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Hi, I'm Katherine,

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I'm a data scientist at Codecademy.

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My job at Codecademy[br]is to analyze our learners

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and see what courses people are taking.

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I work with marketing,[br]engineering, product, curriculum

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to analyze the data that we have.

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Data science is defined[br]as sort of the intersection

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between statistics, software engineering,

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and domain or business knowledge.

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So you have to have[br]a little bit of coding skills,

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a little bit of statistics skills,

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and a little bit of knowledge[br]about your business.

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And I think the site,

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the piece of that that's actually[br]most critical is the business side of it,

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where in the organization do you fit[br]into that I think going forward.

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A lot of companies[br]are trying to figure out

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how to best integrate data people[br]into their organization

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so that they still have a say over[br]strategy and decision-making,

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and sort of the implementation[br]of the analysis that they come up with.

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We haven't honed in on like how[br]data science or data analysis as a field

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fits into different organizations.

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For a lot of companies is the first time[br]they're building a data team.

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I think traditionally in the past[br]when there have been two teams,

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It's kind of sit separately from like[br]engineering and software side of it,

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and now that's more integrated

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and it just looks very different[br]from teams in the past.

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I think the data science community[br]is really collaborative.

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I would say that every time I go to[br]like a conference or meet up

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I'm always humbled by[br]how much more I have to learn.

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And I think originally[br]when I broke into the field

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I felt really overwhelmed

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and I felt a lot of like imposter syndrome

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about having to learn a lot

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like half the time I was just like --[br]I don't know what's going on,

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but then I realized actually,

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when you work[br]in data analysis or statistics

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you end up specializing in one part of it.

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So you might specialize[br]in predictive analysis,

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you might specialize in reporting,

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you might specialize in machine[br]learning or artificial intelligence

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and there is so many subsets.

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Usually, data scientists,[br]or statisticians, or data analysts

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will focus on one thing[br]and get really good at it

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and that will be sort of[br]the core of their work.

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You meet a lot of interesting[br]people along the way

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who just know like really random[br]things about random subjects.

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Like a lot of the people who I meet[br]at conferences and meetups

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have become my friends.

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And it's really interesting,[br]cause between each other

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we think we do really different things,

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but from the outside looking in,[br]someone who doesn't work in the data world

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probably thinks we're[br]all doing the same thing.