Modern Data Science with R, 2/e
暫譯: 現代數據科學與 R，第二版
Baumer, Benjamin S., Kaplan, Daniel T., Horton, Nicholas J.
- 出版商: CRC
- 出版日期: 2021-04-14
- 定價: $3,800
- 售價: 9.5 折 $3,610
- 語言: 英文
- 頁數: 632
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 0367191490
- ISBN-13: 9780367191498
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相關分類:
Data Science
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相關翻譯:
現代數據科學（R語言·第2版） (簡中版)
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商品描述
From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What's more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician).
Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions.
The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.
商品描述(中文翻譯)
從第一版的評論中：「Modern Data Science with R... 充滿了範例，並且有著強烈的敘述聲音。此外，它提供了一個組織框架，令人信服地論證了資料科學是一門與應用統計學不同的課程」（The American Statistician）。
Modern Data Science with R 是一本針對大學生的綜合性資料科學教科書，結合了統計和計算思維，以解決現實世界中的資料問題。這本書不僅僅專注於案例研究或程式語法，而是展示了如何在最先進的 R/RStudio 計算環境中利用統計程式設計，從各種資料中提取有意義的信息，以解決引人注目的問題。
第二版已更新，以反映 tidyverse 套件組日益增長的影響力。書中的所有程式碼都已修訂並重新格式化，以提高可讀性和易懂性。來自 sf、purrr、tidymodels 和 tidytext 等套件的新功能現在已整合到文本中。所有章節均已修訂，並且有幾個章節被拆分、重新組織或重新構思，以符合最佳實踐的變化。
作者簡介
Benjamin S. Baumer is an associate professor in the Statistical & Data Sciences program at Smith College. He has been a practicing data scientist since 2004, when he became the first full-time statistical analyst for the New York Mets. Ben is a co-author of The Sabermetric Revolution and Analyzing Baseball Data with R. He received the 2019 Waller Education Award and the 2016 Significant Contributor Award from the Society for American Baseball Research.
Daniel T. Kaplan is the DeWitt Wallace emeritus professor of mathematics and computer science at Macalester College. He is the author of several textbooks on statistical modeling and statistical computing. Danny received the 2006 Macalester Excellence in Teaching award and the 2017 CAUSE Lifetime Achievement Award.
Nicholas J. Horton is Beitzel Professor of Technology and Society (Statistics and Data Science) at Amherst College. He is a Fellow of the ASA and the AAAS, co-chair of the National Academies Committee on Applied and Theoretical Statistics, recipient of a number of national teaching awards, author of a series of books on statistical computing, and actively involved in data science curriculum efforts to help students "think with data".
作者簡介(中文翻譯)
本傑明·S·鮑默是史密斯學院統計與數據科學計畫的副教授。他自2004年以來一直是一名實踐中的數據科學家，當時他成為紐約大都會隊的第一位全職統計分析師。本傑明是《數據分析革命》和《用R分析棒球數據》的共同作者。他獲得了2019年美國棒球研究學會的沃勒教育獎和2016年顯著貢獻者獎。
丹尼爾·T·卡普蘭是馬卡萊斯特學院數學與計算機科學的德威特·華萊士名譽教授。他是幾本有關統計建模和統計計算的教科書的作者。丹尼獲得了2006年馬卡萊斯特卓越教學獎和2017年CAUSE終身成就獎。
尼古拉斯·J·霍頓是阿默斯特學院技術與社會（統計與數據科學）比茲爾教授。他是美國統計協會（ASA）和美國科學促進會（AAAS）的會士，國家科學院應用與理論統計委員會的共同主席，獲得多項全國教學獎項，並且是統計計算系列書籍的作者，積極參與數據科學課程的努力，幫助學生「用數據思考」。
