R4DS May Challenge: sign up for office hours!

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!

R4DS April Challenge: time for some spring cleaning!

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.

Data Science in Education Textbook Collaboration

A text designed to cover both the use of data science in education as well as data science education

Dripping Data and Measuring Minds

A four day summer workshop for HS-aged Girl Scouts focused on learning and applying the principles of data science to water usage at the GSNETX STEM Center of Excellence

R4DS Online Learning Community

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

So you’ve been asked to make a reprex

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!

Data Science with R: how do I start?

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!

R4DS: the next iteration

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.

Join the “R for Data Science” online learning community

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.

Want to work with Data? Don’t wait.

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.