Shower thoughts on data science. Not really a genre, until it is.
3 inequalities for good code 👌
Guidelines for good code, that often hold true.
Either one or everyone: Two paths for accountability in data science 👨🏼⚖️
Professionals need to hold each other accountable. Especially data scientists. If there is nobody who can judge you work, what keeps you from cheating / slacking / lying? There are two paths you can take. A hard one and a scary one.
There is no shortcut: Data scientists need hard training 🏋️
Every data scientist needs rigorous training in mathematics, statistics, and programming.
Are you a data scientists? You need a blog. 👨🏼💻
Be the glue guy on your research team 🏀
As data scientists, allegedly the Sexiest Job in the 21st century, it is easy to view ourselves as the superstars of our research team. In fact, we are the glue guys, the people that do humble work and make everyone else better by doing so.
No such thing as coder's block 🧱
There is no such thing as writer's block. Seth Godin has been preaching this for years, to writers and other creatives. It also applies to us, data scientists: There is no such thing as coder's block. Writers see an empty page and are "[afraid] of bad writing". Programmers see an
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