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Data Science Methodology: Best Practices for Successful Implementations

In the domain of data science, solving problems and answering questions through data analysis is standard practice. Often, data scientists construct a model to predict outcomes or discover underlying patterns, with the goal of gaining insights. Organizations can then use these insights to take actions that ideally improve future outcomes. 

The flow of the methodology illustrates the iterative nature of the problem-solving process. As data scientists learn more about the data and the modeling, they frequently return to a previous stage to make adjustments, iterate quickly and provide continuous value to the organization. Models are not created once, deployed and left in place as is; instead, are continually improved and adapted to evolving conditions.   

Learn how the flow of data science methodology provides continuous value to your organization with this white paper.

Content provided by IBM


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