10 Ways to Manage a Data Science Project – Part II: Agile
Agile has taken the software community by storm with Scrum and Kanban leading the way. What are these approaches and do they fit in the world of data science?
Read moreAgile has taken the software community by storm with Scrum and Kanban leading the way. What are these approaches and do they fit in the world of data science?
Read moreHow 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 moreIt is ironic that data science, a field built upon rigorous scientific methodologies, has been slow to adopt much rigor from project management approaches. Rather, in data science, project management tends to be ad hoc and is often on the back-burner as a secondary consideration to technologies and algorithms. Fortunately, […]
Read moreVarious process models and frameworks such as CRISP-DM, TDSP, Domino Data Labs Life Cycle, or Data Driven Scrum describe how to execute a data science project. While useful, such models do not explicitly explain how to communicate with stakeholders on what they care most about: what deliverables will they get […]
Read moreWhile the potential ethical issues that might arise when using data science and artificial intelligence have certainly been in the popular press recently, there is not been as much discussion with respect to how a data science team should incorporate ethics within a project. Furthermore, despite this recent publicity, via […]
Read moreIronically, data science teams that are so intensely focused on model measurement often don’t measure their own project performance which is problematic because… …But wait! Data scientists measure all sorts of metrics. Of course, they will closely monitor data science metrics and KPIs such as RMSE, F1 scores, or correlation […]
Read moreData science projects are challenging. To increase your odds of success, start them by asking several key data science project questions. As Ben Franklin said, “an ounce of preparation saves a pound of headaches in your data science projects”.* To help you prepare for a project and evaluate whether to […]
Read moread 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 moreData science projects are unique. It’s time to start managing them as such.
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