Data Science -Data science project lifecycle

Updated: Jan 22

Using data to get insights on 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.


Data Science Project Lifecycle


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


1. Business understanding



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





2. Data understanding



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.






3. Data Analysis







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







4. Visualizations





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







5. Presentation of findings




The wrap-up stage. Everything is put together 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?


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