When dealing with data with dozens or hundreds of features one important tool is to look at the correlations between different features as a heat map. Although it is easy to generate a correlation heat map not all such visualizations are created equal. Here are some rules of thumb to keep in mind,

Limit the range of the color map to the middle 99.x% of the values
Use symmetric magnitude bounds
Use a divergent color map
Make 0 correlation correspond to a dull dark color (dark grey), and high magnitude correlations high luminance
Different orderings of the features can have a huge impact, pick wisely.

Using these guidelines together almost always improves the overall quality of the visualization of a correlation or covariance matrix.

We will apply these guidelines one by one to an example data set (see below) talking about the motivation for each guideline as we apply it.

Asymptotic Labs (Posts about covariance)

Tips for Visualizing Correlation Matrices