In this chapter, we will explore the common pitfalls of data science projects, as well as the mistakes that increase the risks your projects may encounter and that are easy to commit. It's important that you know how to deal with them for the success of your projects. Different types of data science solutions have many tempting ways of executing the project that can lead to undesired difficulties in the later stages of the project. We will pick and mitigate those issues one by one while following the data science project life cycle.
In this chapter, we will cover the following topics:
- Avoiding the common risks of data science projects
- Approaching research projects
- Dealing with prototypes and minimum viable product (MVP) projects
- Mitigating risks in production-oriented data science systems
