The data science ecosystem is constantly evolving. Packages are updated, new software is released, and fresh strategies are developed. For educators, the speed of the changes can be dizzying. They must stay proficient in their skills and informed on the advancements in teaching statistics and data science.
We’ve been busy here at RStudio and wanted to share some exciting new tools and features for those teaching data science. If these tools pique your interest, we are hosting a workshop on Designing the Data Science Classroom at rstudio::conf(). You will acquire concrete guidance on content, workflows, and infrastructure to employ these tools in your teaching. Watch a video on three reasons why you might be interested, or read more below:
Learn what’s new in data science
The tidyverse and tidymodels packages provide users with consistent design philosophy, grammar, and data structures for data science. There have been many updates in the past year, from new ways to wrangle data to the ability to use case weights.
With so many updates to relevant packages and tools, educators have to determine how to prioritize tweaking their course content. In our workshop, we’ll walk through what is important to incorporate into your curriculum.
Design a computational infrastructure
In addition to content, educators need to think about the computational infrastructure they will use in their teaching. Will students locally install software or use cloud resources? How will they interact with version control tools? And how will they get feedback on their work?
In this workshop, we will highlight RStudio Cloud as the tool of choice for the computational infrastructure of data science courses. It integrates with Git/GitHub, runs an RStudio IDE environment in the cloud (so that students do not have to run installations on their local machine), and allows educators to access, view, and edit projects. Recently, we introduced collaborative editing so that you can see students’ edits in real-time.
We will also introduce learnr, an R package that allows you to turn your R Markdown documents into interactive tutorials with automated feedback. These tutorials can be used for summative or formative feedback and can be highly enjoyable learning experiences for students.
Finally, in this workshop, you’ll also get a mini-module on R package development, specifically, making data packages for teaching purposes.
As you consider what concepts to teach in your classroom, a thorough knowledge of available tools and how to use them is key for a great classroom experience. Over two days of this workshop, you’ll get both a deep and broad coverage of content and tooling for teaching data science with R.
Engage with others
The data science community comprises members who aid and support each other. As you can imagine, many educators ask questions like, “Should I teach the base R pipe now?” or “How do I teach my students to find help online when they get stuck?”. By meeting and learning from like-minded people, you can find answers and inspiration to bring back to your classroom.
We are excited to share more about the new tools and ideas in the data science space. We’d love to see you at the Designing the Data Science Classroom Workshop in July. Sign up today!