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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

Importance of data visualization

The goal of data visualization is to expose something new about the underlying patterns and relationships contained within the data. The visualization not only needs to be beautiful but also meaningful in order to help organizations make better decisions. Visualization is an easy way to jump into a complex dataset (small or big) to describe and explore the data efficiently. Many kinds of data visualization are available, such as bar charts, histograms, line charts, pie charts, heat maps, frequency Wordles (as is shown in the following image), and so on, for one variable, two variables, many variables in one, and even two or three dimensions:

Importance of data visualization

Data visualization is an important part of our data analysis process because it is a fast and easy way to perform exploratory data analysis through summarizing their main characteristics with a visual graph.

The goals of exploratory data analysis are as follows:

  • Detection of data errors
  • Checking of assumptions
  • Finding hidden patters (like tendency)
  • Preliminary selection of appropriate models
  • Determining relationships between the variables

We will go into more detail about data visualization and exploratory data analysis in Chapter 3, Getting to Grips with Visualization.

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