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Data Science Methodology

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This course focuses on the Foundational Methodology for Data Science by John Rollins, which was introduced in the previous video. However, it is not the only methodology that you will encounter in data science. For example, in data mining, the Cross Industry Process for Data Mining (CRISP-DM) methodology is widely used. The CRISP-DM methodology is a process aimed at increasing the use of data mining over a wide variety of business applications and industries. The intent is to take case specific scenarios and general behaviors to make them domain neutral. CRISP-DM is comprised of six steps with an entity that has to implement in order to have a reasonable chance of success. The six steps are shown in the following diagram: Fig.1 CRISP-DM model, IBM Knowledge Center, CRISP-DM Help Overview Business Understanding This stage is the most important because this is where the intention of the project is outlined. Foundational Methodology and CRISP-DM are aligned here. It requires communication

This course focuses on the Foundational Methodology for Data Science by John Rollins, which was introduced in the previous video. However, it is not the only methodology that you will encounter in data science. For example, in data mining, the Cross Industry Process for Data Mining (CRISP-DM) methodology is widely used. The CRISP-DM methodology is a process aimed at increasing the use of data mining over a wide variety of business applications and industries. The intent is to take case specific scenarios and general behaviors to make them domain neutral. CRISP-DM is comprised of six steps with

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