Creating a Course

TODO Automate this as much as possible.

Let's say we're going to create a new course on Kaggle Learn called Data Science.

Notebooks

1. On the command line, navigate to the learntools/notebooks/ directory.
2. Create a new branch on master with a name like ds-course. Be sure to check that there isn't already a branch with that name.
3. Decide on a "track name" like data_science. This will be the name of the directory where your course files will exist. Check that there isn't already a directory with that name.
4. There should be a Bash script called new_track.sh. Run /.new_track.sh data_science.
5. Stage the new files: git add data_science.
6. Commit the changes: git commit -m "Create track ds-course."
7. Create a pull request on GitHub named [Data Science] New course.

Checking Code

1. Navigate to the learntools root directory learntools/ (the directory containing setup.py).
2. Install an editable version of learntools: pip install --editable . (note the period).
3. Navigate to learntools/learntools.
4. Create a directory for your course: mkdir data_science.
5. Create an initialization file: touch data_science/__init__.py.
6. Commit the changes.

Datasets

Create a folder to contain local copies of the course data: mkdir learntools/notebooks/input. This folder will just be for your own use while developing and won't be committed to the repository (it's in notebooks/.gitignore).

Create a folder for a course dataset: mkdir input/ds-course-data. Put all of the data you plan to use in here. If you develop your notebooks in the raw folder (notebooks/data_science/raw/), then you can access your datasets just like you would on Kaggle, like '../input/ds-course-data/data.csv'.

Jenkins

Add track name 'data_science' to TRACKS and TESTABLE_NOTEBOOK_TRACKS in learntools/notebooks/test.sh.

Create a new file setup_data.sh in learntools/notebooks/data_science/:

#!/bin/bash
# Download the datasets used in the ML notebooks to correct relative_paths (../input/...)

mkdir -p input

DATASETS="ryanholbrook/ds-course-data ryanholbrook/some-other-data"

for slug in $DATASETS
do
    name=`echo $slug | cut -d '/' -f 2`
    dest="input/$name"
    mkdir -p $dest
    kaggle d download -p $dest --unzip $slug
done

You'll need to keep this list of datasets in DATASETS up-to-date with those you use in your course (that is, those defined in track_meta.py).