Overview
- Learn R through structured and optimized, real project examples
- Covers applied statistics using R, first by learning R basics, then applying to data visualizations, descriptive, inferential and regressions-based statistics
- Explore data science using applied statistics and data visualization
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Table of contents (6 chapters)
Keywords
About this book
Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations.
What You Will Learn
- Discover R, statistics, data science, data mining, and big data
- Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions
- Work with descriptive statistics
- Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots
- Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions
Who This Book Is For
Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations.
Authors and Affiliations
About the author
He holds a Masters of Technology degree from the National University of Singapore, an Executive MBA degree from U21Global (currently GlobalNxt) and IGNOU, a Graduate Diploma in Mechatronics from A*STAR SIMTech (a national research institute located in Nanyang Technological University), and Coursera Specialization Certificate in Business Statistics and Analysis from Rice University. He possessed a Bachelor of Science degree in Computing from the University of Portsmouth after National Service. He is also a AIIM Certified Business Process Management Master (BPMM), GSTF certified Big Data Science Analyst (CBDSA), and IES Certified Lecturer.
Bibliographic Information
Book Title: Learn R for Applied Statistics
Book Subtitle: With Data Visualizations, Regressions, and Statistics
Authors: Eric Goh Ming Hui
DOI: https://doi.org/10.1007/978-1-4842-4200-1
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Eric Goh Ming Hui 2019
Softcover ISBN: 978-1-4842-4199-8Published: 01 December 2018
eBook ISBN: 978-1-4842-4200-1Published: 30 November 2018
Edition Number: 1
Number of Pages: XV, 243
Number of Illustrations: 111 b/w illustrations
Topics: Programming Languages, Compilers, Interpreters, Big Data, Probability and Statistics in Computer Science, Open Source