What is it like learning Python if you know R? It’s like being every single character in this scene all at once, all the time:
Which is to say that the last two months have been some of the most frustrating months of my learning life - and I’m including 3.5 years in an Immunology & Infectious Disease PhD program in that calculation.
Sure I’m a little frustrated with Python, and I’m kind of frustrated with the fields of machine learning (and deep learning and data science and AI), but nothing compares with how frustrated I am with how slowly I’m learning.
A note on COVID-19 We are currently in the midst of a global pandemic, and it feels disingenuous for me not to acknowledge the context in which this learning series is being created. When faced with overwhelming circumstances, one of my go-to coping mechanisms is to spend as much time as possible learning. Burying oneself in learning doesn’t have to be your coping mechanism, and for many people I suspect that it is not.
Have you ever played Civ? You start the game by choosing your historical figure, set up your gameplay, and are suddenly dropped in the middle of nowhere. Well, you’re somewhere - but everything on the map is obscured, completely shrouded in an impenetrable fog. Which in a lot of ways is what it feels like when you decide that this is it. This time you’re going to learn Data Science//Machine Learning//AI//Deep Learning//the “it” thing of the moment.
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 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!
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.
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.
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.
The first iteration of the R4DS Online Learning Community was created as an online space for learners and mentors to gather and work throughthe “R for Data Science” text in a collaborative and supportive environment. The creation of this group was inspired by my own success in transitioning to a career in data science coupled with the resources that I wanted to see in the R programming space. This talk will go through the learnings of creating an online learning space focused on R programming for data science, and how futureiterations of similar groups can more proactively center on bringing about diversity, equity, and inclusion to data science spaces.