As the importance of data science is increasing, the number of projects involving data science and machine learning is rising either in quantity or in complexity. It is essential to employ a methodology that may contribute to the improvement of the outputs. In this context, it is crucial to identify possible approaches. And an overview of the evolution of data mining process models and methodologies is given for context. And the analysis showed that the methodologies covered were not complete. So, a new approach is proposed to tackle this problem. POST-DS (Process Organization and Scheduling electing Tools for Data Science) is a process-oriented methodology to assist the management of data science projects. This approach is not supported only in the process but also in the organization scheduling and tool selection.

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Data science methodology,CRISP-DM,Data science process,Machine learning,Data science,Data mining

• Computer and Information Sciences - Natural Sciences

This publication is an output of the following project(s):

• Alternative Shaper
• Impact of Blockchains and Decentralised Ledger Technologies