worked in the financial industry as a Fixed Income Analyst. He writes about data manipulation, data exploration, visualization, and basic R plotting functions on his blog.
Atmajitsinh has a master's degree in Financial Economics from the State University of New York (SUNY), Buffalo. He also graduated with a master of arts degree in Economics from University of Pune, India. He loves to read blogs on data visualization and loves to go out on hikes in his free time. Yu-Wei, Chiu (David Chiu) is the founder of LargitData. He has previously worked for Trend Micro as a software engineer with the responsibility of building big data platforms for business intelligence and customer relationship management systems. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on Python, R, Hadoop, and tech talks at a variety of conferences.
In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook (Packt Publishing).
Viswa has taught extensively in fields ranging from operations research, computer science, software engineering, management information systems, and enterprise systems. In addition to university teaching, Viswa has conducted training programs for industry professionals and has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education. He has authored a book titled Data Analytics with R:A hands-on approach.
Viswa thoroughly enjoys hands-on software development and has single-handedly conceived, architected, developed, and deployed several web-based applications.
Apart from his deep interest in technical fields such as data analytics, artificial intelligence, computer science, and software engineering, Viswa harbors a deep interest in education with special emphasis on the roots of learning and methods to foster deeper learning. He has done research in this area and hopes to pursue the subject further.
Viswa would like to express deep gratitude to professors Amitava Bagchi and Anup Sen, who were inspirational forces during his early research career. He is also grateful to several extremely intelligent colleagues, notable among them being Rajesh Venkatesh, Dan Richner, and Sriram Bala, who significantly shaped his thinking. His aunt, Analdavalli; his sister, Sankari; and his wife, Shanthi, taught him much about hard work, and even the little he has absorbed has helped him immensely. His sons, Nitin and Siddarth, have helped with numerous insightful comments on various topics. Shanthi Viswanathan is an experienced technologist who has delivered technology management and enterprise architecture consulting to many enterprise customers. She has worked for Infosys Technologies, Oracle Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE, among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling. When she is not in front of her Mac, Shanthi spends time hiking in the suburbs of NY/NJ, working in the garden, and teaching yoga.
Shanthi would like to thank her husband, Viswa, for all the great discussions on numerous topics during their hikes together and for exposing her to R and Java. She would also like to thank her sons, Nitin and Siddarth, for getting her into the data analytics world. Atmajitsinh Gohil works as a senior consultant at a consultancy firm in New York City. After graduating, he worked in the financial industry as a Fixed Income Analyst. He writes about data manipulation, data exploration, visualization, and basic R plotting functions on his blog.
Atmajitsinh has a master's degree in Financial Economics from the State University of New York (SUNY), Buffalo. He also graduated with a master of arts degree in Economics from University of Pune, India. He loves to read blogs on data visualization and loves to go out on hikes in his free time. Yu-Wei, Chiu (David Chiu) is the founder of LargitData. He has previously worked for Trend Micro as a software engineer with the responsibility of building big data platforms for business intelligence and customer relationship management systems. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on Python, R, Hadoop, and tech talks at a variety of conferences.
In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook (Packt Publishing).