Five stages in data science life cycle

There are five stages in data science life cycle:
1) Data Extraction: Extracting data from source for subsequent processing and analysis.
2) Scrubbing Data : Here the data is cleansed, and all the redundant and irrelevant data will be eliminated.
3) Data Exploration : Exploring and visualizing data in order to discover insights or trends.
4) Model Building : Selecting a statistical, mathematical, or simulating model to acquire insights and make predictions.
5) Data Interpretations : Developing a reasonable scientific argument to comprehend your data and provide your inferences as conclusion.