LEADER 04258oam 2200565M 450 001 9911117062903321 005 20260720160706.0 010 $a1-5231-4416-5 010 $a0-429-82231-6 010 $a0-429-44664-0 010 $a0-429-82232-4 010 $a9780429446641 024 7 $a10.1201/9780429446641 035 $a(CKB)4100000011241741 035 $a(MiAaPQ)EBC6191891 035 $a(OCoLC)1154070455$z(OCoLC)1155202754$z(OCoLC)1155638026 035 $a(OCoLC-P)1154070455 035 $a(FlBoTFG)9780429446641 035 $a(PPN)25284503X 035 $a(EXLCZ)994100000011241741 100 $a20200508d2020 uy 0 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced data science and analytics with Python /$fJesu?s Rogel-Salazar 205 $a1st ed. 210 1$aBoca Raton :$cCRC Press,$d2020. 215 $a1 online resource (424 pages) $cillustrations 225 0 $aChapman & Hall/CRC data mining & knowledge discovery series 311 08$a0-429-44661-6 311 08$a9781138315068 320 $aIncludes bibliographical references and index. 327 $a1.No Time To Lose: Time Series Analysis2.Speaking Naturally: Text and Natural Language Processing3.Let Us Get Social: Graph Theory and Social Network Analysis4.Thinking Deeply: Neural Networks and Deep Learning5.Here Is One I Made Earlier: Machine Learning Deployment 330 $a"Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences - in this case, literally to the users' fingertips in the form of an iPhone app. About the Author Dr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK." -- Publisher's description. 606 $aPython (Computer program language) 606 $aDatabases 606 $aData mining 615 0$aPython (Computer program language) 615 0$aDatabases. 615 0$aData mining. 676 $a006.3/12 700 $aRogel-Salazar$b Jesus$02005262 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9911117062903321 996 $aAdvanced data science and analytics with Python$94791312 997 $aUNINA