<ul><li>The data science lifecycle is a framework that guides data science projects from conception to deployment.</li><li>It involves defining the problem, collecting data from various sources, cleaning and preparing the data, conducting exploratory data analysis, building models, evaluating and deploying the models.</li><li>Continuous monitoring and maintenance of the deployed models is also important, as well as effective communication of results to stakeholders.</li><li>By following this structured approach, organizations can leverage data science to make informed decisions and optimize operations.</li></ul>

What is the Data Science Lifecycle? A Complete Guide

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