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.
The ‘R for Data Science’ online learning community is a dynamic and supportive environment that brings together mentors and learners as they undertake self-paced learning via the ‘R for Data Science’ text
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!
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!
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.
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.
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.