4.2 KiB
For Educators
Would you like to use this curriculum in your classroom? Go ahead!
In fact, you can use it directly on GitHub by leveraging GitHub Classroom.
To do this, fork this repository. You'll need to create a separate repository for each lesson, so you'll have to extract each folder into its own repository. This way, GitHub Classroom can handle each lesson individually.
These detailed instructions will guide you on how to set up your classroom.
Using the repository as is
If you'd prefer to use this repository as it currently exists, without GitHub Classroom, that's also possible. You'll need to coordinate with your students on which lesson to work through together.
In an online format (Zoom, Teams, or similar), you could create breakout rooms for quizzes and mentor students to prepare them for learning. Then, invite students to complete the quizzes and submit their answers as 'issues' at a designated time. You could follow the same approach for assignments if you want students to collaborate openly.
If you'd rather use a more private format, ask your students to fork the curriculum, lesson by lesson, into their own private GitHub repositories and grant you access. This way, they can complete quizzes and assignments privately and submit them to you via issues on your classroom repository.
There are many ways to adapt this for an online classroom setting. Let us know what works best for you!
Included in this curriculum:
20 lessons, 40 quizzes, and 20 assignments. Sketchnotes are included to support visual learners. Many lessons are available in both Python and R and can be completed using Jupyter notebooks in VS Code. Learn more about setting up your classroom to use this tech stack: https://code.visualstudio.com/docs/datascience/jupyter-notebooks.
All sketchnotes, including a large-format poster, are located in this folder.
The entire curriculum is available as a PDF.
You can also run this curriculum as a standalone, offline-friendly website using Docsify. Install Docsify on your local machine, then in the root folder of your local copy of this repository, type docsify serve. The website will be served on port 3000 on your localhost: localhost:3000.
An offline-friendly version of the curriculum will open as a standalone web page: https://localhost:3000
Lessons are organized into six parts:
- 1: Introduction
- 1: Defining Data Science
- 2: Ethics
- 3: Defining Data
- 4: Probability and Statistics Overview
- 2: Working with Data
- 5: Relational Databases
- 6: Non-Relational Databases
- 7: Python
- 8: Data Preparation
- 3: Data Visualization
- 9: Visualization of Quantities
- 10: Visualization of Distributions
- 11: Visualization of Proportions
- 12: Visualization of Relationships
- 13: Meaningful Visualizations
- 4: Data Science Lifecycle
- 14: Introduction
- 15: Analyzing
- 16: Communication
- 5: Data Science in the Cloud
- 17: Introduction
- 18: Low-Code Options
- 19: Azure
- 6: Data Science in the Wild
- 20: Overview
Please give us your thoughts!
We want this curriculum to work for you and your students. Share your feedback in the discussion boards! Feel free to create a classroom section on the discussion boards for your students.
Disclaimer:
This document has been translated using the AI translation service Co-op Translator. While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.