Making data scientists more productive

 

NEW YORK--How do you make data scientists more productive? Jeremy Achin has an answer for you.

 

http://www.meetup.com/NYC-Data-Business-Meetup/events/226086620/

 

The current path to becoming a data scientist is based on learning statistics, programming and algorithms, then applying practical knowledge and practicing real world experience which can unfortunately take up a lot of time.

 

The better way, he insists, is automated using modern tools and computational power where you can go dive right away into your practical knowledge and real world experience and then just add if you want, statistics, programming and algorithms later.

Achin was talking about his company DataRobot, which he said offers predictive analytics fast. By fast, Achin believes DataRobot can cut down time it takes for a data scientist to solve a problem in hours rather than months.

 

“”People take months to manually build regression models. There are technique-agnostic ways to assess and interpret predictive models,” he said.

 

Achin spoke with other presenters Josh Bloom of Wise.io, Alexi Le-Quoc, founder of Datadog and Haile Owusu, chief data scientist of Mashable at Data-Driven’s monthly meetup last November 16 at Bloomberg.

 

Where DataRobot is about speed, Wise is about easy--making machine learning easy focused on letting users build and deploy models for predicting customer behavior. It’s interesting to note here that the founders were from University of California, Berkeley, astrophysics professors and researchers who have worked together for over a decade.

Today, it is pushing the limits of cutting-edge machine learning technology for customer success. It offers a host of intelligence routing/triage, response recommendation, auto response, knowledge-base deflection and many more.

In its presentation, Datadog showed how its cloud service helps customers monitor infrastructure and software.  

 

Datadog gathers performance metrics from your application comp; it visualizes and pull in data real time, and alerts because your understand is only as good as your monitoring.

 

Founded 2010, the company raised $31 million primarily from Index Ventures early this year.