Aaron Koblin Quotes

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.

I'm an analytics girl.

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

Big Data is just that - big. But, it's a term that is largely misunderstood and difficult to explain.

The people who are thinking most about big data right now are corporations and governments.

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.

If you have a lot of data and you want to create value from that data, one of the things you might consider is building up an AI team.

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.

I'm going to say something rather controversial. Big data, as people understand it today, is just a bigger version of small data. Fundamentally, what we're doing with data has not changed; there's just more of it.

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

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.

The premise and promise of Big Data is that there are no stories, only patterns; that the human preference for story is aligned with the human tendency for error; and that only through dislocations in scale - the scale of sample size and of time - will truth emerge.

I think of 'data science' as a flag that was planted at the intersection of several different disciplines that have not always existed in the same place. Statistics, computer science, domain expertise, and what I usually call 'hacking,' though I don't mean the 'evil' kind of hacking.

I keep saying the sexy job in the next ten years will be statisticians. People think I'm joking, but who would've guessed that computer engineers would've been the sexy job of the 1990s?

Big data is mostly about taking numbers and using those numbers to make predictions about the future. The bigger the data set you have, the more accurate the predictions about the future will be.

Every part of your business will change based on what I consider predictive analytics of the future.

There's definitely a huge opportunity for businesses to transform their operations and decision making by using data.

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.

Most of 'big data' is a fraud because it is really 'dumb data.'

The possibilities for creation and insight are endless. We're constantly collecting more data, and it's starting to be very relevant to our lives.

People are saying, 'Big Data is the new oil.'

There will always be plenty of things to compute in the detailed affairs of millions of people doing complicated things.

The whole concept of data science is that the software becomes the expert, and you, as the average user, are able to understand what's going on.

The answers to today's most important scientific, business, and social problems lie in data.

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