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LIFE CYCLE PHASES 1 , - Coggle Diagram
LIFE CYCLE PHASES 
DATA ANALYTICS :
Model planning
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data science teams create data sets that can be used for training for testing, production, and training goals
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Model building
Tools : Rand PL/R, Octave, WEKA
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Data preparation
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investigate the possibilities of pre-processing, analysing, and preparing data before analysis and modelling
Tools : Hadoop, Alpine Miner, Open Refine, etc.
Communication results
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The team is discussing how to best deliver results and conclusions to team members and other stakeholders.
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discovery
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operationalize
The team produces the last reports, presentations, and codes
Tool : WEKA, SQL, MADlib, and Octave
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DATA SCIENCE :
Business understanding
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Even a minute error in defining the problem may be very important for the project hence it is to be done with maximum precision.
After asking required questions we moved to next stage,data collection
Data collection
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Data preparation
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At this stage, exploratory data analysis (EDA) is crucial since summarizing clean data allows for the detection of the data's structure, outliers, anomalies, and patterns.
Data modelling
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in this model, we take data as a input and try to prepare output
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Model deployment
before model is deployed ,ensure that you have chose right solution
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DATA MINING :
Data gathering
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wherever the data comes from, data scientist moves data to data lake for further process
Data preparation
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Unless a data scientist is attempting to evaluate unfiltered raw data for a specific application, data transformation is also done to make data sets consistent.
Mining the data
once the data is prepared , data scientist choose appropriate data mining techniques
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Data analysis and interpretation
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The data scientist must communicate the findings to business executives and users through data visualization and data storytelling techniques.
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