Introduction to data analysis in R: hands-on coding, data mining, visualization and statistics from scratch
Material type:
TextPublication details: Switzerland Springer 2020 Description: xv, 276 pISBN: 9783030489960Subject(s): Data mining | R (Computer program language) | StatisticsDDC classification: 519.50285 Summary: About this book
This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications. This textbook is primarily intended for undergraduate students of mathematics, statistics, physics, economics, finance and business who are pursuing a career in data analytics. It will be equally valuable for master students of data science and industry professionals who want to conduct data analyses.
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 519.50285 SAI (Browse shelf(Opens below)) | 1 | Available | 003530 |
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| 519.5 BER Guide to intelligent data science: how to intelligently make use of real data | 519.5 WEI Statistics using R: | 519.5 WOL Primer for data analytics and graduate study in statistics | 519.50285 SAI Introduction to data analysis in R: | 519.502855133 GIL A guide to R for social and behavioral science statistics | 519.502855133 GRO Hands-on programming with R: write your own functions and simulations | 519.502855133 GRU Data science from scratch: first principles with Python |
About this book
This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications. This textbook is primarily intended for undergraduate students of mathematics, statistics, physics, economics, finance and business who are pursuing a career in data analytics. It will be equally valuable for master students of data science and industry professionals who want to conduct data analyses.
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