Deployed 7d94583 with MkDocs version: 1.5.2

Author: imarranz

03_workflow/034_workflow_management_concepts.html

@@ -232,6 +232,7 @@ <h3 id="models">Models<a class="headerlink" href="#models" title="Permanent link
 <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>
+<div align="center">
 <div class="mermaid">graph TD
     subgraph CRISP-DM
         style CRISP-DM fill:#EEEEEE, stroke:#333, stroke-width:2px
@@ -261,8 +262,10 @@ <h4 id="crisp-dm_cross-industry_standard_process_for_data_mining">CRISP-DM (Cros
     style E fill:#aaa,stroke:#333,stroke-width:2px
     style F fill:#aaa,stroke:#333,stroke-width:2px
 </div>
+</div>
 <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>
+<div align="center">
 <div class="mermaid">graph TD
 
     subgraph TDSP
@@ -297,9 +300,11 @@ <h4 id="tdsp_team_data_science_process">TDSP (Team Data Science Process)<a class
     style E fill:#aaa,stroke:#333,stroke-width:2px
     style F fill:#aaa,stroke:#333,stroke-width:2px
 
+</div>
 </div>
 <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>
+<div align="center">
 <div class="mermaid">graph TD
     subgraph KDD
         style KDD fill:#EEEEEE, stroke:#333, stroke-width:2px
@@ -334,9 +339,11 @@ <h4 id="kdd_knowledge_discovery_in_databases">KDD (Knowledge Discovery in Databa
 
     end
 
+</div>
 </div>
 <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>
+<div align="center">
 <div class="mermaid">graph LR
 
     subgraph GUO
@@ -408,7 +415,6 @@ <h4 id="guos_data_science_workflow">Guo's Data Science Workflow<a class="headerl
 
     end
 
-
     style A fill:#aaa,stroke:#333,stroke-width:2px
     style B fill:#aaa,stroke:#333,stroke-width:2px
     style C fill:#aaa,stroke:#333,stroke-width:2px
@@ -425,6 +431,7 @@ <h4 id="guos_data_science_workflow">Guo's Data Science Workflow<a class="headerl
     style N fill:#aaa,stroke:#333,stroke-width:2px
     style O fill:#aaa,stroke:#333,stroke-width:2px
 
+</div>
 </div>
 </div>
 </div><footer>

index.html

@@ -354,5 +354,5 @@ <h3>Model Implementation and Maintenance</h3>
 
 <!--
 MkDocs version : 1.5.2
-Build Date UTC : 2024-07-16 14:49:17.941806+00:00
+Build Date UTC : 2024-07-16 15:03:14.425346+00:00
 -->