Statistical Inference via Data Science: A ModernDive into R and the Tidyverse
暫譯: 透過數據科學的統計推斷：深入探索 R 與 Tidyverse
Ismay, Chester, Kim, Albert Y., Valdivia, Arturo
- 出版商: CRC
- 出版日期: 2025-05-02
- 售價: $7,100
- 貴賓價: 9.5 折 $6,745
- 語言: 英文
- 頁數: 456
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 103272451X
- ISBN-13: 9781032724515
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相關分類:
R 語言、Data Science
尚未上市，無法訂購
商品描述
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse, Second Edition offers a comprehensive guide to learning statistical inference with data science tools widely used in industry, academia, and government. The first part of this book introduces the tidyverse suite of R packages, including ggplot2 for data visualization and dplyr for data wrangling. The second part introduces data modeling via simple and multiple linear regression. The third part presents statistical inference using simulation-based methods within a general framework implemented in R via the infer package, a suitable complement to the tidyverse. By working with these methods, readers can implement effective exploratory data analyses, conduct statistical modeling with data, and carry out statistical inference via confidence intervals and hypothesis testing. All of these tasks are performed by strongly emphasizing data visualization.
Key Features in the Second Edition:
- Minimal Prerequisites: No prior calculus or coding experience is needed, making the content accessible to a wide audience.
- Real-World Data: Learn with real-world datasets, including all domestic flights leaving New York City in 2023, the Gapminder project, FiveThirtyEight.com data, and new datasets on health, global development, music, coffee quality, and geyser eruptions.
- Simulation-Based Inference: Statistical inference through simulation-based methods.
- Expanded Theoretical Discussions: Includes deeper coverage of theory-based approaches, their connection with simulation-based approaches, and a presentation of intuitive and formal aspects of these methods.
- Enhanced Use of the infer Package: Leverages the infer package for "tidy" and transparent statistical inference, enabling readers to construct confidence intervals and conduct hypothesis tests through multiple linear regression and beyond.
- Dynamic Online Resources: All code and output are embedded in the text, with additional interactive exercises, discussions, and solutions available online.
- Broadened Applications: Suitable for undergraduate and graduate courses, including statistics, data science, and courses emphasizing reproducible research.
The first edition of the book has been used in so many different ways--for courses in statistical inference, statistical programming, business analytics, and data science for social policy, and by professionals in many other means. Ideal for those new to statistics or looking to deepen their knowledge, this edition provides a clear entry point into data science and modern statistical methods.
商品描述(中文翻譯)
《統計推論與資料科學：R 與 Tidyverse 的現代探索（第二版）》提供了一個全面的指南，幫助讀者學習使用在業界、學術界和政府廣泛應用的資料科學工具進行統計推論。本書的第一部分介紹了 R 套件的 tidyverse 套件，包括用於資料視覺化的 ggplot2 和用於資料整理的 dplyr。第二部分介紹了透過簡單和多重線性回歸進行資料建模。第三部分則使用基於模擬的方法進行統計推論，這些方法在 R 中透過 infer 套件實現，這是對 tidyverse 的適當補充。透過這些方法，讀者可以實施有效的探索性資料分析，進行資料的統計建模，並透過信賴區間和假設檢定進行統計推論。所有這些任務都強調資料視覺化的重要性。
第二版的主要特色：
- 最小的先備知識：不需要先前的微積分或程式編碼經驗，使內容對廣泛的讀者群體可及。
- 真實世界的資料：使用真實世界的資料集進行學習，包括2023年所有從紐約市出發的國內航班、Gapminder 專案、FiveThirtyEight.com 的資料，以及有關健康、全球發展、音樂、咖啡品質和間歇泉噴發的新資料集。
- 基於模擬的推論：透過基於模擬的方法進行統計推論。
- 擴展的理論討論：包括對基於理論的方法的更深入探討，這些方法與基於模擬的方法的聯繫，以及這些方法的直觀和正式方面的介紹。
- 增強的 infer 套件使用：利用 infer 套件進行「整潔」和透明的統計推論，使讀者能夠透過多重線性回歸及其他方法構建信賴區間和進行假設檢定。
- 動態的線上資源：所有程式碼和輸出都嵌入在文本中，並提供額外的互動練習、討論和解決方案可在線上獲得。
- 擴大應用範圍：適用於本科和研究生課程，包括統計學、資料科學以及強調可重複研究的課程。
本書的第一版已被用於多種不同的方式——用於統計推論、統計程式設計、商業分析和社會政策的資料科學課程，以及許多其他專業用途。這一版非常適合對統計學感興趣的新手或希望深化知識的讀者，提供了一個清晰的進入資料科學和現代統計方法的切入點。
作者簡介
Chester Ismay is Vice President of Data and Automation at MATE Seminars and is a freelance data science consultant and instructor. He also teaches in the Center for Executive and Professional Education at Portland State University. He completed his PhD in statistics from Arizona State University in 2013. He has previously worked in various roles, including as an actuary at Scottsdale Insurance Company (now Nationwide E&S/Specialty) and at Ripon College, Reed College, and Pacific University. He has experience working in online education and was previously a Data Science Evangelist at DataRobot, where he led data science, machine learning, and data engineering in-person and virtual workshops for DataRobot University. In addition to his work for *ModernDive*, he contributed as the initial developer of the `infer` R package and is the author and maintainer of the `thesisdown` R package.
