Hilary Mason

Actress

102 Quotes

Companies that rely on licensing a proprietary dataset should expect to be outpaced by competitors using modern data collection techniques and more frequent updates and greater accuracy.

Companies that rely on licensing a proprietary dataset should expect to be outpaced by competitors using modern data collection techniques and more frequent updates and greater accuracy.

To gain the competitive edge, companies must master the ability to innovate.

The sender and subject line are actually the most important parts of an e-mail because people tend to put more important information in the subject.

A good scientist can understand the current state of a field, pick interesting questions where a success will actually lead to useful new knowledge, and push that field further through their work.

Data science requires having that cultural space to experiment and work on things that might fail.

Often, people think that individual data is the most valuable thing they can collect. But it's not useful to know what I am doing or where I am, unless you're particularly interested in me, which is weird. But it is very useful to know what a population of people are doing.

If you find something obscure fascinating, learn as much about it as you can, because there's a good chance it won't be obscure for long.

As your competitors learn more, you'll need to learn, too.

I don't actually, as a general policy, block any sort of cookies. I keep them all turned on, and that's because I'm willing to make the tradeoff that I let companies gather this information about me in return for a better experience.

As your competitors learn more, you'll need to learn, too.

Everyone likes taking their own photos and seeing themselves reflected back.

I'm a huge fan of the liberal arts approach of teaching you to think, analyze, and communicate, then sending you out into the world to cause trouble.

Nobody really cares about short links, but people do care about saving and sharing content.

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

I decided that since I was trying to teach 'style' of thinking in science and engineering, and 'style' is an art, I should therefore copy the methods of teaching used for the other arts - once the fundamentals have been learned.

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.

It's rare that you can solve a technology problem with more technology.

A good scientist can understand the current state of a field, pick interesting questions where a success will actually lead to useful new knowledge, and push that field further through their work.

I think we need more ambition about using our data to make our lives better.

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