Actions for Big Data Visualization [electronic resource].

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Big Data Visualization [electronic resource].

Author
Miller, James D.
Published
Birmingham : Packt Publishing, 2017.
Physical Description
1 online resource (299 pages)
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Contents
Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Introduction to Big Data Visualization; An explanation of data visualization; Conventional data visualization concepts; Training options; Challenges of big data visualization; Big data; Using Excel to gauge your data; Pushing big data higher; The 3Vs; Volume; Velocity; Variety; Categorization; Such are the 3Vs; Data quality; Dealing with outliers; Meaningful displays; Adding a fourth V; Visualization philosophies; More on variety; Velocity; Volume, All is not lostApproaches to big data visualization; Access, speed, and storage; Entering Hadoop; Context; Quality; Displaying results; Not a new concept; Instant gratifications; Data-driven documents; Dashboards; Outliers; Investigation and adjudication; Operational intelligence; Summary; Chapter 2: Access, Speed, and Storage with Hadoop; About Hadoop; What else but Hadoop?; IBM too!; Log files and Excel; An R scripting example; Points to consider; Hadoop and big data; Entering Hadoop; AWS for Hadoop projects; Example 1; Defining the environment; Getting started; Uploading the data, Manipulating the dataA specific example; Conclusion; Example 2; [Sorting]; Sorting; Parsing the IP; Summary; Chapter 3: Understanding Your Data Using R; [Definitions and explanations]; Definitions and explanations; Comparisons; Contrasts; Tendencies; Dispersion; Adding context; About R; R and big data; Example 1; Digging in with R; Example 2; Definitions and explanations; No looping; Comparisons; Contrasts; Tendencies; Dispersion; Summary; Chapter 4: Addressing Big Data Quality; Data quality categorized; DataManager; DataManager and big data; Some examples; Some reformatting; A little setup, Selecting nodesConnecting the nodes; The work node; Adding the script code; Executing the scene; Other data quality exercises; What else is missing?; Status and relevance; Naming your nodes; More examples; Consistency; Reliability; Appropriateness; Accessibility; Other Output nodes; Summary; Chapter 5: Displaying Results Using D3; About D3; D3 and big data; Some basic examples; Getting started with D3; A little down time; Visual transitions; Multiple donuts; More examples; Another twist on bar chart visualizations; One more example; Adopting the sample; Summary, and Chapter 6: Dashboards for Big Data -- TableauAbout Tableau; Tableau and big data; Example 1 -- Sales transactions; Adding more context; Wrangling the data; Moving on; A Tableau dashboard; Saving the workbook; Presenting our work; More tools; Example 2; What's the goal? -- purpose and audience; Sales and spend; Sales v Spend and Spend as % of Sales Trend; Tables and indicators; All together now; Summary; Chapter 7: Dealing with Outliers Using Python; About Python; Python and big data; Outliers; Options for outliers; Delete; Transform; Outliers identified; Some basic examples
Subject(s)
Genre(s)
ISBN
9781785284168
1785284169
Note
Description based upon print version of record.
Testing slot machines for profitability
View MARC record | catkey: 20062058