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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>
💡 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.
❗ 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.
Project | Merge Resolving Conflicts
Lab | Entity Relationship Diagram
Lab | Python List Comprehension & Error Handling
Lab | Python String Operations & Functional Programming
Project | API and Web Data Scraping
Lab | Subsetting and descriptive stats
Lab | Dataframe Calculation and Transformation
Lab | Pivot Table and Correlations
Project | Data Analysis with Pandas
Lab | DataViz with Seaborn & Matplotlib
Project | Visualizing Real World Data
Project | Storytelling with Data Visualizations
Lab | Probability Distribution
Project | Calculating Game of Chances
Lab | Introduction to BI and Tableau
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