國立高雄大學圖資館 |

登入

回首頁

Data science concepts and techniques with applications

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data science concepts and techniques with applicationsby Usman Qamar, Muhammad Summair Raza.
作者:	Qamar, Usman.
其他作者:	Raza, Muhammad Summair.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	xxiv, 474 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Data mining.
電子資源:	https://doi.org/10.1007/978-3-031-17442-1
ISBN:	9783031174421$q(electronic bk.)

Data science concepts and techniques with applications
Qamar, Usman.

Data science concepts and techniques with applications[electronic resource] /by Usman Qamar, Muhammad Summair Raza. - Second edition. - Cham :Springer International Publishing :2023. - xxiv, 474 p. :ill., digital ;24 cm.

1. Introduction -- 2. Applications of Data Science -- 3. Widely Used Techniques in Data Science Applications -- 4. Data Preprocessing -- 5. Classification -- 6. Clustering -- 7. Text Mining -- 8. Deep Learning -- 9. Frequent Pattern Mining -- 10. Regression Analysis -- 11. Data Science Programming Language -- 12. Practical Data Science with WEKA.

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.

ISBN: 9783031174421$q(electronic bk.)

Standard No.: 10.1007/978-3-031-17442-1doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Data science concepts and techniques with applications
LDR:03432nmm a2200361 a 4500 001 642526
003 DE-He213
005 20230402094216.0
006 m d
007 cr nn 008maaau
008 231123s2023 sz s 0 eng d
020 $a 9783031174421$q(electronic bk.)
020 $a 9783031174414$q(paper)
024 7 $a 10.1007/978-3-031-17442-1 $2 doi
035 $a 978-3-031-17442-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a UYQE $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b Q1 2023
100 1 $a Qamar, Usman. $3 787323
245 1 0 $a Data science concepts and techniques with applications $h [electronic resource] / $c by Usman Qamar, Muhammad Summair Raza.
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xxiv, 474 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1. Introduction -- 2. Applications of Data Science -- 3. Widely Used Techniques in Data Science Applications -- 4. Data Preprocessing -- 5. Classification -- 6. Clustering -- 7. Text Mining -- 8. Deep Learning -- 9. Frequent Pattern Mining -- 10. Regression Analysis -- 11. Data Science Programming Language -- 12. Practical Data Science with WEKA.
520 $a 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.
650 0 $a Data mining. $3 184440
650 1 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Data Analysis and Big Data. $3 913147
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Raza, Muhammad Summair. $3 787322
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-17442-1
950 $a Computer Science (SpringerNature-11645)