Mastering data.table in R: Programming Techniques for Data Science
暫譯: 精通 R 中的 data.table：資料科學程式設計技術
Shilane, David
相關主題
商品描述
Mastering data.table in R: Programming Techniques for Data Science provides a comprehensive discussion of R programming with the data.table package. Widely regarded for its breadth of applications and computational efficiency, data.table provides an excellent set of tools for data science investigations. This textbook introduces the core programming syntax of data.table, discusses advanced data.table techniques, and reinforces learning with a wide range of data science applications. Along the way, readers will learn many lessons related to data exploration, processing, analysis, computer programming, and machine learning.
Key Features
- Introduction to the core methods of data.table programming, such as extracting information, counting records, summarizing data, sorting tables, and grouped computations
- Discussion of advanced methods that facilitate scalable processing and specialized computations, such as simultaneous calculations, indexed record selections, reshaping data, and rolling joins
- Examination of links between data.table and programming tools such as grep and AWK for advanced file reading applications
- Presentation of a significant range of learning examples, including coding samples and their outputs, which progress from simple analyses to more complex operations
- Development of significant case studies highlighting the applications of data.table in all stages of data science investigations
- Integration of data.table within a data science practice that emphasizes research aims, rigorous methods of investigation, and computer programming that facilitates analysis
This book is suitable for university students in data science and more seasoned practitioners alike. Students with limited exposure to R and data science can gain experience with computer programming and data analysis. Practitioners can utilize this text to master advanced techniques or to quickly gain new skills when learning R as a new language. The examples and case studies touch upon a wide range of applications, helping to prepare learners to face new challenges in their data science practice.
商品描述(中文翻譯)
Mastering data.table in R：資料科學的程式設計技術
《Mastering data.table in R：資料科學的程式設計技術》完整探討如何使用 data.table 套件進行 R 程式設計。data.table 廣泛應用於各種領域，並以運算效率著稱，為資料科學研究提供了一套優異的工具。本書介紹 data.table 的核心程式語法，討論進階 data.table 技術，並透過廣泛的資料科學應用案例強化學習成效。在學習過程中，讀者將掌握許多與資料探索、資料處理、資料分析、電腦程式設計及機器學習相關的重要知識。
主要特色
• 介紹 data.table 程式設計的核心方法，例如擷取資訊、計算記錄數量、彙總資料、排序資料表，以及分組計算
• 討論有助於可擴充處理與特殊計算的進階方法，例如同時執行多項計算、依索引選取記錄、重新塑造資料，以及 rolling joins
• 探討 data.table 與 grep、AWK 等程式設計工具之間的關聯，說明其在進階檔案讀取應用中的使用方式
• 提供大量學習範例，包括程式碼範例及其輸出結果，內容由簡單分析逐步延伸至更複雜的操作
• 建立重要的案例研究，凸顯 data.table 在資料科學研究各階段中的應用
• 將 data.table 整合至資料科學實務中，強調研究目標、嚴謹的研究方法，以及能夠促進分析工作的電腦程式設計
本書適合資料科學相關科系的大學生，也適合具備較豐富實務經驗的從業人員。對 R 與資料科學接觸有限的學生而言，本書能幫助他們累積電腦程式設計與資料分析的經驗。從業人員則可以利用本書掌握進階技術，或在學習 R 這門新語言時，迅速培養新的技能。書中的範例與案例研究涵蓋廣泛的應用領域，有助於學習者為資料科學實務中即將面對的新挑戰做好準備。
作者簡介
David Shilane is a Lecturer of Applied Analytics at Columbia University. He teaches courses in applied machine learning, research methods, and data science consulting. David conducts research in healthcare outcomes and utilization, social determinants of health, applied machine learning, data science education, and statistical software. He has developed a range of R software packages that utilize or extend data.table, and he has taught a range of data.table workshops. As a practitioner, David has served as a statistical consultant in academic research, healthcare organizations, technological start-ups, and product research firms. Often serving as the first data scientist for organizations and advising chief level officers, he has played a role in building data systems and developing data science initiatives from the ground up. David received degrees from Stanford University and the University of California Berkeley.
作者簡介(中文翻譯)
David Shilane 是 Columbia University 的應用分析講師。他教授應用機器學習、研究方法與資料科學顧問等課程。David 的研究領域包括醫療照護結果與利用、健康的社會決定因素、應用機器學習、資料科學教育及統計軟體。
他開發了一系列運用或擴充 data.table 的 R 軟體套件，也曾教授多種 data.table 工作坊。作為實務工作者，David 曾擔任學術研究、醫療照護組織、科技新創公司及產品研究公司的統計顧問。他經常擔任組織中的第一位資料科學家，並為高階主管提供諮詢，協助從零開始建置資料系統及推動資料科學計畫。
David 擁有 Stanford University 和 University of California Berkeley 授予的學位。
