Families and K-12 students are invited to join ASU Prep for a free, daily Hour of Code during Innovation Week 20201 from December 7-11. The Hour of Code started as a one-hour introduction to computer science, designed to demystify "code", to show that anybody can learn the basics, and to broaden participation in the field of computer science. It has since become a worldwide effort to celebrate computer science, starting with 1-hour coding activities but expanding to all sorts of community efforts.
Innovation Week Programming
Monday, Dec 7
3:30 - 4:30 p.m.
Emoticon Madness
This activity is designed to give you your first experience programming, and has been specially designed for the Hour of Code. Use the programming language Python to explore emoticons and text manipulation.
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Tuesday, Dec 8
3:30 - 4:30 p.m.
Don't Yeet the Yeti
You will learn how to edit and create conditional statements. They will “complete” an unfinished game by adding JavaScript code to rescue a Yeti and their friends! If their code doesn’t work, then the Yeti will be Yeeted (tossed from the game).
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Wednesday, Dec 9
3:30 - 4:30 p.m.
Trivia Chatbot
Join a robot monkey in an hour-long course where you will learn the basics of Python and chatbot programming. In the activity, you will advance through a 15 exercise-long course in which you will learn how to program your very own trivia chatbot.
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Thursday, Dec 10
3:30 - 4:30 p.m.
Intro to App Lab
Create your own app in JavaScript using blocks or text. You'll make a simple app with buttons, images, sounds and multiple screens that you can share with your friends or publish to a public gallery. If you've already done some coding with blocks, take your skills to the next level.
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Friday, Dec 11
3:30 - 4:30 p.m.
Quick Draw Emoji with Machine Learning
Build a live interactive website that uses a Machine Learning model to recognize your and your friends' hand-drawn emojis! Along the way you'll get hands-on practice with core concepts of Machine Learning - 1) gathering data, 2) cleaning and formatting the data, 3) training the model, and 4) using the trained model to make predictions.
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