Data Science Concepts and Techniques with Applications
暫譯: 數據科學概念與技術及其應用

Qamar, Usman, Raza, Muhammad Summair

Data Science Concepts and Techniques with Applications
  • 出版商: Springer
  • 出版日期: 2023-04-04
  • 售價: $3,080
  • 貴賓價: 9.5 折 $2,926
  • 語言: 英文
  • 頁數: 474
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3031174410
  • ISBN-13: 9783031174414
  • 相關分類: Data Science

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines.

The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book.

This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics.

"This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.

商品描述(中文翻譯)

這本教科書全面涵蓋了與資料科學相關的基本和進階主題。資料科學是一個總稱，包含資料分析、資料探勘、機器學習以及其他幾個相關學科。

本書的章節分為三個部分：第一部分（第1至第3章）是資料科學的一般介紹。從基本概念開始，本書將強調資料的類型、用途、重要性以及在資料分析中通常面臨的問題，接著介紹資料科學中廣泛使用的應用和技術。第二部分相比於第一版進行了更新和大幅擴展，專注於應用於資料科學的各種技術和工具。第4至第10章詳細介紹了資料預處理、分類、聚類、文本探勘、深度學習、頻繁模式探勘和回歸分析。最後，第三部分（第11和第12章）簡要介紹了Python和R這兩種主要的資料科學程式語言，並在全新的一章中展示了在WEKA（懷卡托知識分析環境）中進行實際資料科學的應用，這是一個用於執行不同機器學習和資料探勘任務的開源工具。附錄解釋了資料科學的基本數學概念，為本書畫上句點。

這本教科書適合高年級本科生、研究生以及從事資料科學研究的業界實務者。他們不僅能從重要主題的全面介紹中受益，還能從眾多應用範例和全面的進一步閱讀清單中獲益，這些清單指向提供更深入研究結果的其他出版物或提供相關主題更詳細描述的來源。

「這本書提供了系統性且經過深思熟慮的資料科學材料。」 — 來自加拿大阿爾伯塔大學 Witold Pedrycz 的前言。

作者簡介

Usman Qamar has over 15 years of experience in data engineering and decision sciences both in academia and industry. He is currently Tenured Professor of Data Sciences at the National University of Sciences and Technology (NUST) Pakistan and director of Knowledge and Data Science Research Centre, a Centre of Excellence at NUST, Pakistan. He has authored nearly 200 peer-reviewed publications and has also received multiple research awards.

Muhammad Summair Raza currently associated with the Virtual University of Pakistan as an assistant professor. He has published various papers in international-level journals and conferences with a focus on rough set theory. His research interests include feature selection, rough set theory, trend analysis, software design, software architecture, and non-functional requirements.


作者簡介(中文翻譯)

**Usman Qamar** 擁有超過 15 年的數據工程和決策科學經驗，涵蓋學術界和產業界。他目前是巴基斯坦國立科學與技術大學（NUST）的數據科學終身教授，以及該校卓越中心的知識與數據科學研究中心主任。他已發表近 200 篇經過同行評審的出版物，並獲得多項研究獎項。

**Muhammad Summair Raza** 目前在巴基斯坦虛擬大學擔任助理教授。他在國際級期刊和會議上發表了多篇論文，專注於粗集理論。他的研究興趣包括特徵選擇、粗集理論、趨勢分析、軟體設計、軟體架構和非功能性需求。

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