In the previous chapter, we looked at innovation management. We developed recipes that can help find ideas for data science projects and matched them with their market demand. In this chapter, we will cover the non-technical side of data science project management by looking at how data science projects stand out from general software development projects. We'll look at common reasons for their failure and develop an approach that will lower the risks of data science projects. We will conclude this chapter by diving into the art and science of project estimates.
In this chapter, we will look at how we can manage projects from start to end by covering the following topics:
- Understanding data science project failure
- Exploring the data science project life cycle
- Choosing a project management methodology
- Choosing a methodology that suits your project...
