Data Science Concepts and Techniques with Applications

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.

這本教科書全面涵蓋了與資料科學相關的基礎和高級主題。資料科學是一個總稱，包括資料分析、資料挖掘、機器學習和其他相關學科。

本書的章節分為三個部分：第一部分（第1至3章）是對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、使用、重要性以及在資料分析中通常面臨的問題，並介紹資料科學中廣泛應用的技術和技巧。

第二部分相對於第一版進行了更新和擴充，專門介紹了資料科學中應用的各種技術和工具。第4至10章詳細介紹了資料預處理、分類、聚類、文本挖掘、深度學習、頻繁模式挖掘和回歸分析。

最後，第三部分（第11和12章）簡要介紹了Python和R這兩種主要的資料科學程式語言，並在一個全新的章節中展示了WEKA（Waikato Environment for Knowledge Analysis）中的實際資料科學應用，WEKA是一個用於執行不同機器學習和資料挖掘任務的開源工具。附錄解釋了資料科學的基本數學概念，為本書提供了完整性。

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工業從業人員使用。他們不僅可以從重要主題的全面介紹中受益，還可以從許多應用示例和廣泛的進一步閱讀列表中獲益，這些列表指向提供更深入研究結果或提供更詳細描述相關主題的其他出版物。

「這本書提供了一個有系統、深思熟慮的資料科學材料。」——Witold Pedrycz（加拿大阿爾伯塔大學）的前言。

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.

Usman Qamar在學術界和工業界擁有超過15年的數據工程和決策科學經驗。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，也是NUST的知識與數據科學研究中心的主任，該中心是NUST的卓越中心。他已經發表了近200篇同行評審的論文，並且也獲得了多個研究獎項。

Muhammad Summair Raza目前是巴基斯坦虛擬大學的助理教授。他在國際級期刊和會議上發表了多篇論文，重點關注粗糙集理論。他的研究興趣包括特徵選擇、粗糙集理論、趨勢分析、軟件設計、軟件架構和非功能需求。