Coordination Framework

3 Steps to Define a Data Science Process

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How do you effectively define a data science process? Conceptually, a data science process explains and defines how a team should execute a project. Having a robust, repeatable process helps to ensure that the project efficiently and effectively delivers actionable insight. In this article, I’ll explore how to create a well-defined data science process in […]

Workflow

Data Science Project Roadmap Example

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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 […]

Agile

10 Data Science Project Metrics

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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, they will closely monitor data science metrics and KPIs such as RMSE, F1 scores, or correlation coefficients. Such metrics are critical […]

Agile

Data Driven Agile With Data Driven Scrum

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Data Driven Scrum

When teams try to have a data driven agile approach, they often try to use an existing framework, such as Scrum or Kanban. Yet, there are key challenges teams have in leveraging these frameworks. Therefore, the Data Science Process Alliance created an alternative framework called Data Driven Scrum which is designed with data science in […]

Agile

Vertical vs Horizontal Slicing Data Science Deliverables

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layered cake

Traditional software approaches favor developing software layer-by-layer (horizontal slicing) while software agilists strive to deliver software by thin end-to-end value streams (vertical slicing). …but what makes sense for data science? Consider a churn project… Imagine that you are tasked to pro-actively minimize customer churn at a telecom company. The retention department has requested the following […]

Agile

Is Agile a Fit for Data Science?

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Agile Gazelle

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 details that ignore agile for […]

Agile

5 Agile Data Science Myths

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Agile data science myths

One of the most common questions of data science project management is some variation of: “Is agile a fit for data science?” Unfortunately – like a lot of questions in data science – this question itself is often misunderstood. Many (if not most) blog posts, on-line forums, and conversations debate this question by evaluating specific […]

Team

Lessons from 20 Data Science Teams

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Data science life cycle per Domino Data Labs

What can you learn if you observe data science teams across 20 large companies? I asked Mac Steele, Director of Product at Domino Data Lab, to find out. Mac combined the lessons he learned from observing data science teams with concepts from CRISP-DM and agile to create the Domino Data Science Lifecycle. It is defined in a 25-page whitepaper, The […]