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Practical Data Analysis Using Jupyter Notebook

You're reading from   Practical Data Analysis Using Jupyter Notebook Learn how to speak the language of data by extracting useful and actionable insights using Python

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781838826031
Length 322 pages
Edition 1st Edition
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Author (1):
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Marc Wintjen Marc Wintjen
Author Profile Icon Marc Wintjen
Marc Wintjen
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Data Analysis Essentials
2. Fundamentals of Data Analysis FREE CHAPTER 3. Overview of Python and Installing Jupyter Notebook 4. Getting Started with NumPy 5. Creating Your First pandas DataFrame 6. Gathering and Loading Data in Python 7. Section 2: Solutions for Data Discovery
8. Visualizing and Working with Time Series Data 9. Exploring, Cleaning, Refining, and Blending Datasets 10. Understanding Joins, Relationships, and Aggregates 11. Plotting, Visualization, and Storytelling 12. Section 3: Working with Unstructured Big Data
13. Exploring Text Data and Unstructured Data 14. Practical Sentiment Analysis 15. Bringing It All Together 16. Works Cited
17. Other Books You May Enjoy
Section 1: Data Analysis Essentials

In this section, we will learn how to speak the language of data by extracting useful and actionable insights from data using Python and Jupyter Notebook. We'll begin with the fundamentals of data analysis and work with the right tools to help you analyze data effectively. After your workspace has been set up, we'll learn how to work with data using two popular open source libraries available in Python: NumPy and pandas. This will lay the foundation for you to understand data so that you can prepare for Section 2: Solutions for Data Discovery.

This section includes the following chapters:

  • Chapter 1, Fundamentals of Data Analysis
  • Chapter 2, Overview of Python and Installing Jupyter Notebook
  • Chapter 3, Getting Started with NumPy
  • Chapter 4, Creating Your First pandas DataFrame
  • Chapter 5, Gathering and Loading Data in Python...
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