Data Science Life Cycle

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Data Science Life Cycle
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• Understanding Topic
• Acquisition of Data
• Preparation of Data
• Exploring Data
• Predictive modeling and Evaluation
• Interpretation and Deployment

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Understanding Topic

• Firstly, Data Scientist identifies the
  problem and analyzes the problem for solution.
  This is a decisive phase in which they also find
  if such a case happened in the past.

 Acquisition Of Data
Data Acquisition is also called data
discovery or data collection. In this
acquisition, data is readily available for
working or you will be collecting data required
to a deal with the acquisition of data depends on
its quality and processing.
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Preparation Of Data

• Data Preparation is the most important step
  in this life cycle. It does not matter how you
  collected The data you must clean data and make
  it ready for analysis. During this stage data
  will be wobbling, so we will sometimes need to go
  back and collect the data required. Many data
  scientists say this preparation and cleaning of
  data consume 80 of time

 Exploring Data
Data Exploration is also called Data
Mining. This is a step where you start analyzing
and understanding the patterns of the data
prepared. You May Need to do additional cleaning
of data while analyzing it.
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Predictive Modeling and Evaluation

• In this, you try different combinations
  with your data to evaluate the outcomes. You will
  be noticing new things as you analyze your data
  set. Using separate validation sets of data to
  know how your model is performing.

 Interpretation and Deployment
Once your prediction model is confirmed you
outcome can interpret the data and results,
finally, your model is deployed and can be used
in real-time.
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Thank You

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