Python Data Science Essentials

Python Data Science Essentials

by Alberto Boschetti, Luca Massaron

NOOK Book(eBook)

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The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results.

In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

Product Details

ISBN-13: 9781785287893
Publisher: Packt Publishing
Publication date: 04/30/2015
Sold by: Barnes & Noble
Format: NOOK Book
File size: 3 MB

About the Author

Alberto Boschetti is a data scientist, with strong 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 daily in natural language processing (NLP), machine learning, and probabilistic graph models. He is very passionate about his job and he always tries to be up to date on the latest development of data science technologies,by attending meetups, conferences, and other events.

Luca Massaron is a data scientist and a research director specialized in multivariate statistical analysis, machine learning, and customer insight with over a decade of experience in solving real-world problems and in 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 top ten Kaggler, he has always been passionate about everything regarding data and analysis and demonstrating the potentiality of data-driven knowledge discovery to both experts and non-experts. Favouring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essential.

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