For some context, this post is largely borne out of a conversation thatĪlison Hill and I had a few months back where we discussed the pains of developing and maintaining online educational content. And, that’s really what this blog post sets out to discuss. With eye on our goal (make the thing!) and our non-negotiables determined (plain text, no recording videos, and could be completed on a Chromebook), we were ready to start building. Markua, which is a slightly-specialized Markdown syntax.Īs for the videos, that didn’t exist yet. Their platform allowed everything to be written in While their experience has historically been as an online publisher of books, they were developing their courses platform at the same time we were developing CBDS. This would thus enable us to generate automated videos that would be re-compileable in an instant, rather than re-recorded in a painful and time-consuming manner. We wanted those issues to be things of the past.įor this CBDS endeavor, we planned to develop the content on a platform that allowed all of the content to be plain text.
R studio for chromebook update#
This happens frequently with online courses because to update MOOCs, you typically have to not only interact with a proprietary platform to update content but you also have to spend hours in front of the camera to re-record and edit videos.
R studio for chromebook code#
This meant that a lot of the code and examples presented in the MOOC were (almost instantly) out-of-date. Jeff Leek tells a great story about how the Data Science Specialization team released their MOOC, only for Hadley Wickham to release dplyr right after their initial launch. Regardless, it was settled: anyone with an Internet connection would be able to complete this program.įor the second two non-negotiables, these were largely informed by our previous development of theĭata Science Specialization – a popular MOOC hosted on the Coursera Platform. So committed in fact, we initially named the program Chromebook Data Science, which was later changed to Cloud-Based Data Science 2. With positive results, we were committed. Proof of concept experiment, working exclusively on a Chromebook for months to ensure that doing data science is possible on a Chromebook. Chromebooks are available at most libraries and are way more affordable than typical laptops. We wanted this content to be as accessible as possible.
have videos that were actually updateable.1 Given my interest in education, it was a very natural fit that I (with others!) would focus on developing the content for the courses that would become the online program.įrom the first discussions of this project we had a few non-negotiables. Naturally, there was a whole team of people focused on making this happen, and our roles were distributed across the many moving pieces. I spent a year solely focused on getting CBDS off the ground. With the benefit of hindsight, this was kind of a wild dream. You see, we had this dream of getting people who typically had limited to no access to data science education an education in data science for free, in a short period of time, and without any requirements for previous programming or advanced math training. Team of incredible people to make Cloud-Based Data Science (CBDS) a thing.
That’s probably a different blog post though.Īnyway, before I started at UC San Diego, I was in Baltimore at Johns Hopkins Bloomberg School of Public Health working with the Okay, I also have to do all that less-than-fun administrative stuff that goes along with teaching really large undergraduate course, but naturally using R makes even most of that a bit easier.
R studio for chromebook how to#
This means that I get to spend my days teaching, working with students and instructional staff members and thinking about how to do all of that better. I’m now an Assistant Teaching Professor at UC San Diego. Ultimately, I’m writing this so that I can propose a dream platform for online educational content that learns from our mistakes/ experience, in not-so-secret hopes that someone else builds it. We’ll talk about what it is, its development history, and a few associated pain points. This is a story about one of my favorite topics:Ĭloud-Based Data Science.
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