Python for Data Analysis - Dylan Penny
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Résumé :
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How many times have you thought about learning how to code but got discouraged as you didn't have any technical experience, the time to learn, or, simply, you didn't think you were intelligent enough?
Then this book is perfect for your costumers will never stop to use this awesome guide!
You don't need a costly computer science degree, a genius mind, and a 1000-page textbook to learn Python's basics for Data Analysis.
This book, PYTHON FOR DATA ANALYSIS: A PRACTICAL GUIDE TO MANIPULATING, PROCESSING, CLEANING, AND CRUNCHING DATA SETS IN PYTHON. HOW TO EFFECTIVELY SOLVE A WIDE RANGE OF DATA ANALYSIS PROBLEMS, is a concise, step-by-step guide to Python for Data Analysis.
Many books about Pythons are theoretical and have little to no practical examples. This manual offers a plethora of simple illustrations and examples to underline core concepts and enhance your understanding. Loads of practice exercises are provided to make you learn fast, remember, and build a thorough understanding of the key concepts.
Are you ready to find out more?
Here's a short preview of what you will learn inside this book:
Why Python for data analysis?
Data analysis bases
Python libraries and installation
Python language basics, ipython and jupyter notebooks
Built-in data structures, functions, and files
Introduction to modeling libraries in Python
...And so much more!
This book will offer you a comprehensive explanation of Python for data analysis while not overpowering you with loads of information. Compared to other books, the examples' outputs are given, so you don't need to wait.
Buy it NOW and let your customers get addicted to this amazing book!
Biographie:
Dylan Penny is an expert in computers and programming languages. He graduated from Stanford University in 1999 with a degree in web programming and now works for a major California company. He writes books mostly in this area because he wants to bring his knowledge to as many people as possible. He lives with his family in Sunnyvale.
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