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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

Data modeling for results

The introduction to data modeling we provided in Chapter 5, Gathering and Loading Data in Python, gave us an understanding of relational databases and fundamental statistics that can be performed against structured data. In those examples, we learned about the relationships of data and how data can be modeled from the perspective of the data producer.Data producers are responsible for storing data in a structure to ensure the data's integrity is consistent. In the previous chapter, we also learned how anEntity Relationship Diagram (ERD)can be used to define the relationships between tables. In this chapter, we will apply these same concepts with the data consumer in mind. As a result, we will focus on creating new relationships with data, making it easier for analysis.This concept was an evolution in reporting and spawned a new industry commonly known as Business Intelligence (BI) and Analytics.

Introducing dimensions and measures

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