Previously in this series: The “lost boarding pass” puzzle The “deadly board game” puzzle I’ve recently been enjoying The Riddler: Fantastic Puzzles from FiveThirtyEight, a wonderful book from 538’s Oliver Roeder. Many of the probab...
I recently came up with the idea for a series of screencasts: I've thought about recording a screencast of an example data analysis in #rstats. I'd do it on a dataset I'm unfamiliar with so that I can show and narrate my live thought process.Any suggestions for interesting datasets to use?— David Robinson (@drob) October 6, 2018 Hadley Wickham had the...
Like a lot of people, I was intrigued by “I Am Part of the Resistance Inside the Trump Administration”, an anonymous New York Times op-ed written by a “senior official in the Trump administration”. And like many data scientists, I was curious about what role text mining could play. Ok NLP people, now’s your chance to shine. Just spitballing here...
A very useful concept in software engineering is technical debt. Technical debt occurs when engineers choose a quick but suboptimal solution to a problem, or don’t spend time to build sustainable infrastructure. Maybe they’re using an approach that doesn’t scale well as the team and codebase expand (such as hardcoding “magic numbers”), or using a tool for reasons of convenience...
In January, I was excited to make an announcement about a shift in my career: I have some exciting news: today I'm joining @DataCamp as their Chief Data Scientist 🎉📊📈 pic.twitter.com/wiN9J4qSjx— David Robinson (@drob) January 29, 2018 When I first discussed the role with the DataCamp CEO, I described my goal as to “Make DataCamp as good at doing data science...
My new office introduced me to a betting game I wasn’t previously familiar with: Super Bowl squares. It’s played with a ten-by-ten grid, like this one from printyourbrackets.com: Each row and column gets an assortment of digits from 0-9 represen...
In a recent post, I offered a definition of the distinction between data science and machine learning: that data science is focused on extracting insights, while machine learning is interested in making predictions. I also noted that the two fields greatly overlap: I use both machine learning and data science in my work: I might fit a model...
When I introduce myself as a data scientist, I often get questions like “What’s the difference between that and machine learning?” or “Does that mean you work on artificial intelligence?” I’ve responded enough times that my answer easily qualifies for my “rule of three”: When you’ve written the same code 3 times, write a functionWhen you’ve given the same in-person...
Last week I shared a thought on Twitter: When you’ve written the same code 3 times, write a functionWhen you’ve given the same in-person advice 3 times, write a blog post— David Robinson (@drob) November 9, 2017 Ironically, this tweet hints at a piece of advice I’ve given at least 3 dozen times, but haven’t yet written a post about. I’ve...
For the last few years I’ve been encouraging a particular approach to R education, particularly teaching the dplyr and ggplot2 packages first and introducing real datasets early on. This week I’m excited to announce the next step: the release of Introduction to the Tidyverse, my new interactive course on the DataCamp platform. The course is an introduction to the dplyr...
