How do you define the steps to explore the data? I'm falling in love with data science and I'm spending a lot of time studying it. It seems that a common data science workflow is: 


Frame the problem
Collect the data
Clean the data
Work on the data
Report the results


I'm struggling to connect the dots when comes to work on the data. I'm aware that step 4 is where the fun happens, but I don't know where to begin. What are the steps taken when you work on the data? Example: do I need to find the central tendency or the standard deviation? Is machine learning needed? 

Ps: I know these are broad questions, so please answer it within your own domain expertise.
That is a very good framework of solving a question you have. According to me, it has multiple answers. I'll give you the one which I relate to.

After cleaning the data or rather while cleaning it, we have to be clear about the task ahead and about our results. 
The work on the data follows the below steps mostly:


Feature Detection
Training using the above features(there are many Machine Learning/Deep learning models to do this) like Classification (this depends on the task)
Check the trained model on validation test if needed and later on test set 
The features depend on the dataset. Standard deviation or to find central tendency isn’t always a criteria. Machine Learning is needed to train on the dataset in most of the cases.