Data Science projects have a lot in common with other IT projects in general, and with Business Intelligence in particular. There are differences, however, and I’ve covered those for you here in this series on The Keys to Effective Data Science Projects. One of those areas where general projects and Data Science projects are similar is in the project close-out – and not for a good reason.
IT projects can take a long time – weeks or months. And in that time, there’s a lot of planning, euphoria, then obstacles, politics, changes, unexpected time and money issues, and a lot more drama than should be necessary. By the time the project is done – success or failure – most people really want to be done with it. Projects start with a lot of fanfare, then slowly trail off into a lack of communication. But that’s a bad thing, and not in keeping with the “Science” part of Data Science.
One of the most important keys you can remember to follow is to properly learn from and document the project itself. I see so many projects repeat the same errors as earlier projects, or ignore the success factors of previous endeavors. It causes even more waste, time, money and drama.
Happily, there’s a fix. Right at the start of your project, emphasize the last phase of the Team Data Science Process. Set aside time, budget, and personnel to document what worked, what didn’t, where things are, and why you did things the way you did.
The Team Data Science Process has a handy document template you can use – find it here: https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/project-execution
Now, let’s get started working on those projects. Remember these keys as you go:
- The Keys to an Effective Data Science Project – Part 1: The Team Data Science Process
- The Keys to Effective Data Science Projects – Part 2: The Question
- The Keys to Effective Data Science Projects – Part 3: Import the Initial Data
- The Keys to Effective Data Science Projects – Part 4: Explore the Data
- The Keys to Effective Data Science Projects – Part 5: Update the Data
- The Keys to Effective Data Science Projects – Part 6: Feature Selection
- The Keys to Effective Data Science Projects – Part 7: Create and Train the Model
- The Keys to Effective Data Science Projects – Part 8: Operationalize
- The Keys to Effective Data Science Projects – Part 9: Testing and Validation
Caffeinated Data Science 