Lots of suggestions... but a caution,
Although tools will run on data, there are underlying assumptions that the tools don't check.
For instance, correlation and regression...
- you shouldn't use it unless you know the underlying requirements before you apply the tool to the data.
What if you find out what the assumption is, then perform an analysis of the distribution of the data (descriptive stats and tests of the distribution).
- What if the data aren't applicable to that 'test'?
- Do you abandon all hope and move on?
- or do you move on to another test which doesn't have the same assumption?
- Do you transform your data until the distribution conforms (maybe it is the 5/4th root of income...)
This tome is just a cautionary tale about being swayed by the beauty and speed of a 'tool' or 'method of analysis' without accepting the fact that the results may be completely spurious because you didn't do your 'homework'
I have seen too many term projects that might even have been captured in books like this....
Spurious Correlations
Sadly some of the examples ring too true.... proceed with caution in your analysis.... the 'pile' is deep enough