Learn more about the technical writers paired with NumPy and SciPy during Google Season of Docs
From September through November, our little corner of the open-source world is going to involve technical documentation updates at NumPy and SciPy!
Welcome to NumPy and SciPy!!!
You’re going behind the scenes to meet the people and learn about some of the work we’re doing right now at NumPy and SciPy.
A couple of weeks ago, I told you I would let you know more about the technical writers who are going to be working with NumPy and SciPy during Google Season of Docs. It’s time to meet Christina Lee!
If you aren’t familiar with the project, you can read all about it here:
What is Google Season of Docs?
Google did an amazing thing by creating Season of Docs. It built real opportunities for technical writers to collaborate with open source organizations.
Season of Docs is a three-month mentoring program that pairs technical writers with open source organizations. Writers have the opportunity to work with well-known and highly-regarded organizations. Open source organizations (who often don’t have a budget for technical writers) have the opportunity to work with experienced technical writers to improve and expand their existing documentation.
It’s pretty incredible.
I’m working with NumPy! Just to make things even cooler, there’s so much overlap between the NumPy and SciPy projects, that we get to meet frequently and collaborate with each other. That means that I get to update all of you with the changes we’re making!
Since I hadn’t yet learned a lot about Christina when I wrote the last post, it seemed like a good idea to use today’s post to introduce her to you.
I made a couple of very minor tweaks, but here’s what Christina had to say about herself and her plans:
Meet Christina Lee!
Overall, I want to improve SciPy.org and docs.scipy.org’s design and structure.
I’m returning to Python after being a Julia programmer, so I might be helpful for newbie proofing Python code. I write Julia Jupyter notebooks on a variety of physics and numerics topics, available at albi3ro.github.io/M4 . At JuliaCon, I gave a lightning talk on “Teaching with Code”, written up at http://albi3ro.github.io/M4/Teaching_With_Code.html, which summarizes my code teaching ideals.
From her proposal:
Work on both the SciPy website and docs.scipy needs to start with a structural and graphical overhaul. At each page, I cannot instinctively tell how to navigate to what I want, what the purpose of the page is, or what the page wants me to feel and do. While Sphinx may be the tool of choice for documentation, we can pull away from Sphinx for both the main website (scipy.org) and the tutorials in favor of a more versatile web layout. Designing two distinct layouts for scipy.org and docs.scipy.org will help clear up the confusion between the ecosystem and the package.
While reworking the container for the content would form a good portion of the GSoD project, I would also work on the content on the website. The content breaks down into tutorial pages and surrounding pages. For the tutorials, I would highlight the basic usage front and center to get users up and going rapidly. Then I would want to focus on explaining what the numerical method accomplishes and what is possible beyond basic usage. Tutorials already exist, but editing could make them better. Reworking the content on the main pages would help with the navigational and structure problems discussed above.
If it sounds exciting to work with organizations like NumPy and SciPy, just do it! Don’t wait! People get really overwhelmed at the idea of working on the code for an open-source organization. But there’s more going on than just the code. You can’t imagine how helpful it can be to have someone step in on the documentation side.
If you want to get involved with open-source projects, get involved. If you love to write (or you love to work on the writing other people have done), get in there and work your magic! It’s up to everyone to make the tech world an even more amazing place than it already is.
If you’re into data science, machine learning, artificial intelligence, or technology in general, then you’ve seen some documentation. If you’re having trouble understanding some of it, don’t sit back and wish things were different. Get in there and help.
Make a difference!
You might get to learn something new. You might even get to meet some incredibly cool people!
If you want to contribute to open-source organizations but don’t know how to use GitHub, check out this article:
Thanks for reading! As always, if you do anything cool with this information, let everyone know about it in the comments below!