Use of visualizations for analyzing multi-dimensional data

Visualizations are a very powerful ways of representing complex data. The visualizations that you should choose depends on the kind of data you want to represent and kind of analysis that you want to facilitate on the top of this data. There are a few heavily used visualizations for data analysis, and below is a brief list of the visualizations that I admire the most for analytical data representation.

1) Box plot / Scatter plot charts - These charts are mostly used for outliers analysis.

2) Candlestick charts - For analyzing extremely volatile data like movement of a particular stock during the day, with associated values like high-low-open-close.

3) Line charts / Range charts: For displaying multiple trends on the same graph for trend analysis and correlation analysis.

4) Tree map / Performance map: For portfolio analysis and measuring weighted values of each item within a portfolio.

5) Decomposition Trees: For problem decomposition using drill-down and drill-through techniques in the same visualization.