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020			_a9781138315068
040			_aIIITD
082	0	0	_a006.3 _bROG-A
100	1		_aRogel-Salazar, Jesus
245	1	0	_aAdvanced data science and analytics with python _cby Jesus Rogel-Salazar
260			_aOxon : _bCRC Press, _c©2020.
300			_axxxv, 383 p. : _bill. ; _c25 cm.
490			_aChapman & Hall/CRC data mining & knowledge discovery series
504			_aIncludes bibliographical references and index.
505			_t1. Time Series _t2. Speaking Naturally: Text and Natural Language Processing _t 3. Getting Social: Graph Theory and Social Network Analysis _t4. Thinking Deeply: Neural Networks and Deep Learning _t5. Here Is One I Made Earlier: Machine Learning Deployment
520			_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 from the topics discuss 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. 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 in the book. The book discusses the need to develop data products and tackles the subject of bringing models to their intended audiences. In this case literally to the users's fingertips in the form of an iPhone app"--
650		0	_aData mining.
650		0	_aPython
650		0	_aDatabases.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK
999			_c189566 _d189566