CRISP-DM remains the most popular methodology for analytics, data mining, and data science projects, with 43% share in latest KDnuggets Poll, but a replacement for unmaintained CRISP-DM is long overdue.| KDnuggets
Our 2020 State of Data and Analytics research provides unique insights into how US data leaders are responding to COVID-19 and where their strategic priorities will lay over the next 12-24 months.| www.coriniumintelligence.com
Microsoft's Team Data Science Process (TDSP) combines elements of Agile, modern software practices, and CRISP-DM. Is it effective for data science projects?| Data Science Process Alliance
SEMMA is a step-by-step data mining process used to extract knowledge in databases. Learn about how to use it your data science projects.| Data Science Process Alliance
Does Scrum work for data science projects? Software's most popular agile framework provides some great benefits but it also constrains data science work.| Data Science Process Alliance
What is the Knowledge Discovery in Databases (KDD) Process and Data Mining? Learn how to use these in your data science projects.| Data Science Process Alliance
What is Kanban, and does it work for data science? Its flexibility is ideal for many projects, espicially when combined with more comprehensive approaches.| Data Science Process Alliance
This is the web's most comprehensive guide to managing data science projects. Combine a data science methodology with an agile approach.| Data Science Process Alliance
CRISP DM is the most popular framework for delivering data science projects. Learn 5 keys to leverage CRISP DM on your projects.| Data Science Process Alliance
The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the data science life cycle.| Data Science Process Alliance
