We cannot capture all that is Data Science. Nor can we keep up - the pace at which this field progresses outdates work as fast as it is produced. As a result, we have opened this field guide to the world as a living document to bend and grow with the community, technology, expertise, and evolving techniques. Therefore, if you find the guide to be useful, neat, or even lacking, then we encourage you to add your expertise, including:
- Case studies from which you have learned
- Citations for journal articles or papers that inspire you
- Algorithms and techniques that you love
- Your thoughts and comments on other people’s additions
Everyone you will ever meet knows something you don't.
Email us your ideas and perspectives at [email protected] or submit them via a pull request on the Github repository.
Tell us and the world what you know. Become an author of this story.
In the original content, we picked a few topics of interest to us and gave examples. Feel free to add an example to an already mentioned topic or begin a new topic.
Building Blocks - References to Learn From
We all have to start somewhere and where is better than with source from someone/somewhere that is doing great things?
Journal articles inspire by sheding light on the possible. It can be so much easier to be critical than to complement at times, but we really would rather focus on papers with techinques or points of view that you have used to be successful. Journal reviews or examples that are rude or attack authors personally will not be posted.
Have you taken an online tutorial that you think is worth while?
Do you have a favorite library or open source software package that you couldn't live without?!?! Let's hear it.
Please note that we cannot possibly police all hyperlinks put on here, but we will do our best. If you find a link that is bad or broken, please file an issue immediately.
##How to Contribute
- Pull requests
- Email, [email protected]
- Blog posts