Explore the fascinating intersection of data science and lunar exploration in this 51-minute Microsoft video. Dive into the world of moon missions, both fictional and real, as Dr. G draws inspiration from Netflix's "Over the Moon" and NASA's Apollo missions. Learn how to analyze and clean data to predict moon rock collection for the upcoming Artemis Program. Follow along with hands-on Python coding in Visual Studio Code, using pandas to manipulate and explore NASA's Lunar Rock Curation data. Discover the importance of weight in space exploration, create dataframes to compare missions, and determine optimal sample collection strategies. No prior coding experience required. Gain valuable insights into data-driven decision-making for space exploration and leave with practical recommendations for future lunar missions.
Overview
Syllabus
- Introduction and Agenda for the episode.
- Setup local environment guidelines.
- Setup for this project and intro to Python notebooks in VSCode.
- Choosing data for analysis - NASA Lunar Rock Curation.
- Discussion on importance of weight in space exploration.
- Defining problem to solve through data science practices.
- Import and explore data into notebook with pandas.
- Manipulate rock sample data to match rocket data.
- Create dataframe to view data based on unique missions.
- Add total sample amount for each mission.
- Comparing missions.
- Removing not-a-number values.
- Add rocket ship data into dataframe.
- Add rocket payload data and determine ratios.
- Add Artemis data.
- Estimate sample weight per Artemis mission.
- Determining the right samples to collect.
- Exploring the cut of samples to prioritize.
- Finalizing the samples that represent the profile for new samples.
- Create the dataframe final profile for new sample gathering.
- Determine recommendations based on Artemis constraints.
- Recap.
Taught by
Microsoft Developer
