Quotes from Hilary Mason

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 believe technology should give us superpowers. Everyone should have the opportunity to learn to think, analyze, and create with technology.

It's a huge competitive advantage to see in real time what's happening with your data.

Creating things that people want to tie into their identity is the best way to make them spread.

In some ways, chocolate chip cookie recipes are my favorite algorithms. You put a bunch of bad-for-you stuff in a bowl and get a delicious result.

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

Data is a tool for enhancing intuition.

My Grinnell education didn't prepare me directly for the work that I'm doing, but it did give me the tools that I needed to focus my curiosity.

Even smaller companies are putting resources behind their analytics teams in the same way they put resources behind engineering and product teams. There are some great tools out there that allow even tiny businesses to use data effectively.

Science is the practice of failing repeatedly but learning as you go.

I joined bit.ly as chief scientist in October of 2009. The company is a URL-shortener and content-sharing platform; we provide tools for people to share and track links on the Internet.

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

Technology is giving companies superpowers to compete more intelligently and capture the data behind changing trends, expanding markets, and new opportunities.

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.

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

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.

It turns out it's important to build a product and not just a bunch of data models.

Deep learning allows you to create predictive models at a level of quality and sophistication that was previously out of reach. And so deep learning also enhances the product function of data science because it can generate new product opportunities.

I think e-mail should be sorted by importance, not by time.

Value experiences by how much you're learning, and if you're aren't learning, move on.

I wish I'd known more about how to build a startup when I was younger.

Cool innovation might happen in startups, but they often lack the resources or the deep expertise in the problems they want to address.

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.

When you start at Bitly, you go through this emotional cycle, where first you go, 'Oh my God, this data is amazing.' But then you start looking at it, and you conclude that humanity is completely doomed. Because what people read is cats and Bieber and celebrity gossip and that stuff.