Life Cycle of Data Science Project

Life Cycle of Data Science:

The Data Science Process (DSP) provides a lifecycle to structure the development of your data science projects. The lifecycle outlines the steps from start to finish that projects usually follow when they are executed. If you are using another data science lifecycle such as your organization’s custom process, you can still use the task-based DSP in the context of those development lifecycles.

The lifecycle has been designed for data science projects that ship as part of intelligent applications. These applications deploy machine learning or artificial intelligence models for predictive analytics. Exploratory data science projects or ad-hoc analytics projects can also benefit from using this process.

CRISP-DM remains the top methodology for data mining projects. It was conceived around 1996. The 6 high-level phases of CRISP-DM are still a good description of the analytics process, but the details and specifics need to be updated. It doesn’t seem to be maintained and adapted to the challenges of Big Data and modern data science.

Five Stages of Data Science Life Cycle:

The lifecycle of a Data Science project outlines the major stages that projects typically execute, often iteratively:

• Business Understanding
• Data Acquisition
• Modeling
• Deployement
• Customer Acceptance