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Image of - An Introduction To Data Analysis In R Hands On Coding Data Mining Visualization And Statistics From Scratch | Paperback
An Introduction To Data Analysis In R Hands On Coding Data Mining Visualization And Statistics From Scratch | Paperback

An Introduction To Data Analysis In R Hands On Coding Data Mining Visualization And Statistics From Scratch | Paperback

by Saiz A.Z.

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.

Highlights

  • binding-icon

    9783030489960

    ISBN:

  • binding-icon

    Saiz A.Z.

    Author:

  • binding-icon

    276

    Pages:

  • binding-icon

    215 gm

    Weight:

  • langauage-icon

    English

    Language:

  • date-icon

    2020

    Year:

  • edition-icon

    N/A

    Edition:

  • binding-icon

    Paperback

    Binding:

₹6466

₹8083

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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