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  • Training for Individuals
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Data Science Process Alliance

Tag: stakeholder management

August 28, 2021Last Updated: May 2, 2022Project ManagementBy Nick Hotz

The Data Science Project Checklist

What do flight attendants, surgical teams, and successful data science project managers have in common? They all use a checklist. Why? Although every surgery, project, and flight is different, each has a repeatable pattern. And checklists can help remind us of each step and major consideration. Don’t reinvent the wheel. […]

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Example Data Science Project Roadmap
December 27, 2019Last Updated: May 2, 2022Life Cycle, Project ManagementBy Nick Hotz

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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Measuring Data Science Project Performance
August 24, 2019Last Updated: June 6, 2022Project ManagementBy Nick Hotz

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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Questions to ask for Data Science Projects
May 20, 2019Last Updated: May 2, 2022Project ManagementBy Nick Hotz

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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Agile Gazelle
April 21, 2019Last Updated: August 1, 2022AgileBy Nick Hotz

Is Agile a Fit for Data Science?

As explained in the previous post, much of the debate on agile’s potential fit for data science focuses on the use of a specific framework (such as Scrum), and the associated processes and artifacts such as story pointing, burn down charts, or sprint lengths. Unfortunately, this drowns the argument into […]

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