Agile

Data Science vs. Software Engineering

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Data science and software engineering are similar in many ways. However, dig deeper, and you’ll find key differences in their type of work, in the professionals working in these fields, and how they execute their projects. Data Science vs. Software Engineering Fields What is Data science? If you ask five data scientists to define data […]

Emerging

10 Ways to Manage a Data Science Project – Part IV: Emerging Approaches

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So are there new emerging approaches that are data science native? Microsoft’s Team Data Science Process (TDSP), Domino Data Lab’s Data Science Life Cycle, and the Data Science Process Alliance’s Data Driven Scrum (DDS) are approaches that are both data science native and agile. There are pros and cons specific to each approach but they share some fundamental principles.

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The Rise of Data Science Project Management

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Sunrise

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, this is changing as organizations […]

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An Example Data Science Project Roadmap

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Example Data Science Project Roadmap

Various process models and frameworks such as CRISP-DM, TDSP, Domino Data Labs Lifecycle, 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 through a project lifecycle. In pre-project […]

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Centralized vs Decentralized Data Science Teams

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Centralized vs Decentralized Teams

Does a single centralized data science team or several decentralized teams work better? Many organizations struggle between having a single data science “center of excellence” (sometimes known as an “Enterprise” or a “Shared Service” team), which is leveraged across the organization and having smaller teams embedded within different parts of the organization. For a small […]

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10 Ways to Measure Data Science Project Performance

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Measuring Data Science Project Performance

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, data scientists will closely monitor metrics such as RMSE, F1 scores, or correlation coefficients. Such metrics are critical to answer “How […]