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Foundational Python for Data Science (Addison-Wesley Data & Analytics Series)

Foundational Python for Data Science (Addison-Wesley Data & Analytics Series)

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Foundational Python for Data Science (Addison-Wesley Data & Analytics Series)

Foundational Python for Data Science (Addison-Wesley Data & Analytics Series)

Category

Python

Data Science

Data Mining

Author

Kennedy Behrman

Publication

Pearson

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Data science and machine learningtwo of the worlds hottest fieldsare attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world\'s #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning.

Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once you\'ve learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving.

Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and moreall created with Colab (Jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software.

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ISBN-10

0136624359

ISBN-13

978-0136624356

Publisher

Pearson

Price

34.11

File Type

PDF

Page No.

256

Kennedy Behrman

Kennedy Behrman

Kennedy Behrman is a veteran consultant and author, specializing in architecting and implementing cl ...

About the Author

Kennedy Behrman is a veteran software and data engineer. He first used Python writing asset management systems in the Visual Effects industry. He then moved into the startup world, using Python at startups using machine learning to characterize videos and predict the social media power of athletes.

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