Data Science -Data science project lifecycle

Using data to get insights into how the business is doing is vital for organizations. A data science project life cycle usually observes the following basic steps from the beginning to the end.

Those steps may vary according to the type of project or company:

The main goal of this stage is to get a clear idea about the project's aims. All the outcomes and the respective data sources are defined, and the stakeholders have to agree.

In this phase, consolidation of data sources and cleaning of the data happens. A very time-consuming step where all the issues with the data are identified, and the final dataset, connected to the goals, is generated.

All the possible analyses and models are generated in this phase(where the fun happens!).

Connected with data analysis, this is the stage where all the data take different shapes allowing the uncovering of details otherwise not visible.

The wrap-up stage. Everything is assembled and presented to the stakeholders to support a data-driven decision-making process.

Some of the best theorizing comes after collecting data because then you become aware of another reality -By Robert J. Shiller, Economics Nobel Prize Winner

How are you getting insights from the data that you collect? Do your data science projects use the steps above?