16 April 2019
Managing Data Science projects is notoriously difficult. Going into a project it can be difficult or impossible to foresee data quality issues, which models will work well, and how much effort is necessary to ship a data product.
Leaning on a few years of leading data science engagements at startups and large companies, I've picked up a few tricks to keep data science projects on-time and meeting mission critical milestones, all while keeping data scientists happy. We'll touch on how data science projects differ from most software engineering projects, why its important to spend a bit more time planning them, and how to make sure your projects are a success.