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

Agile Data Science With Data Driven Scrum

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Data Driven Scrum

The Need for a New Agile Framework When teams try to use an agile framework for data science, they often try to use Scrum, Kanban. Below I review the key challenges teams have in leveraging these frameworks have challenges. I also briefly explore a TDSP, which is newer framework already discussed on this web site. […]

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 […]

Kanban

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managing data science with kanban

What is Kanban? Kanban, which literally means billboard in Japanese, started as a supply chain and inventory control system for Toyota manufacturing in the 1940s to minimize work in progress and to match the supply of automotive parts with demand. Kanban Popularity Other industries including software have since adopted Kanban. It is becoming more popular […]

Scrum and Data Science

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managing data science with scrum

Scrum is an agile collaboration framework that can help teams increase the agility of their data science projects. Co-founded by Jeff Sutherland and Ken Schwaber in the 1990s, Scrum has become the most commonly used agile approach with over 12 million practitioners (scrum.org). Although heavily adopted in software, Scrum is also used across a wide variety […]

Agile Data Science

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There are three key concepts that should be followed within an agile data science effort – use iterations, keep the iteration as small as possible and get feedback on each iteration. In other words, while there are several alternative data science workflow frameworks (sometimes known as data science life cycle frameworks), to achieve agility, agile teams should execute an […]