Exploratory Data Analysis Using R
暫譯: 使用 R 進行探索性資料分析
Ronald K. Pearson
- 出版商: Chapman and Hall/CRC
- 出版日期: 2018-09-04
- 售價: $2,750
- 貴賓價: 9.5 折 $2,613
- 語言: 英文
- 頁數: 562
- 裝訂: Paperback
- ISBN: 149873023X
- ISBN-13: 9781498730235
-
相關分類:
R 語言、Data Science、Data-mining
海外代購書籍(需單獨結帳)
買這商品的人也買了...
-
$690$587 -
$730$657 -
$360$353 -
$380$342 -
$450$383 -
$750$638 -
$580$522 -
$534$507 -
$680$612 -
$1,390$1,321 -
$620$489 -
$560$476 -
$2,100$2,058 -
$780$616 -
$420$357 -
$534$507 -
$580$522 -
$1,390$1,321 -
$540$529 -
$560$437 -
$680$537 -
$680$537 -
$580$452 -
$840$798 -
$3,116Measuring Esg Effects in Systematic Investing
相關主題
商品描述
Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data.
The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing.
The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available.
About the Author:
Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).
商品描述(中文翻譯)
《使用 R 進行探索性資料分析》提供了一個經過課堂測試的探索性資料分析(EDA)入門,介紹了資料中可以找到的各種「有趣」特徵——好的、壞的和醜陋的,以及為什麼找到這些特徵是重要的。它還介紹了使用 R 來探索和解釋資料的機制。
本書首先詳細概述了資料、探索性分析和 R,以及 R 中的圖形。接著探討了如何處理外部資料、線性回歸模型和編寫資料故事。本書的第二部分專注於開發 R 程式,包括良好的程式設計實踐和範例、處理文本資料以及一般預測模型。本書最後一章討論了「保持一切整合」的主題,包括管理 R 的安裝、管理檔案、文件記錄,以及可重現計算的介紹。
本書旨在為高年級本科生、入門級研究生以及對資料分析、建模、統計或程式設計幾乎沒有接觸的在職專業人士設計。儘管資料分析本質上是一個數學主題,但本書的處理相對非數學化。大多數章節末尾都包含練習題,並提供了教師解答手冊。
**關於作者:**
**Ronald K. Pearson** 擔任位於加州費爾菲爾德的財產保險公司 GeoVera 的高級資料科學家,並曾在多個應用領域擔任類似職位,包括軟體開發、藥物安全資料分析和工業過程資料分析。他擁有麻省理工學院電機工程與計算機科學的博士學位,並在會議和期刊上發表了從非線性動態模型結構選擇到預測建模中隱藏缺失資料問題等主題的論文。Pearson 博士著有或合著了包括《工程、科學和醫學中的資料探索》(牛津大學出版社,2011年)和《使用 Python 的非線性數位濾波》在內的書籍。他還是 DataCamp 基礎 R 圖形課程的開發者,並且是可從 CRAN(綜合 R 存檔網路)獲得的 datarobot 和 GoodmanKruskal R 套件的作者。