5HAW records my experiences as an old data scientist learning new tricks by committing 5 hours a week to tackling something new (to me) and relevant (to data science), but not necessarily something currently needed for my job.
My background on day one
At the start of this project, I would consider myself a classically-trained statistician. I have a Ph.D. in Statistics, earned back in 2006. My first statistical software in graduate school was S-PLUS, but then I quickly switched to using R, writing scripts in text files or Emacs. I’ve since become an adopter of RStudio and R Markdown for working in R, and have slowly switched to using the tidyverse and ggplot, but I am still most comfortable in base R. I wrote an R package once (in graduate school, now defunct).
Why am I doing this?
It’s easy to become comfortable with the tools you know, unless external circumstances force you to try new things. Even if the newer tool is really a better tool to use. For example, I only started using the tidyverse in order to be able to teach it to my students; if left to my own devices, I would not have done so. But I am a better data analyst now that I have this tool.
What exactly am I doing?
I am committing to investing 5 hours a week for an entire year to improving my data science skills. It may be one hour a day during the work week, or a single five-hour block once during the week. I will have a particular goal in mind for what I want to learn or accomplish each week, but I won’t force myself beyond five hours if I don’t meet that goal. I will use this blog as a record of what I’ve done and a resource when I need to revisit old work.
Why 5 hours?
Five hours a week is roughly one hour per work day. It doesn’t seem daunting to find one hour a day to spend on professional development. But if I make it through the entire year, that will be over 250 hours of professional development! (Note: I’m definitely not the first person who thinks this is a good idea.)
What new tricks do I plan on learning?