Resources for learning to visualize data with R/ggplot2

I’m currently learning visualisation with R/ggplot2 and was wondering whether you could share tips/links/videos/books/resources that helped you in your journey :-)

Sure! Here ya go:

Tips

The only tip I’ll give is that you should strive to make every chart look exactly how you want it to look and say exactly what you want it to say. You will learn in the process of doing. When it’s time to visualize the data and you have an idea for a very specific look and story, don’t give it up or compromise on your vision just because you don’t know how to do it. Trust me, there is so much documentation out there and so many posts on Stack Overflow that you will be able to figure it out. (But also it’s totally fine to get 90-95% of the way there and call it done if that last 5-10% sprint is driving you bonkers.)

The journey so far…

I recently received an email which said, “I’m interested in learning more about you and your journey to where you are today,” so I thought I’d describe how I went from studying visual arts to analyzing data at Wikimedia Foundation (WMF).

Growing up I excelled in visual arts and mathematics at school, and they continued to be my strongest subjects. My parents and I immigrated to US from Russia when I was 10, and I spent the first few years focused on learning English – which was especially difficult because I was the only Russian-speaking person at my school. I was okay at English when I entered 6th grade, having learned a lot of it from The Simpsons of all things. That was also the year I joined band and started learning trombone, but that wouldn’t last.

Advice for graduates applying for data science jobs

2019-08-01 update

Things were a little different when I wrote this in 2017. These days I constantly see new/junior data scientists get rejected because they don’t have the experience. Even those who have an impressive portfolio of projects to show off that they have the technical know-how get thumbs down. I firmly believe this is a failure of employers, not the new generation of recently graduated data scientists entering the field.

Probabilistic programming languages for statistical inference

Introduction

This post was inspired by a question about JAGS vs BUGS vs Stan:

right, that’s what got me confused! so they.. do the same thing?

— Andrew MacDonald, January 10, 2017

Explaining the differences would be too much for Twitter, so I’m just gonna give a quick explanation here.

Mostly-free resources for learning data science

In the past year or two I’ve had several friends approach me about learning statistics because their employer/organization was moving toward a more data-driven approach to decision making. (This brought me a lot of joy.) I firmly believe you don’t actually need a fancy degree and tens of thousands of dollars in tuition debt to be able to engage with data, glean insights, and make inferences from it. And now, thanks to many wonderful statisticians on the Internet, there is now a plethora of freely accessible resources that enable curious minds to learn the art and science of statistics.