Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Big Data Visualization
  • Toc
  • feedback
Big Data Visualization

Big Data Visualization

By : James D. Miller
2 (1)
close
Big Data Visualization

Big Data Visualization

2 (1)
By: James D. Miller

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)
close
close
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Introduction to Big Data Visualization
1. Introduction to Big Data Visualization
An explanation of data visualization
Challenges of big data visualization
Approaches to big data visualization
Summary
2
2. Access, Speed, and Storage with Hadoop
2. Access, Speed, and Storage with Hadoop
About Hadoop
Log files and Excel
Hadoop and big data
Example 1
Example 2
Summary
3
3. Understanding Your Data Using R
3. Understanding Your Data Using R
Definitions and explanations
Adding context
About R
Example 1
Digging in with R
Example 2
Summary
4
4. Addressing Big Data Quality
4. Addressing Big Data Quality
Data quality categorized
DataManager
DataManager and big data
Some examples
More examples
Summary
5
5. Displaying Results Using D3
5. Displaying Results Using D3
About D3
D3 and big data
Some basic examples
More examples
Summary
6
6. Dashboards for Big Data - Tableau
6. Dashboards for Big Data - Tableau
About Tableau
Tableau and big data
Example 1 - Sales transactions
Example 2
Summary
7
7. Dealing with Outliers Using Python
chevron up
7. Dealing with Outliers Using Python
About Python
Python and big data
Outliers
Some basic examples
More examples
Summary
8
8. Big Data Operational Intelligence with Splunk
8. Big Data Operational Intelligence with Splunk
About Splunk
Splunk visualization -  real-time log analysis
Splunk visualization - deeper into the logs
Summary

Some basic examples

In the world of gaming, slot machines (a gambling machine operated by inserting coins into a slot and pulling a handle that determines the payoff) are quite popular. Most slot machines today are electronic and therefore are programmed to continuously track its activity. This provides an opportunity for our first example.

Testing slot machines for profitability

The owners of the casino want to use this data (as well as various supplementary data) to drive adjustments to their profitability strategy. In other words, what makes for a profitable slot machine? Is it the machine's theme or its type? Are newer machines more profitable (than older ones)? What about the physical location of the machine? Are lower denomination machines really profitable?

The following information is being collected:

  • The Location of the slot machine

  • Denomination in nickel, quarter, dollar, and so on

  • Month of the year

  • The Weekday

  • Machine Type such as 4 Reel, 5 Reel, Progressive, and so on

  • Theme of the...

End of Section 3
Next Section
bookmark search playlist download
font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete