Transcript: DBTB INT Ryan Orban r

yes my name is Ryan Orban from galvanized where I serve as the CTL ah I think data by the bay is a great conference we love hosting it here at galvanize a big part of our mission is to help transform lives through education and through community support and are one of our main programs is data science and data engineering instead of being able to support and host a conference like this bring in amazing lumination fueled Alexia's amazing organizer and just getting all of these people activated on our campus and getting our students or companies our members exposed to this level of content is super great so we're happy to support data by the bay we have for such for some time now and we're even talking about potentially having a greater level of collaboration and taking this not only nationally but globally you know it sounds a bit trite but you know key portion of my talk was that really data science and data is the future when we think about the new technologies that are coming out either being an artificial intelligence or deep learning or even machine learning it's really changing the economy and that every company now is a tech company even from you know construction and manufacturing to what you would typically think about as leveraging data science i can move or Facebook every company is having to answer the call in order to be more competitive and to reduce costs and so I think this is one of the most exciting areas to be in right now because we're seeing a tectonic shift not only through things like you know self-driving cars and artificial intelligence but you know we're going to see a pretty big blanchett moment of the next 10 to 15 years the most you know populous job in the country and I was truck driving and you know with this you know this morning they just announced a thirty thousand dollar add on to a semi which makes it well to drive to Costa to host unaided by a particular human and so this is one of the reasons we what we do what we do is providing the access and opportunity to the skills knowledge that you need to not only be working with the AIS but actually programming them and being able to understand and leverage data i think is a big component of that and just having basic data literacy of knowing how this is being used in what dan you're generating the value of that i think is going to be really important because the amount of data is continuously going to grow its growing an exponential rate we're all carrying supercomputers around in our pockets right now and they might have data exhaust that we have is only going to continue to grow and be more more you know i think combined into the very fabric of our lives so yeah I mean I think data really holds the key for a lot of the big technological and kind of industrial revolutions that we're going to see and really bring it at your age yeah we're gonna see some crazy stuff now that we think we know it necessarily what we're going to see right i mean and i think that's the other exciting yet terrifying component is that it's moving faster than it ever has before right we're seeing these massive contractions in the cycle of when these innovations come out and so I'll even just like data science wasn't even a term ten years ago I don't even think we know what the new jobs will be in the next 10 years I think will continuously change and a big part of you know what galvanized tries to do is move the pace of that industry all right and making sure that we're providing the programs and the opportunities and the on ramps for people for those jobs that don't even exist I think like anything particularly data science it all comes down to practice and practical experience and that's a key part of our programs is that you can go and get a masters degree you know n number of universities both you know domestically in worldwide some of them a great program some of it or not but typically they're focusing on the theoretical aspects right they're going to go and walk you through a proof and prove to you that under all network converges but i think the rubber really meets the road is when you apply this stuff in practice right and when does it work when is it not work how do you clean data how do you work with the team i do work with engineering put things into production and so I think that practical experience and knowing when things work and especially when they don't work and being able to have that intuition and being able to understand and identify where potential opportunities in your data are because data science even though it's called data science is as much of an art as it is a science and having that human component of it and that creative passion and how these things are applied I think is really important and so my biggest advice is you know practice makes perfect and you really need to get you know dig into the data you need to be working on interesting problems new problems all the time and really working cross-functionally with your team because i think you know we've seen data science applied a lot to things like online advertising and self driving and you know real key revenue generators in the business but some of the greatest data scientists they came in through data science orthogonal right they didn't study it they were facing some problem at a different domain and they needed to use data science to solve it at large scale and I think we're going to see more and more about acceleration and things like healthcare things like education where typically we haven't seen a lot of data being used or leverage that you know I think that's where the next kind of vanguard of great data scientists are going to come from you