10 Ways to Manage a Data Science Project – Part II: Agile
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Agile has taken the software community by storm with Scrum and Kanban leading the way. What are these approaches and do the fit in the world of data science?
Category: Agile
What is Agile Data Science?
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Exploring how to “do” agile data science
10 Data Science Project Metrics
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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 […]
Data Driven Agile With Data Driven Scrum
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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, to effectively implement data driven agile projects, the Data Science Process Alliance created an alternative framework called DDS (Data Driven […]
Vertical vs Horizontal Slicing Data Science Deliverables
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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 […]
Is Agile a Fit for Data Science?
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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 […]
5 Agile Data Science Myths
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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 […]
This blog ain’t much but Isaac Newton would like it
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At the time of posting this, this blog has a generic template, broken links, no logo, only one post, and is all around…well, bad. So why would we voluntarily share with the world something so embarrassing? Because Jeff and I have been thinking about starting a blog for several months but up until now, we have nothing […]
Kanban
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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 for Data Science
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Scrum for Data Science Given Scrum’s popularity with software teams, it’s no surprise that many organizations are turning to Scrum for data science product development. But, Does Scrum work for Data Science? Well…results vary. So we’ll start by defining Scrum, then identify Scrum’s use in data science, evaluate its pros and cons, and finally dive […]