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.
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
Most IT project management sites focus on software engineering. To our knowledge, there is no comprehensive guide focused on data science. So gathering information from interviews, our industry experience, and third-party sources, we have developed a project management guide dedicated to data science with the goal to arm practitioners with a better understanding of various […]
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 […]