Explore space with Python and Visual Studio Code; inspired by Netflix’s Over the Moon Microsoft Quiz Answers

Get Explore space with Python and Visual Studio Code; inspired by Netflix’s Over the Moon Microsoft Quiz Answers

Learning to code sometimes feels out of reach. But if we’ve learned anything from the people who have burst through our atmosphere, orbited our Earth, or walked on the Moon, it’s that goals that seem out of reach require determination and passion. In this learning path, we’ll draw on these themes from the story line of Over the Moon.

Over the Moon is a film about Fei Fei, a young girl who builds a rocket to the Moon on a mission to prove the existence of a legendary Moon Goddess. The girl is fueled by the memories and love of her mother to use determination and imagination to accomplish something beyond this world: reach the Moon. Although the story takes place in a beautifully drawn universe, it’s directly related to the types of problems real-life engineers face as they prepare and execute missions to the Moon and beyond.

These modules were inspired by Over the Moon and the story of real NASA engineers and astronauts. Tying together principles of data science, machine learning, and artificial intelligence with tools like Python, Visual Studio Code, and Azure, these modules guide you through upskilling in tech while imagining how to solve space travel–related challenges. And don’t forget to watch Over the Moon on Netflix today!

Through these modules, you will learn how to:

  • Aggregate data from multiple sources into pandas DataFrames
  • Use Python to explore, cleanse, and manipulate your data
  • Ensure ethical practices throughout AI development
  • Train a Custom Vision AI model on a particular animal

Tip:

This learning path is part of a multimodal learning experience. Start the first module to see how you can follow along!

Prerequisites:

None

Enroll on Microsoft

Module 1: Plan a Moon mission by using Python pandas

Like Fei Fei, use data to plan your own mission to the Moon. Ensure that your rocket can not only get you there, but also bring you and all your Moon rocks safely back to Earth.

Learning objectives:

In this module, you will:

  • Create a clear representation of data from many sources.
  • Use Python and pandas to explore data.
  • Use data cleansing techniques to get a clear representation of data.
  • Hypothesize how much rock sample astronauts might bring back on the Artemis missions.

Tip:

This module is part of a multimodal learning experience. Start the module to see how you can follow along!

Prerequisites:

This module is part of these learning paths:

Quiz 1: Knowledge check

Q1. If you have a pandas DataFrame called samples, what would the output be for samples.head()?

  • The top row of the DataFrame
  • The top five rows of the DataFrame
  • 10 random rows from the DataFrame
  • The column names of the DataFrame

Q2. In pandas, DataFrame and series are two names for the same kind of data type.

  • True
  • False

Q3. What is the total weight of the samples that were brought back by the six Apollo missions that landed on the Moon?

  • Over 825 lb, or 375 kg
  • Over 63 lb, or 28 kg
  • Over 136 lb, or 62 kg
  • Over 490 lb, or 92 kg

Module 2: Predict meteor showers by using Python and Visual Studio Code

Learn how to use concepts from machine learning to predict the occurrence of meteor showers (or Moon Goddess tears).

Learning objectives:

In this module, you’ll learn:

  • The basics of meteor showers: what they are and why we see them.
  • How to choose and collect appropriate data.
  • Strategies to cleanse and manipulate your data.

Tip:

This module is part of a multimodal learning experience. Start the module to see how you can follow along!

Prerequisites:

This module is part of these learning paths:

Quiz 1: Knowledge check

Q1. How often do we see meteor showers on Earth?

  • Only when Earth aligns with certain planets or moons.
  • Meteor showers originate from Mars, which has about a two-year orbit around the Sun.
  • Meteor showers happen every night.
  • Every 76 years, when the Halley comet orbits near the Sun.

Q2. Where can you find meteorites on Earth?

  • Antarctica
  • The desert
  • Your backyard
  • All of the above

Q3. What would the following code return? some_return_value = friends.loc[(friends['location'] == my_location) & (friends['hunger'] >= my_hunger), 'favorite_food'].tolist()

  • A list of all the favorite foods of my friends who were with me and who were at least as hungry as I was.
  • A list of my friends, sorted from least hungry to most hungry, who were with me and had the same favorite food as me.
  • A list of the locations of my friends who were at least as hungry as I was and who had a favorite food.
  • A list of all my friends who were with me, who were at least as hungry as I was, and who had the same favorite food as me.

Module 3: Use AI to recognize objects in images by using the Custom Vision service

Use the Custom Vision service to analyze images of animals, like Bungee, without ever writing code.

Learning objectives:

This module is part of a multimodal learning experience. Start the module to see how you can follow along!

In this module, you will:

  • Create a good dataset of images for training AI.
  • Train a Custom Vision AI model on a particular animal.
  • Test the model you trained.
  • Ensure ethical practices throughout AI development.

Tip:

This module is part of a multimodal learning experience. Start the module to see how you can follow along!

Prerequisites:

None

This module is part of these learning paths:

Quiz 1: Knowledge check

Q1. How much code is required to use the Custom Vision service?

  • To use the Custom Vision service, you need to write an entire artificial intelligence algorithm.
  • You can call the Custom Vision service only by using C# code.
  • There’s a browser option where you can use it without any code.
  • It takes only 50 lines of code to use the Custom Vision Service.

Q2. How many images do you need, at a minimum, to use to train the Custom Vision service to identify an object in an image?

  • 1,000
  • 1
  • 15
  • 100

Q3. If you’re using cloud resources in the East US Region to train your Custom Vision model, which region do you need to provision resources in to run the predictions?

  • Any available region
  • East US
  • West US 2
  • East US 2
Conclusion:

I hope this Explore space with Python and Visual Studio Code; inspired by Netflix’s Over the Moon Microsoft Quiz Answers would be useful for you to learn something new from this problem. If it helped you then don’t forget to bookmark our site for more Coding Solutions.

This Problem is intended for audiences of all experiences who are interested in learning about Data Science in a business context; there are no prerequisites.

Keep Learning!

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