000			03099nam a2200313Ia 4500
000			03691nam a22003375i 4500
001			978-3-031-17442-1
003			DE-He213
005			20240319120826.0
007			cr nn 008mamaa
008			230402s2023 sz | s |||| 0|eng d
020			_a9783031174421 _9978-3-031-17442-1
082			_a6.312
100			_aQamar, Usman. _929825
245			_aData Science Concepts and Techniques with Applications _cby Usman Qamar, Muhammad Summair Raza. _h[electronic resource] /
250			_a2nd ed. 2023.
260			_aCham _bSpringer International Publishing _c2023
300			_aXXIV, 474 p. 70 illus. in color. _bonline resource.
520			_aThis 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			_aArtificial intelligence. _929826
650			_aArtificial Intelligence. _929827
650			_aData Analysis and Big Data. _929828
650			_aData Mining and Knowledge Discovery. _929829
650			_aData mining. _929830
650			_aQuantitative research. _929831
700			_aRaza, Muhammad Summair. _929832
856			_uhttps://doi.org/10.1007/978-3-031-17442-1
942			_cEBK _2ddc
999			_c15080 _d15080