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Foundational Python for Data Science, 1st edition

  • Kennedy Behrman

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Overview

Learn all the foundational Python you’ll need to solve real data science problems


Data science and machine learning--two of the world’s hottest fields--are 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.

  • Master Google colab notebook Data Science programming
  • Manipulate data with popular Python libraries such as: pandas and numpy
  • Apply Python Data Science recipes to real world projects
  • Learn functional programming essentials unique to Data Science
  • Access case studies, chapter exercises, learning assessments, comprehensive Jupyter based Notebooks, and a complete final project

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

Published by Addison Wesley (December 15th 2021) - Copyright © 2022

ISBN-13: 9780137605675

Subject: IT Certification

Category: Business Analytics

Table of contents

Preface

Part I:  Learning Python in a Notebook Environment

1   Introduction to Notebooks

2   Fundamentals of Python

3   Sequences

4   Other Data Structures

5   Execution Control

6   Functions


Part II:  Data Science Libraries

7   Numpy

8   Scipy

9   Pandas

10   Visualization Libraries

11   Machine Learning Libraries

12   Natural Language Toolkit


Part III:  Intermediate Python

13   Functional Programming

14   Object-Oriented Programming

15   Other Topics

A   Answers to Selected Exercises 

Index

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