I found some data-sets on Occupational Employment Statistics on Bureau of Labor Statistics site and I played with it to see if I can find something interesting:

Few things about the data & visualization that I am going to share

• US only
• I downloaded the national level data But there’s also state level data available if you’re interested to drill down.
• The reports that you see where created after I got a chance to “clean” the data-set a bit and created a data model that suited basic reporting on top of it.
• For this blog post, I am going to play w/ May 2010 & 2011 data
• With the help of original data-set, you can drill down to get statistics about a particular Job Category if you want. For this blog-post, I am going to share visualizations that correspond to Job categories.
• click on images to see the higher resolution image.

With that, Here are some visualizations:

1) Job Category VS mean hourly salary:

2) Job Category VS number of employees:

3) Scatter Plot:

X Axis: Number of employees

Y – Axis: Wage (Mean Hourly Salary May 2011)

Size of Bubble: Wage (Mean Hourly Salary May 2011)

*Note: This may not be the best approach to create the Scatter Plot as I have used the same value (Mean Hourly Salary May 2011) twice – But since I was just playing w/ it, I went with what I had in the model.

Here’s the visualization:

Some of the things I observed:

1) I belong to an Industry (Computer and Mathematical occupations) which has relatively higher mean hourly wage.

2) There are few people working in “farming, fishing & forestry occupations” that do not get paid much.

3) There are lots of people working in “office administrative support occupations” that do not get paid much.

4) Management Occupations, Legal Occupations and computer & mathematical occupations have relatively higher mean hourly wages.

Conclusion:

In this post, I played w/ Occupational Employment statistics data-sets and shared some visualizations.