Is there a standardized way to do data analysis?
If you only consider the process of making something from your data (excluding data extraction etc), I'd say something like :

Visualising your data to understand it
Cleaning your data, removing useless features etc
Feature engineering : keeping only relevant features, creating new features from existing ones (combining a few one, making ratio of 2 features for example)
Preprocesses (including simplifying your data if it's too precise, like too many classes in a category variable; encoding your variables so it can be taken by a model)
Testing and chosing the most adapted model
Tuning your model (using cross validation to have the best view of what your model does)
Try your model in brand new data (validation set) so you have the best estimation of how good it works, gather your results

This is pretty hard to give 'precise steps' since it differs from one project to another, depending on constraints, type of data, ...