Advanced Data Science and Analytics with Python Advanced Data Science and Analytics with Python
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Advanced Data Science and Analytics with Python

    • ‏64٫99 US$
    • ‏64٫99 US$

وصف الناشر

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.

النوع
تمويل شركات وأفراد
تاريخ النشر
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٥ مايو
اللغة
EN
الإنجليزية
عدد الصفحات
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الناشر
CRC Press
البائع
Taylor & Francis Group
الحجم
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‫م.ب.‬
Data Science in R Data Science in R
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Future Data and Security Engineering Future Data and Security Engineering
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Business Intelligence Business Intelligence
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INTRODUCTION TO MACHINE LEARNING AND QUANTITATIVE FINANCE INTRODUCTION TO MACHINE LEARNING AND QUANTITATIVE FINANCE
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Real World Data Mining Applications Real World Data Mining Applications
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New Trends in Data Warehousing and Data Analysis New Trends in Data Warehousing and Data Analysis
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Data Science and Analytics with Python Data Science and Analytics with Python
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Essential MATLAB and Octave Essential MATLAB and Octave
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Social Networks with Rich Edge Semantics Social Networks with Rich Edge Semantics
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Exploratory Data Analysis Using R Exploratory Data Analysis Using R
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RapidMiner RapidMiner
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Data Mining for Design and Marketing Data Mining for Design and Marketing
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Geographic Data Mining and Knowledge Discovery Geographic Data Mining and Knowledge Discovery
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Biological Data Mining Biological Data Mining
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