Critically assess your biases. Publish this analysis biases alongside your data insights. Consider who might want to opt out of a given data application. Strive to empathize with users that resist features and aim to understand why. ...the Tao is found where we would least expect it--not in the strong but in the weak; not … Continue reading Dao De Data, Chapter 1
In chapter 2 of the book How People Learn, the authors outline six key principles that differentiate experts from novices. Studying experts will not inherently make you an expert in your field, but it can help you frame self-improvement in ways that make your efforts more meaningful. Though these six principles must be integrated and … Continue reading Adaptive Expertise
Oops! I managed to lock the @CLEPyLadies twitter account last week and am still waiting on the humans to fix this. I'm also still wading my way through html files, getting the website in a workable state. That hasn't been my singular focus, so it's not ready to deploy. HOWEVER... Hoorays! Our live Meetup group … Continue reading CLEPyLadies: Baby Steps
Tech Ladies, PyLadies, RLadies, DjangoGirls, BlackGirlsCode, WomenWhoCode, Women in Tech... It has been bugging me lately that there are no active women in tech groups near me. Girls Who Code seems to have a presence here, but the local chapter of Women Who Code has had no activity in almost a year. The closest consistently … Continue reading Cleveland, Where My Ladies At?
Balance is a tricky thing to navigate. Documenting my off-time development is important to me, but I get so into the actual learning and production in the brief time I have before work that I end up pushing multiple commits in the 5am-6:30am range at the expense of personal reflection in public. Aka I have … Continue reading My Morning Commits