Statistical Practice for Data Science: With Hands-On Illustrations Using R
暫譯: 數據科學的統計實務：使用 R 的實作示例
Ravishanker, Nalini, Gopalakrishnan, Asha, Bar, Haim
商品描述
Statistical Practice for Data Science: with Hands-on Illustrations using R is a comprehensive guide designed to equip students from diverse fields--engineering, science, and the biological, physical, and social sciences--with the statistical tools and techniques essential for data science. This book bridges the gap between theoretical concepts and practical applications, offering a clear and accessible introduction to statistics with minimal mathematical prerequisites. With a focus on real-world datasets and hands-on implementation using R, it empowers students to analyze, interpret, and communicate data effectively.
The book begins with foundational concepts in probability and statistics, ensuring that students with only college-level algebra can grasp the material. It progresses through key topics such as data visualization, hypothesis testing, regression modeling, and modern machine learning methods like random forests and gradient boosting. Each chapter is enriched with practical examples and coding exercises in R, making it an invaluable resource for students embarking on a data science program.
Designed as a one-semester course, the book provides flexibility for instructors to tailor the content to their curriculum. Whether exploring generalized linear models, mixed-effects models, or dependent data analysis, students will gain a deep understanding of statistical methods and their applications across various domains. By the end of the book, readers will be equipped to make informed decisions, quantify uncertainty, and communicate their findings effectively.
This book is not just a learning tool--it's a practical companion for aspiring data scientists seeking to master statistical practice and R programming.
商品描述(中文翻譯)
《統計實務與資料科學：使用 R 的實作示例》是一本全面的指南，旨在為來自不同領域的學生——工程、科學以及生物、物理和社會科學——提供資料科學所需的統計工具和技術。本書彌合了理論概念與實際應用之間的鴻溝，提供了一個清晰且易於理解的統計入門，對數學的要求極低。專注於真實世界的數據集和使用 R 的實作，讓學生能夠有效地分析、解釋和傳達數據。
本書從概率和統計的基礎概念開始，確保只有具備大學程度代數的學生也能理解材料。接著進入關鍵主題，如數據視覺化、假設檢定、迴歸建模，以及現代機器學習方法，如隨機森林和梯度提升。每一章都充實了實際範例和 R 的編碼練習，使其成為學生進入資料科學課程的寶貴資源。
本書設計為一學期的課程，為教師提供靈活性以調整內容以符合其課程需求。無論是探索廣義線性模型、混合效應模型，還是依賴數據分析，學生都將深入理解統計方法及其在各個領域的應用。在書籍結束時，讀者將能夠做出明智的決策、量化不確定性，並有效地傳達他們的發現。
這本書不僅僅是學習工具——它是渴望成為資料科學家的實用夥伴，幫助他們掌握統計實務和 R 程式設計。
作者簡介
Nalini Ravishanker is Professor in the Department of Statistics at the University of Connecticut (UConn), Storrs. She has a PhD in Statistics and Operations Research from the Stern School of Business, New York University, and a B.Sc. in Statistics from Presidency College, Madras, India. Her primary area of research is time series analysis with applications in several domains.
G. Asha is Senior Professor in the Department of Statistics at Cochin University of Science and Technology, Cochin, Kerala, India. She has a MPhil in Statistics from University of Kerala and Ph D in Statistics from Cochin University of Science and Technology, Cochin. Her primary area of research is life time data analysis.
Haim Bar Professor in the Department of Statistics at the University of Connecticut (UConn), Storrs. He has a PhD in Statistics from Cornell University, MSc in Computer Science from Yale University, and BSc in Mathematics from the Hebrew University. His areas of interest include high-dimensional models, and applications in genomics.
作者簡介(中文翻譯)
納莉尼·拉維尚克（Nalini Ravishanker）是康乃狄克大學（University of Connecticut, UConn）統計系的教授。她擁有紐約大學斯特恩商學院（Stern School of Business, New York University）的統計與運籌學博士學位，以及印度馬德拉斯的總統學院（Presidency College, Madras）統計學學士學位。她的主要研究領域是時間序列分析，並應用於多個領域。
G. Asha是印度喀拉拉邦科欽科技大學（Cochin University of Science and Technology, Cochin）的高級教授。她擁有喀拉拉大學（University of Kerala）的統計學碩士學位，以及科欽科技大學的統計學博士學位。她的主要研究領域是生命時間數據分析。
海姆·巴爾（Haim Bar）是康乃狄克大學（University of Connecticut, UConn）統計系的教授。他擁有康奈爾大學的統計學博士學位、耶魯大學的計算機科學碩士學位，以及希伯來大學的數學學士學位。他的研究興趣包括高維模型及其在基因組學中的應用。
