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.
We’re so close to March, which for many of you involves a lot of college basketball.
But there’s another exciting challenge happening, once which also happens to involve your participation: R4DS is hosting its first series of community member talks!
The Challenge: Participate! Four of our community members have graciously stepped up to record a 20-30 minute talk on an R4DS-related topic, and they will be developing these over the next few weeks.
It’s February–which most people associate with Valentine’s Day, and that’s fine–but for all of us in the R4DS Online Learning Community, we’re going to focus on winning!
The challenge: share your wins The challenge is short and sweet this month, and the same for both learners and mentors:
Once a week for the month of February, post a “win” that you’ve had in our #wins channel on Slack Engage with community members in the channel–emoji responses or starting a conversation by replying to someone’s win are both great ways to do this!
Happy New Year! I love the start of the New Year for so many reasons, but one of my favorites is the never-ending stream of motivational word art that populates a good 98% of every social media feed for 31 whole days, like so:
Another reason for loving the start of 2018 is that we’re kicking off the next iteration of the R for Data Science Online Learning Community! So if you’re joining us for January, consider participating in the January Challenge.
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.
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.
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.