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008			230402s2023 sz | o |||| 0|eng d
020			_a9783031174421
024	7		_a10.1007/978-3-031-17442-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .D343 _b2023 EB
100	1		_aQamar, Usman _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689513
245	1	0	_aData Science Concepts and Techniques with Applications _cby Usman Qamar, Muhammad Summair Raza
250			_a2nd ed 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_a1. 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			_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.
988			_aSpringer_Computer_2023
650		7	_2embne _9162648 _aData mining
700	1		_9689514 _aRaza, Muhammad Summair _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-17442-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _eb _zSI