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.
When in Doubt My summer started with a bang:
The end of a long-term relationship A surprisingly difficult - but completely treatable - medical diagnosis The illness and eventual passing of a beloved family member Each of these items paced themselves perfectly and provided an ongoing (and most welcome!) sense of relief that beautifully masked my growing disinterest and discontent with my place in the R community. With each of these big ordeals happening in rapid succession, I didn’t have time to feel guilty about the ever-present sense of apathy for projects that once were my reason for getting out of bed in the morning.
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 This past March I had the distinct pleasure of participating in a panel about making the career transition to data science as part of Kaggle’s CareerCon 2018. As a result of this experience, I’ve gotten enough emails asking for more information about my data science journey that it warrants a blog post, per David Robinson’s advice:
When you’ve written the same code 3 times, write a function
When you’ve given the same in-person advice 3 times, write a blog post