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!
We’re going to walk through the basics of posting GIFs on your blogdown website using imgur and GIPHY.
Introduction Blogdown is an amazing tool for - wait for it - blogging. I recently made the jump to blogdown, and after working out all of my self-inflicted initial “OMG what happened now?!” issues, blogdown has become part of my daily workflow. I’m still playing with formatting issues (evidence below) but on the whole things are coming together nicely.
Background It took me eight years to finish my undergraduate degree, not because I took a lot of time off to volunteer or travel the world, but because I dropped out twice (twice!) due to never having learned to learn in my K-12 days.
Looking at my academic performance in high school, I wasn’t the kid you would think would struggle with college. But if you scratched beneath the surface of the good grades, you’d see a student who put in very little effort beyond generally paying attention in class and completing homework assignments.
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.