Jennifer Egan Quotes

If you like overheads, you'll love PowerPoint.

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.

There is a rampant tendency in any industry where someone is trying to sell something with a bunch of data, where they cherry pick a little bit... bias a little bit. This becomes quite easy when there is an enormous amount of data to cherry pick from.

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.

Beautiful Evidence is about the theory and practice of analytical design.

Evolving technologies that allow economists to gather new types of data and to manipulate millions of data points are just one factor among several that are likely to transform the field in coming years.

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.

I think you can have a ridiculously enormous and complex data set, but if you have the right tools and methodology then it's not a problem.

The Government are very keen on amassing statistics - they collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams.

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

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

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.

Making more sense out of my data, my needs, my tasks - to me, that's the future of Office.

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.

There's a very low amount of data literacy around people.

Data science is the combination of analytics and the development of new algorithms.

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.

There's a tendency in graphics to allow the trimming of certain parts. But I think that if you're open about your process, your methodology, such as introducing thresholds, introducing filters, techniques people use in research and data management, it's legitimate. It's legitimate to say, "We're only going to show data above this level, or between levels."

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

Then: I google "time-series visualization" and start work on a new version of my model, thinking that maybe I can impress her with a prototype. I am really into the kind of girl you can impress with a prototype.

The company I invested in is probably a leader in that area. They're a company called Second Spectrum, which happens to be based in LA but was started by two USC computer-science professors. It's filled with guys who love sports, who played sports, but really look like programmers.