What are the key methodologies in data science?
An example of an AI chatbot conversation about data science methodologies.
What are the key methodologies in data science?
Data science methodologies include CRISP-DM, KDD, and Agile Data Science. Each has its own approach to data analysis.
Can you explain the CRISP-DM methodology?
CRISP-DM stands for Cross-Industry Standard Process for Data Mining. It consists of six phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment.
How does Agile Data Science differ from traditional methods?
Agile Data Science emphasizes iterative development and collaboration, allowing teams to adapt quickly to changes and feedback, unlike traditional methods which are more linear.
What role does data cleaning play in these methodologies?
Data cleaning is crucial as it ensures the quality and accuracy of data, which directly impacts the outcomes of any analysis or model.
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