The SAS Institute developed SEMMA as the process of data mining. It has five steps (Sample, Explore, Modify, Model, and Assess), earning the acronym of SEMMA. You can use the SEMMA data mining methodology to solve a wide range of business problems, including fraud identification, customer retention and turnover, database […]
Read moreWhat Is the KDD Process? Dating back to 1989, the namesake Knowledge Discovery in Database (KDD) represents the overall process of collecting data and methodically refining it. The KDD Process is a classic data science life cycle that aspires to purge the ‘noise’ (useless, tangential outliers) while establishing a phased […]
Read more
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. […]
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
Data science projects are unique. It’s time to start managing them as such.
Get the jumpstart guide to better manage your next project.