The Boston Dynamics humanoid robot tripping over a curtain and tumbling off stage? A prediction model that tips a teen’s parents off to her pregnancy? A music recommendation engine that suggests Coldplay? Yup. There are just too many big data, data science, and data analytics failure examples to cover in […]
Read more
During the past few months, we conducted a poll to see what project management framework teams used to help execute their data science projects. Based on our survey of 109 respondents, nearly half of the respondents most commonly use CRISP-DM. This was followed by Scrum, Kanban and “My Own”. See results below. […]
Read more
How do you manage data science projects? Is it software? Is it research? Or maybe, simply magic? This four-part post is an overview 10 ways projects are or could be managed. To start, we’ll explore ad hoc project management, waterfall, and CRISP-DM.
Read more
ad hoc (adv) – “for the particular end or case at hand without consideration of wider application” –Merriam Webster Dictionary High Reliance on Ad Hoc Processes Without established methodologies for managing data science projects, teams often resort to ad hoc practices that are not repeatable, sustainable, or organized. Such teams […]
Read more
Data science projects are unique. It’s time to start managing them as such.
Get the jumpstart guide to better manage your next project.