@bearloga 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 :-) — Raya راية (@rayasharbain) March 12, 2018 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.
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.
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.
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? @RallidaeRule — Andrew MacDonald 🌈 (@polesasunder) January 10, 2017 Explaining the differences would be too much for Twitter, so I’m just gonna give a quick explanation here. 2020-05-18 update: Coming from a background of statistical inference in the context of academia and research using R, where these have been the prevalent PPLs for quite some time, I admittedly have a bit of a blind spot for PyMC3.
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.