Description

Book Synopsis

This book introduces the reader to data science using R and the tidyverse. No prerequisite knowledge is needed in college-level programming or mathematics (e.g., calculus or statistics). The book is self-contained so readers can immediately begin building data science workflows without needing to reference extensive amounts of external resources for onboarding. The contents are targeted for undergraduate students but are equally applicable to students at the graduate level and beyond. The book develops concepts using many real-world examples to motivate the reader.

Upon completion of the text, the reader will be able to:

  • Gain proficiency in R programming

  • Load and manipulate data frames, and tidy them using tidyverse tools

  • Conduct statistical analyses and draw meaningful inferences from them

  • Perform modeling from numerical and textual data

  • Generate data visualizations (numerical and spatial

    Table of Contents

    1. Data Types 2. Data Transformation 3. Data Visualization 4. Building Simulations 5. Sampling 6. Hypothesis Testing 7. Quantifying Uncertainty 8. Towards Normality 9. Regression 10. Text Analysis

Exploring Data Science with R and the Tidyverse

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    £73.14

    Includes FREE delivery

    RRP £76.99 – you save £3.85 (5%)

    Order before 4pm today for delivery by Fri 26 Jun 2026.

    A Paperback by Jerry Bonnell, Mitsunori Ogihara

    15 in stock


      View other formats and editions of Exploring Data Science with R and the Tidyverse by Jerry Bonnell

      Publisher: Taylor & Francis Ltd
      Publication Date: 8/14/2023 12:00:00 AM
      ISBN13: 9781032341705, 978-1032341705
      ISBN10: 103234170X
      Also in:
      Databases / Data management Probability and statistics Environmental science, engineering and technology

      Description

      Book Synopsis

      This book introduces the reader to data science using R and the tidyverse. No prerequisite knowledge is needed in college-level programming or mathematics (e.g., calculus or statistics). The book is self-contained so readers can immediately begin building data science workflows without needing to reference extensive amounts of external resources for onboarding. The contents are targeted for undergraduate students but are equally applicable to students at the graduate level and beyond. The book develops concepts using many real-world examples to motivate the reader.

      Upon completion of the text, the reader will be able to:

      • Gain proficiency in R programming

      • Load and manipulate data frames, and tidy them using tidyverse tools

      • Conduct statistical analyses and draw meaningful inferences from them

      • Perform modeling from numerical and textual data

      • Generate data visualizations (numerical and spatial

        Table of Contents

        1. Data Types 2. Data Transformation 3. Data Visualization 4. Building Simulations 5. Sampling 6. Hypothesis Testing 7. Quantifying Uncertainty 8. Towards Normality 9. Regression 10. Text Analysis

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