Summary
You've watched Fei Fei design, prototype, test, and ultimately succeed at creating a rocket to reach the Moon. And you've seen some of what NASA scientists and engineers might analyze to determine how to account for every ounce of weight on a rocket. It's now clear why data-science skills and practices are so critical to space exploration.
NASA might have a lot more information and clearer standards than we did for this analysis, but the exploration and understanding of data from multiple sources is pervasive throughout many other industries. So you can apply the knowledge and skills you've acquired to solving countless other problems.
In this module, you have:
- Gathered information about samples brought back from the Moon via the Apollo missions.
- Acquired data about the types of spacecraft and rockets used for the Apollo and upcoming Artemis missions.
- Compiled DataFrames, or tables, of that data, which tells stories and provides insights.
- Created a prediction of how much sample weight could be returned from the Artemis missions.
- Made a recommendation for the amount and types of rocks the Artemis astronauts should focus their efforts on, based on the rocks that are currently being used for research here on Earth.
The data journey can take many turns. With your new data-wrangling skills, you should feel empowered to find other datasets or cut the data that's used here in new ways. By doing so, you'll uncover new information about space exploration and Moon sample recovery, or about another problem you might be interested in.
Tip
Remember, this module is part of a multimodal learning experience. Follow along with a video walkthrough of the module in a new tab.
And with that, our champion ends her journey to the Moon.
