Data Science and Analytics with Python

by Jesus Rogel-Salazar

Overview

•Presents the main concepts in data analytics, using tools developed in Python
•Designed for use by beginners and seasoned programmers alike
•Provides the tools, alongside solved examples with steps that the reader can easily reproduce and adapt to their needs
•Focuses one practical use of the tools rather than on lengthy theoretical explanations
•Includes a Python Primer

Product Details

ISBN-13:	9781351647717
Publisher:	CRC Press
Publication date:	02/05/2018
Series:	Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Sold by:	Barnes & Noble
Format:	eBook
Pages:	400
File size:	4 MB

About the Author

Dr. Jesús Rogel-Salazar is a Lead Data Scientist at IBM Data Science Studio and visiting researcher at the Department of Physics at Imperial College London, UK. He is also 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 in the financial industry since 2006. He is the author of the book “Essential Matlab and Octave”, 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 and finance. Dr. Jesús Rogel-Salazar is a Lead Data Scientist at IBM Data Science Studio and visiting researcher at the Department of Physics at Imperial College London, UK. He is also 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 in the financial industry since 2006. He is the author of the book “Essential Matlab and Octave”, 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 and finance.

Table of Contents

The Trials and Tribulations of a Data Scientist. Python: For Something Completely Different. The Machine that Goes "Ping": Machine Learning and Pattern Recognition. The Relationship Conundrum: Regression. Jackalopes and Hares: Clustering. Decisions, Decisions: Hierarchical Clustering, Decision Trees and Ensable Techniques. Less is More: Dimensionality Reduction. Kernel Tricks under the Sleeve: Support Vector Machines. Pipelines in Scikit-learn.

Editorial Reviews

For advanced students and professionals in data science and data analytics, this work provides an excellent introduction to the main concepts of data analytics using tools developed in Python. The popularity and open source nature of Python makes it an excellent choice for developing analytic models using add-on tools such as SciKit-learn, Numpy, and others. The book does not assume a working knowledge of Python and provides a through introductory chapter. The other chapters can be read independently of one another, making the text a valuable resource for readers interested in a specific area of data analytics. The book's design is user-friendly as well; wide margins allow for taking notes while reading. This space also contains summary notes of the material, making it easy to scan for specific concepts. The material covered includes machine learning and pattern recognition, various regression techniques, classification algorithms, decision tree and hierarchical clustering, and dimensionality reduction. Though this text is not recommended for those just getting started with computer programming, it would make an excellent tool for readers who wish to add Python to their programming language repertoire while developing models or analyzing data.
—D. B. Mason, Albright College, CHOICE, June 2018

For advanced students and professionals in data science and data analytics, this work provides an excellent introduction to the main concepts of data analytics using tools developed in Python. The popularity and open source nature of Python makes it an excellent choice for developing analytic models using add-on tools such as SciKit-learn, Numpy, and others. The book does not assume a working knowledge of Python and provides a through introductory chapter. The other chapters can be read independently of one another, making the text a valuable resource for readers interested in a specific area of data analytics. The book's design is user-friendly as well; wide margins allow for taking notes while reading. This space also contains summary notes of the material, making it easy to scan for specific concepts. The material covered includes machine learning and pattern recognition, various regression techniques, classification algorithms, decision tree and hierarchical clustering, and dimensionality reduction. Though this text is not recommended for those just getting started with computer programming, it would make an excellent tool for readers who wish to add Python to their programming language repertoire while developing models or analyzing data.
—D. B. Mason, Albright College, CHOICE, June 2018

From the Publisher