Luca Massaron - Python Data Science Essentials - Second Edition

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".

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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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 Kerasbased on both Theano and Tensorflow), beautiful visualizations (seaborn and ggplot), and web deployment (using bottle). This book starts by showing you how to set up your essential data science toolbox in Pythons latest version (3.5), using a single-source approach (implying that the book's code will be easily reusable on Python 2.7 as well). Then, it will guide you across all the data munging and preprocessing phases in a manner that explains all the core data science activities related to loading data, transforming, and fixing it for analysis, and exploring/processing it. Finally, the book will complete its overview by presenting you with the 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.