During the past month, we conducted at poll to see what project management framework teams used to help execute their data science projects. Based on our survey of 109 respondents, CRISP-DM was the most commonly used data science process framework (it was used by about half the respondents). This was followed by Scrum, Kanban and “my […]
A data science workflow defines the phases (or steps) in a data science project. Using a well-defined data science workflow is useful in that it provides a simple way to remind all data science team members of the work to be done to do a data science project. One way to think about the benefit […]
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 […]
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.
Can you mix and match elements of multiple project management approaches? Of course! This post explores two such general hybrid approaches for data science:agile-waterfall and research and development.
How do you manage data science projects? Is it software? Is it research? Or maybe, simply magic? This four-part post is an overview 10 ways projects are or could be managed. To start, we’ll explore ad hoc project management, waterfall, and CRISP-DM.
While there is no standard process for a team to use when working on a data science project, CRISP-DM (CRoss-Industry Standard Process for Data Mining) is one framework that is often considered for data science projects. Perhaps because of this, there are lots of web sites describing the 6 phases of a CRISP-DM project, and […]
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 […]
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 […]
What is a Data Science Life Cycle? A data science life cycle defines the phases (or steps) in a data science project. Using a well-defined data science life cycle is useful in that it provides a common vocabulary (and shared mental model) of the work to be done to do a data science project. Commonly […]