DBTB INT Ruban Phukan

I'm Ruben fukken I'm the co-founder and I'm Ruben fukken I'm the co-founder and the chief product and under desks the chief product and under desks officer of data rpm yeah I think the officer of data rpm yeah I think the biggest thing is that the very very biggest thing is that the very very relevant audience who are focused on relevant audience who are focused on data data science and machine learning data data science and machine learning aspects and that is what we do we help aspects and that is what we do we help automate data science so I think we the automate data science so I think we the talk connected well with the audience talk connected well with the audience here very much and that is what is very here very much and that is what is very exciting I think the kind of problems exciting I think the kind of problems that data can help solve which is that data can help solve which is specifically with using machine learning specifically with using machine learning and automation is this it's insane and and automation is this it's insane and and specifically with new emerging areas and specifically with new emerging areas like machine data and IOT where like machine data and IOT where opportunities are in in trillions of opportunities are in in trillions of dollars right so that is what is most dollars right so that is what is most exciting that data can actually make a exciting that data can actually make a dollar impact that's huge in the dollar impact that's huge in the industry I think the most important thing that I I think the most important thing that I and the message that I wanted to get and the message that I wanted to get across is that the way to scale data across is that the way to scale data science is not by having more people science is not by having more people everyone has to think automation and everyone has to think automation and that is that is essentially the next that is that is essentially the next thing because otherwise currently we thing because otherwise currently we have a gap of almost like 1 million data have a gap of almost like 1 million data scientists that is required in the next scientists that is required in the next couple of years time and that is a skill couple of years time and that is a skill gap that we cannot match right so most gap that we cannot match right so most data scientists the job of the future data scientists the job of the future would be of how can they automate their would be of how can they automate their work as much as possible and helps keep work as much as possible and helps keep themselves yeah I think the most important factor yeah I think the most important factor of becoming a creative the scientist is of becoming a creative the scientist is in understanding not just algorithms or in understanding not just algorithms or the techniques and the math and stats the techniques and the math and stats behind it I mean all of those are behind it I mean all of those are required but it is an understanding how required but it is an understanding how how to solve business problems right how to solve business problems right with data I think that Kinect is is what with data I think that Kinect is is what makes the difference between an average makes the difference between an average data scientist and in created a data scientist and in created a scientific understanding business to scientific understanding business to means and how data can help solve those