Difference between revisions of "Glossary of Data Analysis and Visualization Terms"

From Explore Analytics: The Wiki
Jump to navigation Jump to search
Line 17: Line 17:
  
 
a geographical chart is a [[#Data Visualization|visualization]] that specializes in showing data by location, address, or geographical coordinates (longitude and latitude). It allows you to detect geographical patterns in your data and explain location the drivers behind the data. Data is typically presented using a geographical map.
 
a geographical chart is a [[#Data Visualization|visualization]] that specializes in showing data by location, address, or geographical coordinates (longitude and latitude). It allows you to detect geographical patterns in your data and explain location the drivers behind the data. Data is typically presented using a geographical map.
 +
 +
=====Pivot=====
 +
 +
A Pivot is a tabular data presentation in which data is summarized by one or more categories. The labels for these categories are arranged across the top or down the side, and the table is populated with aggregate numerical calculations such as sums, averages, or counts that correspond to these categories. A pivot table makes it easy to see a high-level aggregate view and break it down by various categories to understand the drivers behind the data and make comparisons.
  
 
=====Timeline Chart=====
 
=====Timeline Chart=====

Revision as of 04:15, 13 June 2012

Category Chart

a category chart is a visualization that specializes in breaking down data by category. It allows you to easily compare data and focus on the categories of interest that explain the drivers behind the data. Data is typically presented as bars or pie slices.

Data Visualization

Visual representation of data that's designed for:

  • easy data comparisons
  • to reveal trends and changes over time
  • to discover correlation between different variables in the data
  • to discover patterns in the data

Good data visualization allows you to better understand the drivers behind the data and to make predictions based on that understanding. Common Data Visualizations include Timeline Chart, Category Chart, XY Chart, and Geographical Chart.

Geographical Chart

a geographical chart is a visualization that specializes in showing data by location, address, or geographical coordinates (longitude and latitude). It allows you to detect geographical patterns in your data and explain location the drivers behind the data. Data is typically presented using a geographical map.

Pivot

A Pivot is a tabular data presentation in which data is summarized by one or more categories. The labels for these categories are arranged across the top or down the side, and the table is populated with aggregate numerical calculations such as sums, averages, or counts that correspond to these categories. A pivot table makes it easy to see a high-level aggregate view and break it down by various categories to understand the drivers behind the data and make comparisons.

Timeline Chart

A timeline chart is a visualization that specializes in temporal data and is good for spotting trends. It shows data over time by putting the date/time in the horizontal axis and other variables on the vertical axis. A timeline chart offers various data presentations including lines and bars that can be shown on the same scale or different sales or in any combination to highlight changes and allows easy comparison. The main characteristic of a timeline chart is that time dimension is linear on the horizontal axis going from left to right and allowing scroll and zooming to focus on a particular time period.

XY Chart

An XY chart is a visualization that specializes in studying the relationship between numeric variables. Data is shown as a graph where points are drawn based on two variables in the data (two fields or calculations). These two variables are mapped to the X and Y axes respectively. A Bubble Chart is a specialized kind of XY chart in which a third variable is presented by varying the area of the points (bubbles) based on the value of the third variable. Category data can also be presented by varying the color or shape of the points (markers).