Life Cycle of Data Science | (Data Science ss : 4)

Life Cycle of Data Science

This are the stages involved in the life cycle of Data Science

Lets see all the stages of life cycle one by one :

(i) Data Acquisition : 

Our initial step would be to integrate all the data & store it in one single location(data warehouse). Now this integrated data had to select a particular section to implement the data science tasks.

(ii) Data Pre-processing :

So the raw data we acquired could have been used directly for the data science tasks. But for better results this data needs to be processed by a applying certain operations such as normalization & aggregation etc.

  

(iii) Model Building :

This is the most important part in the data science life cycle. Here we apply different scientific or some machine learning algorithms such as different types of regressions, K-mean etc to find intrusting  insights.

All these Machine Algorithms & coding related to it i'll show you once we are done with the basic's of Data Science so stay tuned.

(iv) Pattern Evaluation :

Once we build a model on top of a data & extract some patterns its time to check the validity of this patterns i.e in this we check if the obtained information is correct, useful & new. If information satisfied all this conditions we consider the information to be valid else we discard it.

(v) Knowledge Representation :

Once the information is validated it's finally time to represent the information with simple easy statistic graphs.