Kenny Ning's guest appearances

Kenny Ning

Towards Data Science

Sarah Williams

Ben Goertzel

Jeremy Howard

Roland Memisevic

Max Welling

Ayanna Howard

Mike Tung

Margaret Mitchell

Melanie Mitchell

Gillian Hadfield

Anders Sandberg

Brian Christian

Joel Grus

Jeffrey Ding

Ben Lorica

OwlTail

There’s been a lot of talk about the future direction of data science, and for good reason. The space is finally coming into its own, and as the Wild West phase of the mid-2010s well and truly comes to an end, there’s keen interest among data professionals to stay ahead of the curve, and understand what their jobs are likely to look like 2, 5 and 10 years down the road. And amid all the noise, one trend is clearly emerging, and has already materialized to a significant degree: as more and more of the data science lifecycle is automated or abstracted away, data professionals can afford to spend more time adding value to companies in more strategic ways. One way to do this is to invest your time deepening your subject matter expertise, and mastering the business side of the equation. Another is to double down on technical skills, and focus on owning more and more of the data stack —particularly including productionization and deployment stages. My guest for today’s episode of the Towards Data Science podcast has been down both of these paths, first as a business-focused data scientist at Spotify, where he spent his time defining business metrics and evaluating products, and second as a data engineer at Better.com, where his focus has shifted towards productionization and engineering. During our chat, Kenny shared his insights about the relative merits of each approach, and the future of the field.

There’s been a lot of talk about the future direction of data science, and for good reason. The space is finally coming into its own, and as the Wild West phase of the mid-2010s well and truly comes to an end, there’s keen interest among data professionals to stay ahead of the curve, and understand what their jobs are likely to look like 2, 5 and 10 years down the road. And amid all the noise, one trend is clearly emerging, and has already materialized to a significant degree: as more and more of the data science lifecycle is automated or abstracted away, data professionals can afford to spend more time adding value to companies in more strategic ways. One way to do this is to invest your time deepening your subject matter expertise, and mastering the business side of the equation. Another is to double down on technical skills, and focus on owning more and more of the data stack —particularly including productionization and deployment stages. My guest for today’s episode of the Towards Data Science podcast has been down both of these paths, first as a business-focused data scientist at Spotify, where he spent his time defining business metrics and evaluating products, and second as a data engineer at <a href="http://Better.com" rel="nofollow" target="_blank">Better.com</a>, where his focus has shifted towards productionization and engineering. During our chat, Kenny shared his insights about the relative merits of each approach, and the future of the field.

45. Kenny Ning - Is data science merging with data engineering?