When working with big data, it is always advantageous for data scientists to follow a well-defined data science workflow. Regardless of whether a data scientist wants to perform analysis with the motive of conveying a story through data visualization or wants to build a data model- the data science workflow process matters. Having a standard workflow for data science projects ensures that the various teams within an organization are in sync, so that any further delays can be avoided.
The end goal of any data science project is to produce an effective data product. The usable results produced at the end of a data science project is referred to as a data product. A data product can be anything -a dashboard, a recommendation engine or anything that facilitates business decision-making) to solve a business problem. However, to reach the end goal of producing data products, data scientists have to follow a formalized step by step workflow process. A data product should help answer a business question. The lifecycle of data science projects should not merely focus on the process but should lay more emphasis on data products. This post outlines the standard workflow process of data science projects followed by data scientists.