Burt Rutan Quotes

Data, I think, is one of the most powerful mechanisms for telling stories. I take a huge pile of data and I try to get it to tell stories.

Information is expanding daily. How to get it out visually is important.

There is a reasonable concern that posting raw data can be misleading for those who are not trained in its use and who do not have the broader perspective within which to place a particular piece of data that is raw.

There's something that happens with the collection of a large amount of data when it's dumped into an Excel spreadsheet or put into a pie chart. You run the risk of completely missing what it's about.

There's a lot of power in executing data - generating data and executing data.

We have entered an era where the kinds of data and analyses that people run have gotten incredibly complicated. So you have researchers using sophisticated software programs that they probably don't understand but are generally accepted and everyone [in the field] uses.

It is easy to lie with statistics. It is hard to tell the truth without it.

Data will always bear the marks of its history. That is human history held in those data sets.

My job is to analyze our data set to understand it and build products on it. I look at raw data, do the math to clean it up, and build systems to make it easy to understand.

If we just focus on stats, we're gonna eventually not have them.

A lot of people seem to think that data science is just a process of adding up a bunch of data and looking at the results, but that's actually not at all what the process is.

Numbers can't speak for themselves, and data sets - no matter their scale - are still objects of human design.

Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations.

If the statistics are boring, you've got the wrong numbers.

Statistics are used much like a drunk uses a lamppost: for support, not illumination.

On average, people should be more skeptical when they see numbers. They should be more willing to play around with the data themselves.

Go out and collect data and, instead of having the answer, just look at the data and see if the data tells you anything. When we're allowed to do this with companies, it's almost magical.

Rob Engle and I are concerned with extracting useful implications from economic data, and so the properties of the data are of particular importance.

Good data is hard to find. Hard data is good to find.

They were cherry-picking the information that we provided to use whatever pieces of it that fit their overall interpretation. Worse than that, they were dropping qualifiers and distorting some of the information that we provided to make it seem even more alarmist and dangerous than the information that we were giving them.

Collect It All Process It All Exoloit It All Partner It All Snff It All Know It All

The job of the data scientist is to ask the right questions.

If we start to use social media data sets to take the pulse of a nation or understand a crisis - or actually use it to deploy resources - we are getting a skewed picture of what is happening.

People believe the best way to learn from the data is to have a hypothesis and then go check it, but the data is so complex that someone who is working with a data set will not know the most significant things to ask. That's a huge problem.

Our data has been harvested, collected, modeled, and monetized - sometimes sold on as raw data, and sometimes licensed just for advertisers to be able to target us.