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?
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10 Ways to Manage a Data Science Project – Part I: Traditional Approaches
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.
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The Rise of Data Science Project Management
It 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, […]
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Data Science Project Roadmap Example
Various 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 […]
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10 Data Science Ethics Questions
While 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 […]
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10 Data Science Project Metrics
Ironically, 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 […]
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10 Questions to Ask Before Starting a Data Science Project
Data 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 […]
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Ad Hoc Data Science
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 […]
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