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Practical Data Analysis

You're reading from   Practical Data Analysis Pandas, MongoDB, Apache Spark, and more

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Product type Paperback
Published in Sep 2016
Publisher
ISBN-13 9781785289712
Length 338 pages
Edition 2nd Edition
Languages
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Authors (2):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
Dr. Sampath Kumar Dr. Sampath Kumar
Author Profile Icon Dr. Sampath Kumar
Dr. Sampath Kumar
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Toc

Table of Contents (16) Chapters Close

Preface 1. Getting Started 2. Preprocessing Data FREE CHAPTER 3. Getting to Grips with Visualization 4. Text Classification 5. Similarity-Based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Diseases with Cellular Automata 10. Working with Social Graphs 11. Working with Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with Jupyter and Wakari 15. Understanding Data Processing using Apache Spark

Chapter 3.  Getting to Grips with Visualization

Sometimes, we don't know how valuable data is until we look at it. In this chapter, we will look into a JavaScript-based web visualization framework called D3 (Data-Driven Documents) to create visualizations that make complex information easier to understand. We will cover the following topics:

  • What is visualization?
  • The visualization lifecycle
  • Visualizing different types of data
  • Data from social networks
  • An overview of visualization analytics

Exploratory Data Analysis (EDA), as mentioned in Chapter 2, Preprocessing Data, is a critical part of the data analysis process because it helps us to detect mistakes, determinate relationships, and tendencies, identify outliers, trends, and patterns, or check assumptions. In this chapter, we will present some examples of visualization methods for EDA with discrete and continuous data.

The four types of EDA are univariate nongraphical, multivariate nongraphical, univariate graphical, and multivariate...

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