The following is an interview by Jeff Saltz with Jelle de Jong. Jelle, a certified DSPA Team Lead, is an independent consultant. He currently works with Lely, an agricultural business based in the Netherlands. Jeff: Can you provide some background on your consulting business? Jelle: I’ve been in the quantitative modeling industry for 15+ years. […]
The following is an interview by Jeff Saltz with Hector Rangel. Hector, a certified DSPA Team Lead, is a Consultant and Data Science lead at Arena Analytics, a small consulting company located in Mexico. Jeff: Can you tell me a bit about yourself? Hector: I’m an industrial engineer and a business professional with 8 years of […]
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?
Exploring how to “do” agile data science
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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
