-
Install grip with
brew install grip
(Mac) orpip install grip
(Windows). -
Start local Markdown server:
$ grip -b README.md 8080 --user <your-github-username> --pass <your-github-password>
๐ก grip
uses the GitHub Markdown API to render the files in localhost so that you'll see exactly how GitHub would render the Markdown files. Running grip
with your Github username and password will allow you to make unrestricted requests to GitHub. If you see error when you run the problem that says GitHub Rate Limit Reached
, it's because you didn't run grip with your GitHub credentials or the provided credentials are incorrect.
To work on your first assignment, create a branch of your own with your name (change the branch name unless your name is John Doe):
$ git checkout -b john-doe
Each project/lab has its own directory in which you'll find a README.md
file and a sub-directory named your-code
. The descriptions and requirements of the assignment can be found in the README file. When you work on the assignment, create your code files in the your-code
directory and save regularly while you work.
After you finish, add those files to git, commit, and push your branch to GitHub. In the commit message, specify which lab/project you are submitting. For example:
$ git add <files-to-add>
$ git commit -m "Module 1 MySQL project"
$ git push origin john-doe
The instructional team will review your branch and provide feedback.
To work on the subsequent assignments, keep using the same branch you created and push your new codes to GitHub.
โ Update your branch regularly because the curriculum development team is developing new assignments for you as the course proceeds. Make sure you have committed all your codes then exectue git pull origin master
to obtain the latest code from the master
branch.
lab-code-simplicity-efficiency
lab-object-oriented-programming
lab-correlation-tests-with-scipy
lab-df-calculation-and-transformation
lab-pivot-table-and-correlation
lab-plotting-multiple-data-series
lab-storytelling-data-visualization
lab-subsetting-and-descriptive-stats
lab-tableau-data-visualization
lab-two-sample-hypothesis-tests
visualizing-real-world-data-project
Lab | Intro to Machine Learning
Lab | Feature Extraction and Introduction to Supervised Learning
Lab | Unsupervised Learning with Scikit-Learn
Lab | Supervised Learning with Scikit-Learn
Project | Unsupervised Learning (Clustering)
Lab | Machine Learning Pipelines
Project | Machine Learning Pipeline
Advanced Topics: Network Analysis