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001 978-3-031-17442-1
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008 230402s2023 sz | s |||| 0|eng d
020 _a9783031174421
_9978-3-031-17442-1
050 4 _aQA76.9.D343
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_223
100 1 _aQamar, Usman.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aData Science Concepts and Techniques with Applications
_h[electronic resource] /
_cby Usman Qamar, Muhammad Summair Raza.
250 _a2nd ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXXIV, 474 p. 70 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
500 _aAcceso multiusuario
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.
541 _fUABC ;
_cPerpetuidad
650 0 _aData mining.
650 0 _aQuantitative research.
650 0 _aArtificial intelligence.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aData Analysis and Big Data.
650 2 4 _aArtificial Intelligence.
700 1 _aRaza, Muhammad Summair.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031174414
776 0 8 _iPrinted edition:
_z9783031174438
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-17442-1
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cLIBRO_ELEC
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