Data Science Methodology

The steps used in data science projects in order to produce optimal results that can answer the questions of a problem to be solved are called data science methodology.

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In the KDD methodology, the Data Selection Stage carries out a series of processes to complete the data and maintain data consistency.

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The SEMMA methodology is Sample, Explore, Modify, Model and Assess

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There are 5 stages of CRISP-DM processes starting from Business Understanding to Deployment

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The final stage in IBM Data Science is the Feedback stage

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In Microsoft's Team Data Science Process methodology, model development includes feature engineering, model fitting, and model evaluation.

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Tahap pertama pada Metodologi dari Domino (Domino DataLab Methodology) adalah Data Acquisition and Preparation.

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Examples of technical activity methodologies are KDD and CRISP-DM

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There are three levels of data science competency : basic level, intermediate level,and advanced level.

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One of the level 1 (basic level) competencies is Data Manipulation

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