The challenge One skill that all great educators possess is the ability to ask questions. Asking the right questions at the right time of the learners in your classroom can facilitate understanding, uncover misconceptions, and indicate whether or not learners have mastered the material.
However, when you’re learning on your own you have to simultaneously fill the roles of both learner and educator, and not only know both how and when to ask yourself questions, but also answer your questions, evaluate your answers, and redirect your learning path as you progress.
The problem I’ve been working with preliminary student State of Texas Assessments of Academic Readiness (STAAR) data pulled from the Data Interaction portal. The data I’m interested in includes proficiency levels for every subject for every individual school, disaggregated by all available demographic splits.
Because downloading all of the data will likely require building a webcrawler, I’ve started this project by working with a small subset of 20 Elementary and Middle Schools in the Dallas Independent School District (DISD) for the 2018 school year.
Introduction As all amazing opportunities in my life are wont to do, it started with a tweet:
…which resulted in me spending last Friday talking data science education with some of the great folks at JHU, largely centered on work happening with the Chromebook Data Science project.
It’s rare that I find myself dealing with imposter syndrome, but I did spend Thursday night eating all of my feelings of doubt and insecurity.
Learning to learn can be… well, challenging I find myself on social media for the longest stretches of time when I’m just beginning a project. Social media is a fantastic distraction from getting started, because burying yourself in the seratonin hits from a Like or :heart: is far more pleasant than staring at the sheer amount of project yet to do.
It doesn’t even have to be a big project to send us into a perpetual state of procrastination.
We’re going on a learning adventure! This summer we’d like to challenge you to participate in the “Summer of Data Science” Twitter initiative hosted by none other than Data Science Renee! While the Summer of Data Science (SoDS) supports all languages and endeavors related to becoming a (better) data scientist, we wanted to host a space within our R4DS Online Learning Community for our community members to ask questions, provide support, and in general discuss what we’re learning within our chosen text!
Introduction May is here and it’s time to get back to our roots by revisiting the R for Data Science text as well as introduce materials to help you - yes you - get comfortable with GitHub.
Sure, we could do something similar to the first iteration of our online learning community and say we’re going to cover a specified amount of material each week, but instead we’re going to try something new!
Introduction April is such a fantastic time of the year - the weather starts to warm up, trees and flowers start blooming, and many of us start in on some spring cleaning. So it’s only natural that we’d focus on the same thing in our R4DS Online Learning Community!
Finishing up March’s Viewing Parties Thank you to everyone who participated in our viewing party in early March, where member Chris B.
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!
Background In August of 2017 I launched an experiment, referred to as the R for Data Science Online Learning Community, with the goal of creating a supportive and responsive online space for learners and mentors to gather and work through the R for Data Science text.
Like most online learning endeavors, we had a massive surge of interest at the onset, with exponential drop-offs week after week as we progressively worked through each chapter based on an established schedule.