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Experienced statisticians — the least sexy of titles given to people who explore data — are quick to inform the eager apprentice that most of their time will be spent finding, cleaning, and preparing data. The analysis part — that is, the part that feels the most like panning for gold — is a very small fraction of the job.
An insidious assumption exists, promoted by software vendors, that knowing how to use a particular data analysis software product “auto-magically” imbues one with the skills of a data analyst. Even with good software—something that’s rare—this is far from true. Just as with any area of expertise, data analysis requires training and practice, practice, practice.
… it’s not about having the data, but about the ideas and computational follow-through needed to make use of it …
When’s the last time a city did something so exciting that people from every walk of life and every part of town were talking about it? That’s the reaction Google Fiber sparked in Kansas City, and now the excitement — and electrical current of fiber-to-the-home connections — will reach Austin, Texas.
It’s a good question.
Affordable housing. That’s not a very sexy answer, but it’s true. If we had universal healthcare and affordable housing, people could be more creative, sleep better at night, and live longer.
If you have any Denton tips or food recommendations, hit me up on twitter: @austinkleon
Any data scientist worth their salary will tell you that you should start with a question, NOT the data. Unfortunately, data hackathons often lack clear problem definitions. Most companies think that if you can just get hackers, pizza, and data together in a room, magic will happen. This is the same as if Habitat for Humanity gathered its volunteers around a pile of wood and said, “Have at it!” By the end of the day you’d be left with a half of a sunroom with 14 outlets in it.