Python for Data Science

A Hands-On Introduction

Häftad, Engelska, 2022

799 kr

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Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You'll learn how to write Python code to obtain, transform, and analyse data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support.

Produktinformation

Utgivningsdatum2022-08-02
Mått177 x 234 x 21 mm
Vikt440 g
FormatHäftad
SpråkEngelska
Antal sidor240
FörlagNo Starch Press,US
ISBN9781718502208

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Yuli Vasiliev is a programmer, freelance writer, and consultant, who has been working with databases for more than two decades. He specializes in open-source development, and is experienced in building data structures and models, as well as designing and implementing database backends for various applications using Oracle technologies, MySQL, and natural language processing. Vasiliev is the author of Natural Language Processing with spaCy (No Starch Press.

"A great introduction to Python for data science in a compact package. I'm impressed with what Yuli Vasiliev included: the basics of Python, multiple angles at preparation, and a number of chapters on common types of data. The book is surprisingly visual for a field known for rows and columns—the location data was especially interesting to me. By the end of [Python for Data Science], I was ready to apply the principles to my own datasets."—Adam DuVander, @adamd, Founder of EveryDeveloper"Python to help you evaluate information is a great skill to have. Python for Data Science, written by Yuli Vasiliev, is an excellent resource for someone interested in getting started with Data Science. Although not intended for beginners in data analytics or python programming, this book explains how to implement some essential python concepts, gives excellent code snippets for practice, and guides you on using a few important libraries useful for processing and analyzing data with code." —Kelly Schuster-Paredes, Co-Host of Teaching Python Podcast, Educator