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03_workflow/034_workflow_management_concepts.html

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 <p>In the realm of data science, several established workflow management models help guide teams through the complexities of data projects. These models are designed to ensure that every phase of a project aligns with business objectives and leverages data insights effectively.</p>
 <h4 id="crisp-dm_cross-industry_standard_process_for_data_mining">CRISP-DM (Cross-Industry Standard Process for Data Mining)<a class="headerlink" href="#crisp-dm_cross-industry_standard_process_for_data_mining" title="Permanent link">#</a></h4>
 <p>CRISP-DM is a widely adopted model that provides a comprehensive framework for carrying out data mining projects. It consists of six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. This model emphasizes a cyclical process allowing for continuous improvements based on insights gained from previous iterations.</p>
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 <h4 id="tdsp_team_data_science_process">TDSP (Team Data Science Process)<a class="headerlink" href="#tdsp_team_data_science_process" title="Permanent link">#</a></h4>
 <p>Developed by Microsoft, TDSP structures projects into five key phases: business understanding, data acquisition and understanding, modeling, deployment, and customer acceptance. It stresses the importance of iterative learning and effective communication within data science teams.</p>
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 <h4 id="kdd_knowledge_discovery_in_databases">KDD (Knowledge Discovery in Databases)<a class="headerlink" href="#kdd_knowledge_discovery_in_databases" title="Permanent link">#</a></h4>
 <p>KDD is a non-linear, iterative process focusing on the discovery of actionable knowledge from large volumes of data. This process involves selection, preprocessing, transformation, data mining, and the interpretation of the discovered patterns.</p>
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 <h4 id="guos_data_science_workflow">Guo's Data Science Workflow<a class="headerlink" href="#guos_data_science_workflow" title="Permanent link">#</a></h4>
 <p>Guo's model is particularly useful for ensuring that data science projects are reproducible and transparent. It suggests a workflow where programming and exploratory data analysis are carried out in tandem, allowing for a deeper understanding and more robust analysis.</p>
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