If you treat the work as nothing but an obligation, you will soon be overwhelmed by competition that sees it as a privilege and a calling.
Jumping right into it where the rhyme isn’t fitting, that’s when the melody doesn’t work.
The basic functions of most interactive visualization tools have changed little since 1996, when Ben Shneiderman of the University of Maryland first proposed a “Visual Information-Seeking Mantra”: overview first, zoom and filter, then details-on-demand.
These new tools imply a new paradigm in which no data scientist is involved, but everyone else in the company is: business execs set a vision, managers define specs for integrating predictions, software engineers work on implementation. This requires that everyone knows a bit about machine learning, but that can be rapidly learned even by non-technical types once you skip algorithms and theory to only focus on studying the core concepts, intuitions and possibilities of machine learning, and some key examples.
Digital natives use a cloud-connected baby monitor. Data natives expect their baby monitor to automatically calculate crying percentiles based on millions of other babies.
In general, creativity seems to come when insight is combined with the hard work of analytical processing.
There are two schools of thought about metrics, he said. You can optimize everything, or you can do what the ancients did and say, “Good enough.”
“Good enough is vastly more rigorous than any metric,” he said, “and it’s more humane, too. Once you reduce a human to a metric, you kill them.”
When I need to find a particular data set, it’s often as straightforward as a search for the topic with the word “shapefile” or “gis” attached. There’s so much data just sitting on servers that if you can imagine it, it’s probably out there somewhere (often for free).
…the average phone user unlocks their phone 110 times a day and at the highest levels, 900 times a day