Albert Y. Kim is an Associate Professor of Statistical & Data Sciences at Smith College in Northampton, MA, USA. He completed his PhD in statistics at the University of Washington in 2011. Previously he worked in the Search Ads Metrics Team at Google Inc.\ as well as at Reed, Middlebury, and Amherst Colleges. In addition to his work for *ModernDive*, he is a co-author of the `resampledata` and `SpatialEpi` R packages. Both Dr. Kim and Dr. Ismay, along with Jennifer Chunn, are co-authors of the `fivethirtyeight` package of code and datasets published by the data journalism website FiveThirtyEight.com.
Arturo Valdivia is a Senior Lecturer in the Department of Statistics at Indiana University, Bloomington. He earned his PhD in Statistics from Arizona State University in 2013. His research interests focus on statistical education, exploring innovative approaches to help students grasp complex ideas with clarity. Over his career, he has taught a wide range of statistics courses, from introductory to advanced levels, to more than 1,800 undergraduate students and over 900 graduate students pursuing master's and Ph.D. programs in statistics, data science, and other disciplines. In recognition of his teaching excellence, he received Indiana University's Trustees Teaching Award in 2023.
作者簡介(中文翻譯)
切斯特·伊斯梅（Chester Ismay）是MATE Seminars的數據與自動化副總裁，同時也是一名自由職業的數據科學顧問和講師。他還在波特蘭州立大學的高階與專業教育中心教授課程。他於2013年在亞利桑那州立大學獲得統計學博士學位。他曾在多個角色中工作，包括在斯科茨代爾保險公司（現為Nationwide E&S/Specialty）擔任精算師，以及在Ripon College、Reed College和Pacific University工作。他在在線教育方面有經驗，曾擔任DataRobot的數據科學推廣者，負責為DataRobot University主辦面對面和虛擬的數據科學、機器學習和數據工程工作坊。除了為*ModernDive*工作外，他還是`infer` R套件的初始開發者，並且是`thesisdown` R套件的作者和維護者。
阿爾伯特·金（Albert Y. Kim）是美國麻薩諸塞州北安普頓的史密斯學院（Smith College）統計與數據科學副教授。他於2011年在華盛頓大學獲得統計學博士學位。之前，他曾在谷歌公司（Google Inc.）的搜索廣告指標團隊工作，並在Reed、Middlebury和Amherst學院任教。除了為*ModernDive*工作外，他還是`resampledata`和`SpatialEpi` R套件的共同作者。金博士和伊斯梅博士，以及珍妮佛·春（Jennifer Chunn），共同編寫了數據新聞網站FiveThirtyEight.com發佈的`fivethirtyeight`代碼和數據集。
阿圖羅·瓦爾迪維亞（Arturo Valdivia）是印第安納大學布盧明頓校區統計系的高級講師。他於2013年在亞利桑那州立大學獲得統計學博士學位。他的研究興趣集中在統計教育上，探索創新的方法幫助學生清晰地理解複雜的概念。在他的職業生涯中，他教授了從入門到高級的各類統計課程，教導了超過1,800名本科生和900多名攻讀碩士和博士學位的研究生，涵蓋統計學、數據科學及其他學科。因其卓越的教學表現，他於2023年獲得印第安納大學董事會教學獎。
