A Layman's Guide to Data Science Workflow

When you get involved in a data science project, you must always take care of basic elements first before starting a project like business objective, domain knowledge, standard data science practices of an organization, and previous experiences while considering the next steps to problem solutions like data source identification, data modeling, data management, and data visualizations. The data science industry already offers a variety of data science workflow frameworks to solve different kinds of data science problems. It is not possible to develop an all-inclusive Data Science Workflow to solve all business problems. In lieu of that, it is important to follow some best-standard data science practices, such as automating data pipelines, planning inferences, and doing a post-mortem at the end of every project to identify any potential improvement areas. You will learn about various standard data science workflows in this article. You will also gain an understanding of the structure of a Data Science Workflow and the considerations that need to be taken into account as you follow the Data Science Workflow.