This chapter covers
- Cleaning up, documenting, and storing project materials
- Making sure you or someone else can find and restart the project later
- Project postmortem reflection and lessons
- A project as data science experience and its products as tools
Figure 13.1 shows where we are in the data science process: wrapping up the project. As a project in data science comes to an end, it can seem like all the work has been done, and all that remains is to fix any remaining bugs or other problems before you can stop thinking about the project entirely and move on to the next one (continued product support and improvement notwithstanding). But before calling the project done, there are some things you can do to increase your chances of success in the future, whether with an extension of this same project or with a completely different project.
