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  • Book Overview & Buying Big Data Visualization
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Big Data Visualization

Big Data Visualization

By : James D. Miller , Dalong Chen
2 (1)
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Big Data Visualization

Big Data Visualization

2 (1)
By: James D. Miller , Dalong Chen
Buy this Book

Overview of this book

Gain valuable insight into big data analytics with this book. Covering the tools you need to analyse data, together with IBM certified expert James Miller?s insight, this book is the key to data visualization success. ? Learn the tools & techniques to process big data for efficient data visualization ? Packed with insightful real-world use cases ? Addresses the difficulties faced by professionals in the field of big data analytics
Table of Contents (9 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
Lock Free Chapter
1
1. Introduction to Big Data Visualization
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1. Introduction to Big Data Visualization
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An explanation of data visualization
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Challenges of big data visualization
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Approaches to big data visualization
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Summary
2
2. Access, Speed, and Storage with Hadoop
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2. Access, Speed, and Storage with Hadoop
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About Hadoop
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Log files and Excel
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Hadoop and big data
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Example 1
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Example 2
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Summary
3
3. Understanding Your Data Using R
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3. Understanding Your Data Using R
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Definitions and explanations
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Adding context
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About R
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Example 1
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Digging in with R
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Example 2
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Summary
4
4. Addressing Big Data Quality
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4. Addressing Big Data Quality
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Data quality categorized
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DataManager
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DataManager and big data
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Some examples
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More examples
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Summary
5
5. Displaying Results Using D3
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5. Displaying Results Using D3
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About D3
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D3 and big data
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Some basic examples
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More examples
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Summary
6
6. Dashboards for Big Data - Tableau
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6. Dashboards for Big Data - Tableau
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About Tableau
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Tableau and big data
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Example 1 - Sales transactions
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Example 2
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Summary
7
7. Dealing with Outliers Using Python
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7. Dealing with Outliers Using Python
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About Python
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Python and big data
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Outliers
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Some basic examples
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More examples
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Summary
8
8. Big Data Operational Intelligence with Splunk
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8. Big Data Operational Intelligence with Splunk
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About Splunk
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Splunk visualization -  real-time log analysis
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Splunk visualization - deeper into the logs
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Summary

Some examples

To get started, let's assume that our data provider from Chapter 3, Understanding Your Data Using R, has supplied additional files to us containing patient survey information, but it has been communicated that the data quality of these files is suspect. In the following examples, we'll see what can be done to address specific concerns and improve the quality of the data.

Some reformatting

A profiling exercise from Chapter 3, Understanding Your Data Using R, was to time box the data to establish a time frame in which the hospital visits were recorded. Using R, we scanned the data looking at the field or column named recorddate and generated a list of four character years found in the file. By looking at the list we were able to establish that the data in the file ranged from 1999 through 2016.

This time, let's assume we've again scanned our data and observed that the recorddate field contains both four-character and two-character length years (as shown in the following screenshot...

CONTINUE READING
83
Tech Concepts
36
Programming languages
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Big Data Visualization
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