Aaron Koblin Quotes

So this is the only TV show in America where I am quite confident that you, the audience, will share my excitement when I tell you that coming up in our next segment, we have the best graph ever. Best graph ever.

By moving them vertically, a representative mean curve could be formed, and individual events were then characterized by individual logarithmic differences from the standard curve.

The greatest moments are those when you see the result pop up in a graph or in your statistics analysis - that moment you realise you know something no one else does and you get the pleasure of thinking about how to tell them.

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.

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.

I think we are definitely suffering from an information overload, but I believe that there is going to be better and better ways of organizing that information and processing it so that it will enhance your daily life.

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

To learn something from your data, the forming of a hypothesis lies with the human being, which turns into a query, which becomes a result. The problem is that there are too many queries to make, too many questions to ask.

Mankind invented a system to cope with the fact that we are so intrinsically lousy at manipulating numbers. It's called the graph.

Personally, I'm not very keen on the visualization of absolutely everything.

We are going to completely change what it means to do advanced analytics with our data solutions. We have machine-learning stuff that is about really bringing advanced analytics and statistical machine learning into data-science departments everywhere.

By definition, big data cannot yield complicated descriptions of causality. Especially in healthcare. Almost all of our diseases occur in the intersections of systems in the body.

We are approaching a time when human intelligence alone will be incapable of managing a highly advanced society. Existing technologies are rapidly exceeding the human capacity to absorb and process information.

When the world throws you too much information, the only way you can stay sane or survive is to look for pattern recognition. Amidst all the blurs, is there a constellation that emerges, is there a straight line that's emerging?

I learned analytics. It's a point of reference, of information. It has to be a factor in decision making.

Every day we go over data and use science and data to drive policy and decision-making.

The bigger a data set that you have, the more polls, the more surveys that you have that people undertake, the more accurate your models are going to be. That's just a fact of data science.

You cannot be insightful if you're deluged with information.

The big mystery of Big Data is causation versus correlation.

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.

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

I'm kind of fascinated by this idea that we can surround ourselves with information: we can just pile up data after data after data and arm ourselves with facts and yet still not be able to answer the questions that we have.

In disciplines as disparate as baseball, financial services, trucking and retail, people are realizing the power of data to help make better decisions.

You need to use data science and machine learning to get the ground truth of what's happening inside of a company.