Advanced Data Science and Analytics with Python: 1st Edition (Paperback) book cover

Advanced Data Science and Analytics with Python

1st Edition

By Jesus Rogel-Salazar

Chapman and Hall/CRC

432 pages | 25 B/W Illus.

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Paperback: 9781138315068
pub: 2020-03-27
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Description

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.

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 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.

About the Author

Dr Jesús Rogel-Salazar is a lead data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones, Barclays and TympaHealth Technologies. 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.

Table of Contents

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About the Author

Dr Jesús Rogel-Salazar is a lead data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones, Barclays and Tympa Health Technologies. 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. He obtained his doctorate in Physics at Imperial College London for work on quantum atom optics and ultra-cold matter.

He has held a position as senior lecturer in mathematics as well as a consultant and data scientist for a number of years in a variety of industries including science, finance, marketing, people analytics and health, among others. He is the author of Data Science and Analytics with Python and Essential Matlab and Octave, both also published with CRC Press. His interests include mathematical modelling, data science and optimisation in a wide range of applications including optics, quantum mechanics, data journalism, finance and health.

About the Series

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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Subject Categories

BISAC Subject Codes/Headings:
BUS061000
BUSINESS & ECONOMICS / Statistics
COM012040
COMPUTERS / Programming / Games
COM021030
COMPUTERS / Database Management / Data Mining
COM037000
COMPUTERS / Machine Theory