Statistical Inference via Data Science by Ismay & Kim
Statistical Inference via Data Science - Table of Contents
PART I – GETTING STARTED WITH DATA IN R
1. Getting Started with Data in R
PART II – DATA SCIENCE WITH TIDYVERSE
2. Data Visualization
3. Data Wrangling
4. Data Importing and “Tidy” Data
PART III – DATA MODELING WITH MODERNDIVE
5. Basic Regression
6. Multiple Regression
PART IV – STATISTICAL INFERENCE WITH INFER
7. Sampling
8. Bootstrapping and Confidence Intervals
9. Hypothesis Testing
10. Inference for Regression
PART V – CONCLUSION
11. Tell Your Story with Data
A – Statistical Background
B – Versions of R Packages Used
What You Will Learn in Statistical Inference via Data Science
"Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" by Chester Ismay and Albert Y. Kim is a modern, hands-on guide to learning "statistical inference" through practical "data science" techniques. The book emphasizes learning through real datasets, teaching readers to think critically about data while using modern tools in the "R programming language". It balances theory with practice, making complex concepts like hypothesis testing and confidence intervals accessible for students and beginners.
The book introduces data wrangling, visualization, and manipulation using the "tidyverse" suite of R packages, including "ggplot2" and "dplyr". Readers then learn simulation-based inference, regression modeling, and hypothesis testing using the "infer" package. By integrating statistical inference with computational workflows, the book shows how to explore patterns, fit models, and make evidence-based decisions. Emphasis is placed on understanding the reasoning behind statistical methods rather than just applying formulas.
Ideal for students and professionals entering applied statistics or data science, this book equips readers with practical skills in "data analysis", "visualization", and computational "inference". By combining clear explanations, reproducible examples, and real-world datasets, "Statistical Inference via Data Science" builds a solid foundation in modern statistical thinking, preparing learners to confidently analyze data, conduct hypothesis tests, and draw meaningful conclusions in research and professional applications.
Book Details & Specifications
Title:
Statistical Inference via Data Science by Ismay & Kim
Publisher:
University of Zagreb (FOI)
Year:
2025
Pages:
461
Type:
PDF
Language:
English
ISBN-10 #:
0367409828
ISBN-13 #:
978-0367409821
License:
CC BY-NC-SA 4.0
Amazon:
Amazon
About the Author: Chester Ismay and Albert Y. Kim
The author
Chester Ismay and Albert Y. Kim
are American statisticians and educators known for their work in "statistical inference" and "data science". Ismay earned his "PhD in Statistics" from Arizona State University and has experience as an actuary, consultant, and R package developer. Kim holds a "PhD in Statistics" from the University of Washington and is an Associate Professor at Smith College, with industry experience at Google. Their expertise includes "R programming, tidyverse tools, simulation-based methods, data visualization, and modern data analysis". Together, they authored "Statistical Inference via Data Science" to teach practical, hands-on statistical reasoning, helping students and professionals apply modern techniques effectively with real-world data.
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