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 Domino […]
Read more
The Domino Data Science Life Cycle is a modern life cycle approach. Domino Data Lab, a Silicon Valley vendor that provides a data science platform, crafted its data science project life cycle framework in a 2017 whitepaper. The paper wraps its life cycle around goals, challenges, diagnoses, system recommendations, and role […]
Read more
Team Data Science Process If you combine Scrum and CRISP-DM, you would get something that looks like Microsoft’s Team Data Science Process. Launched in 2016, TDSP is “an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently.” (Microsoft, 2020 ). This is a modern data science […]
Read more
What is a Data Science R&D Approach? The data science process can be viewed as a research endeavor that transitions into an engineering project. As such, some organizations combine traditional research methodologies with modern agile development approaches. Google Brain and DemandJump are two companies that split data science into these […]
Read more
What is Agile-Waterfall? Agilists juxtapose waterfall as the antithesis of agility. However, Eric Stolterman, Senior Executive Associate Dean at Indiana University, believes that crystalline processes such as waterfall and liquid processes such as Scrum should simultaneously co-exist. The same problem can be viewed from the lenses of both crystalline or […]
Read more
The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model that serves as the base for a data science process. It has six sequential phases: Published in 1999 to standardize data mining processes across industries, it has since become the most common methodology for data mining, analytics, and data science projects. Data science […]
Read more
Waterfall, also referred to as the classic life cycle or traditional project management, originated from manufacturing and construction and was applied to software engineering projects starting in the 1960s. A waterfall project flows through defined phases such as shown in the diagram to the right. Some waterfall models include variations […]
Read more
Without well-known, comprehensive project management approaches specific for data science, teams often combine elements from two or more approaches to cater to their own needs. Although combining multiple approaches can be challenging, they allow users to design approaches that fit their individual needs. Two common hybrid approaches in data science […]
Read more
Data science projects are unique. It’s time to start managing them as such.
Get the jumpstart guide to better manage your next project.