In this chapter, we looked at how to manage data science projects. We explored how analytical projects differ from software engineering projects and studied the data science project life cycle. We looked at how we can choose a project management methodology that suits our needs and uncovered practical guidelines for estimating data science projects, and also discussed the limitations of long-term plans. No matter how good your plans and estimates are, data science projects have many inherent risks that can become the failing points of your projects.
In the next chapter, we will look at common pitfalls of data science projects.
