02383cam a22002898i 4500 on1129775792 OCoLC ta 210519s2020 flua b 001 0 eng 2019055620 9781138315068 (pbk.) 1138315060 (OCoLC)1129775792 (OCoLC)1129923395 QA76.9.D343 R64 2020 Rogel-Salazar, Jesus. Advanced data science and analytics with Python / Jesus Rogel-Salazar. Boca Raton, FL : CRC Press, 2020. xxxv, 383 p. : ill. Chapman & Hall/CRC data mining & knowledge discovery series Includes bibliographical references and index. "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"-- Provided by publisher. Data mining. Python (Computer program language) Databases. Chapman & Hall/CRC data mining and knowledge discovery series. lcc BK 0 0 0 0 PNLIB PNLIB GEN 2021-06-17 QA76.9.D343 R64 2020 PNLIB21062553 2021-06-17 2021-06-17 BK 2740 2740