today I want to share with you some notes about Data Science.
In Data Science everything starts from giving questions, or as to say to giving right questions. Here it goes list of question which data scientists ask in order of rising complexity:
So, the first goes descriptive analysis. You just describe what you see, and assume what that may be but not necessary is.
Exploratory analysis goes for searching relationship which you want to discover, but not necessary confirm them. EA is not final conclusion and shouldn't be use for generalizing/predicting.
Inferential analysis is something like mathematical induction. You take small part of data in order to make conclusion about all data. It can be compared to taking one spoon of soup in order to generalize about all soup.
Predictive analysis intended for taking some data from object A in order to predict behavior of object B.
Causal analysis intended what will happen to one variable if you change another. For example if you give some drug to person, will he live longer.
Mechanical analysis is very taught. Purposed to grasp how changes of some variables lead to changes in another variables of individual objects.