Python Data Science Essentials - Second Edition

Alberto Boschetti, Luca Massaron

• Publisher: Packt Publishing
• Year: 2016
• ISBN:
  9781786462138
• Edition: 2
• Topic: Informatica
• Subtopic: Elaborazione di dati

Become an efficient data science practitioner by understanding Python's key concepts

About This Book

• Quickly get familiar with data science using Python 3.5
• Save time (and effort) with all the essential tools explained
• Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience

Who This Book Is For

If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.

What You Will Learn

• Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux
• Get data ready for your data science project
• Manipulate, fix, and explore data in order to solve data science problems
• Set up an experimental pipeline to test your data science hypotheses
• Choose the most effective and scalable learning algorithm for your data science tasks
• Optimize your machine learning models to get the best performance
• Explore and cluster graphs, taking advantage of interconnections and links in your data

In Detail

Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow.

Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.

Style and approach

The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

Python Data Science Essentials - Second Edition

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Python Data Science Essentials - Second Edition

Copyright © 2016 Packt Publishing

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Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: April 2015

Second edition: October 2016

Production reference: 1211016

Published by Packt Publishing Ltd.

Livery Place

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Birmingham

B3 2PB, UK.

ISBN 978-1-78646-213-8

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Credits

Authors Alberto Boschetti Luca Massaron	Copy Editor Vikrant Phadke
Reviewer Zacharias Voulgaris	Project Coordinator Nidhi Joshi
Commissioning Editor Veena Pagare	Proofreader Safis Editing
Acquisition Editor Namrata Patil	Indexer Aishwarya Gangawane
Content Development Editor Mayur Pawanikar	Graphics Disha Haria
Technical Editor Vivek Arora	Production Coordinator Arvindkumar Gupta

About the Authors

Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.

I would like to thank my family, my friends, and my colleagues. Also, a big thanks to the open source community.

Luca Massaron is a data scientist and marketing research director specializing in multivariate statistical analysis, machine learning, and customer insight, with over a decade of experience of solving real-world problems and generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms. From being a pioneer of web audience analysis in Italy to achieving the rank of a top ten Kaggler, he has always been very passionate about every aspect of data and its analysis, and also about demonstrating the potential of data-driven knowledge discovery to both experts and non-experts. Favoring simplicity over unnecessary sophistication, Luca believes that a lot can be achieved in data science just by doing the essentials.

To Yukiko and Amelia, for their loving patience. "Roads go ever ever on, under cloud and under star, yet feet that wandering have gone turn at last to home afar".

About the Reviewer

Zacharias Voulgaris is a data scientist and technical author specializing in data science books. He has an engineering and management background, with post-graduate studies in information systems and machine learning. Zacharias has worked as a research fellow at Georgia Tech, investigating and applying machine learning technologies to real-world problems, as an SEO manager in an e-marketing company in Europe, as a program manager in Microsoft, and as a data scientist at US Bank and at G2 Web Services.

Dr. Voulgaris has also authored technical books, the most notable of which is Data Scientist - the definitive guide to becoming a data scientist (Technics Publications), and his newest book, Julia for Data Science (Technics Publications), was released during the summer of 2016. He has also written a number of data-science-related articles on blogs and participates in various data science/machine learning meetup groups. Finally, he has provided technical editorial aid in the book Python Data Science Essentials (Packt), by the same authors as this book.

I would very much like to express my gratitude to the authors of the book for giving me the opportunity to contribute to this project. Also, I'd like to thank Bastiaan Sjardin for introducing me to them and to the world of technical editing. It's been a privilege working with all of you.

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Preface

	"A journey of a thousand miles begins with a single step."
	--Laozi (604 BC - 531 BC)

Data science is a relatively new knowledge domain that requires the successful integration of linear algebra, statistical modeling, visualization, computational linguistics, graph analysis, machine learning, business intelligence, and data storage and retrieval.

The Python programming language, having conquered the scientific community during the last decade, is now an indispensable tool for the data science practitioner and a must-have tool for every aspiring data scientist. Python will offer you a fast, reliable, cross-platform, mature environment for data analysis, machine learning, and algorithmic problem solving. Whatever stopped you before from mastering Python for data science applications will be easily overcome by our easy, step-by-step, and example-oriented approach that will help you apply the most straightforward and effective Python tools to both demonstrative and real-world datasets. As the second edition of Python Data Science Essentials, this book offers updated and expanded content. Based on the recent Jupyter Notebooks (incorporating interchangeable kernels, a truly polyglot data science system), this book incorporates all the main recent improvements in Numpy, Pandas, and Scikit-learn. Additionally, it offers new content in the form of deep learning (by presenting Keras–based on both Theano and Tensorflow), beautiful visualizations (seaborn and ggplot), ...