Better collaborative data science

Two guiding insights learned from Kaggle’s community

Last Saturday, I gave a keynote at Data Con LA 2018 (formerly Big Data Day LA), the largest data conference of its kind in Southern California. This is my talk in blog format.

In my keynote, I shared two things Kaggle’s team has learned about what enables data scientists to collaborate more productively on projects. These two insights have shaped how our community has grown to become the world’s largest online community of data scientists over the past eight years.

The importance of access to socially validated knowledge The importance of making data projects easily reproducible

Intro to Kaggle

First, in case you haven’t heard of Kaggle, I’ll provide you with a little bit of background. Today, over 2 million Kagglers come to the platform to do data science projects. They participate in machine learning competitions and publish and explore data on our Datasets platform. For both competitions and our datasets platform, users can analyze the data in R or Python and share their work with our free hosted notebook environment, Kernels.