Saturday, November 12, 2016

Rocky Mountain Datacon

I spent the previous two days at the first Rocky Mountain Datacon. I haven't yet figured out how to blog during a conference (I have two half-finished posts and a number of ideas), but it was a great experience and I learned a ton. All the talks were filmed, and it was successful enough that the organizers expect to do it again next year.

For the moment, I thought I would share a few thoughts of what I learned at the conference. Feel free to hit me up if you are interested in a discussion about any of them since that will help my eventual posts be more useful and articulate for everyone.

  • Data has allegories to oil, currency, intellectual property, and inventory.
  • Data is a tax-free asset (though it does cost money to keep it and use it).
  • With the current technology and tools, we have distinct classes of big, medium, and small data. Accurately assessing what you have and will have in the future is important for picking the right technology stack.
  • I picked up a lot of data science 101 including what all the titles should mean, what a technology stack is, how to pronounce the word "munging," what the technology options right now look like, how to "break into the field," and a ton of other things.
  • And for the OR folks reading, very little of any of this is using optimization yet. Several people threw around "5 years" as the timeframe to get there, so it seems to be a pretty good time for us to join the data science world.