The long march to the boss’s office to get evaluated—it’s a moment we all dread. Performance reviews are awkward. They’re biased. They stick us in boxes and leave us waiting far too long for feedback. It’s no surprise that by the end of 2015, at least 30 of the Fortune 500 companies had ditched performance evaluations altogether. But let’s not throw the baby out with the bathwater.

The Story Of Alvin Irby And Barbershop Books

In one forum or another recently, a tester, developer or DevOps person (I wasn’t sure which of these he/she was) asked:

Do you perform any exploratory testing on APIs? How do you do it?

Here’s an in-depth version of the reply I gave, with the benefit of links to deeper material.

The other day I learned that Ben Frederickson has written an awesome new Python profiler called py-spy!

It takes a similar approach to profiling as rbspy, the profiler I worked on earlier this year – it can profile any running Python program, it uses process_vm_readv to read memory, and it by default displays profiling information in a really easy-to-use way.

According to Wikipedia:
   In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.

You can say that EDA is statisticians way of story telling where you explore data, find patterns and tells insights. Often you have some questions in hand you try to validate those questions by performing EDA.