A text designed to cover both the use of data science in education as well as data science education
A four day summer workshop for HS-aged Girl Scouts focused on learning and applying the principles of data science to water usage at the GSNETX STEM Center of Excellence
Introduction If you’ve found yourself here, you’ve probably been asked to create a reproducible example, or reprex, in response to a question you asked on the RStudio Community Site. This post provides a cursory overview of both creating a reprex as well as how to share your reprex on the RStudio Community site.
A brief reprex “how to” Install the development version of reprex from GitHub and then load the reprex package Highlight the code and associated packages, as indicated by library(package_name), like so: Copy the highlighted code by pressing Ctrl/Cmd + c In the console type reprex() and hit Enter/Return Everything that you need to post a reprex is now automatically stored on your clipboard!
It always starts with a DM on Twitter, where someone shares with me their personal data science ambitions, where they currently are in their plans, and then they follow up with a request for me to help them figure out where to go next.
I love these messages—they’re an affirmation that the R community continues to grow and attract new members in part by creating a welcoming and supportive space for beginners, and that our community members are deemed approachable (enough) for someone brand new to R to reach out!
Introduction Every day I talk to individuals who are working in one field, but are interested in learning more about how and where to get started in data analysis either as a hobby, or as part of a broader career transition. While there are a myriad of options out there - from online bootcamps to self-guided study through various web platforms and textbooks - it can be daunting to start something like this on your own.
Data is everywhere, and it seems you can’t go more than 48 hours without hearing how data scientists are going to rule the world–if only we can train enough of them.
The thing is, you don’t need to wait until after you get that Master’s (or PhD) in Data Science, or even until complete that online course that cost you several thousand dollars. You can start working with data now, and depending on your interests, skills, and commitment to learning, be job-ready in six months to a year.
One of the biggest pain points for both teachers and learners of data science in R is navigating the often unspoken prerequisite skills and content knowledge necessary to successfully apply R to data science problems. In this talk, R for data science educators will learn actionable strategies to more effectively bring learners up to speed, while learners will develop strategies to identify and address their own knowledge gaps.
By incorporating learnings from the establishment of a data-driven culture at Teaching Trust, coupled with her experience creating and leading the R for Data Science Online Learning Community, Jesse will share strategies that can be immediately implemented with groups of any size in order to more quickly develop data science skills in R.