During the past few months, we conducted a poll to see what project management framework teams used to help execute their data science projects. Based on our survey of 109 respondents, nearly half of the respondents most commonly use CRISP-DM. This was followed by Scrum, Kanban and “My Own”. See results below. A quick review of the […]
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 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 […]
What is CRISP DM? The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that naturally describes the data science life cycle. It’s like a set of guardrails to help you plan, organize, and implement your data science (or machine learning) project. Business understanding – What does the business need? Data understanding – What data do we have […]
Ad hoc processes might work for smaller, one-off projects but are becoming less sustainable as data science matures into a team sport. Meanwhile, Waterfall is the classic highly-structured project management approach that dates back to antiquity and was common in software 10 – 20 years ago. Realizing the need for a process specific to data […]