WEBVTT 00:00:00.000 --> 00:00:01.486 Hi, I'm Katherine, 00:00:01.509 --> 00:00:03.549 I'm a data scientist at Codecademy. 00:00:07.666 --> 00:00:11.033 My job at Codecademy is to analyze our learners 00:00:11.057 --> 00:00:13.245 and see what courses people are taking. 00:00:13.256 --> 00:00:17.504 I work with marketing, engineering, product, curriculum 00:00:17.514 --> 00:00:20.252 to analyze the data that we have. 00:00:22.539 --> 00:00:25.379 Data science is defined as sort of the intersection 00:00:25.390 --> 00:00:29.256 between statistics, software engineering, 00:00:29.268 --> 00:00:32.220 and domain or business knowledge. 00:00:32.230 --> 00:00:34.560 So you have to have a little bit of coding skills, 00:00:34.560 --> 00:00:36.348 a little bit of statistics skills, 00:00:36.358 --> 00:00:38.746 and a little bit of knowledge about your business. 00:00:39.195 --> 00:00:40.788 And I think the site, 00:00:40.809 --> 00:00:45.618 the piece of that that's actually most critical is the business side of it, 00:00:45.641 --> 00:00:48.988 where in the organization do you fit into that I think going forward. 00:00:49.038 --> 00:00:51.500 A lot of companies are trying to figure out 00:00:51.572 --> 00:00:54.824 how to best integrate data people into their organization 00:00:54.835 --> 00:00:58.616 so that they still have a say over strategy and decision-making, 00:00:58.627 --> 00:01:01.915 and sort of the implementation of the analysis that they come up with. 00:01:01.949 --> 00:01:07.699 We haven't honed in on like how data science or data analysis as a field 00:01:07.747 --> 00:01:10.062 fits into different organizations. 00:01:10.084 --> 00:01:13.569 For a lot of companies is the first time they're building a data team. 00:01:13.579 --> 00:01:16.743 I think traditionally in the past when there have been two teams, 00:01:16.753 --> 00:01:21.527 It's kind of sit separately from like engineering and software side of it, 00:01:21.539 --> 00:01:23.082 and now that's more integrated 00:01:23.092 --> 00:01:25.834 and it just looks very different from teams in the past. 00:01:28.706 --> 00:01:32.244 I think the data science community is really collaborative. 00:01:32.666 --> 00:01:36.490 I would say that every time I go to like a conference or meet up 00:01:36.506 --> 00:01:39.759 I'm always humbled by how much more I have to learn. 00:01:39.759 --> 00:01:42.164 And I think originally when I broke into the field 00:01:42.164 --> 00:01:43.944 I felt really overwhelmed 00:01:43.959 --> 00:01:46.288 and I felt a lot of like imposter syndrome 00:01:46.303 --> 00:01:48.991 about having to learn a lot 00:01:49.001 --> 00:01:52.159 like half the time I was just like -- I don't know what's going on, 00:01:52.171 --> 00:01:53.724 but then I realized actually, 00:01:53.746 --> 00:01:56.462 when you work in data analysis or statistics 00:01:56.480 --> 00:01:58.970 you end up specializing in one part of it. 00:01:58.980 --> 00:02:01.822 So you might specialize in predictive analysis, 00:02:01.836 --> 00:02:03.725 you might specialize in reporting, 00:02:03.747 --> 00:02:07.445 you might specialize in machine learning or artificial intelligence 00:02:07.478 --> 00:02:09.602 and there is so many subsets. 00:02:09.633 --> 00:02:13.277 Usually, data scientists, or statisticians, or data analysts 00:02:13.291 --> 00:02:17.250 will focus on one thing and get really good at it 00:02:17.270 --> 00:02:20.109 and that will be sort of the core of their work. 00:02:20.125 --> 00:02:22.500 You meet a lot of interesting people along the way 00:02:22.511 --> 00:02:26.747 who just know like really random things about random subjects. 00:02:26.759 --> 00:02:30.366 Like a lot of the people who I meet at conferences and meetups 00:02:30.366 --> 00:02:32.286 have become my friends. 00:02:32.286 --> 00:02:34.777 And it's really interesting, cause between each other 00:02:34.777 --> 00:02:36.631 we think we do really different things, 00:02:36.645 --> 00:02:40.245 but from the outside looking in, someone who doesn't work in the data world 00:02:40.262 --> 00:02:42.537 probably thinks we're all doing the same thing.