Greetings and Salutations from 360DigiTMG. This set of videos explains the many stages of a data science project.
One of the most important stages of the data science life cycle is perhaps the first: business understanding.

A well-defined problem frequently has its own answer inside it, which is typically relatively simple and evident. It is easier to tackle problems when they are correctly defined, which results in time, money, and resource savings.

2. Data preparation accelerates the process and offers a stronger basis for decision-making when done with the appropriate tools.

3.Model Building: Finding a statistical, mathematical, or simulation model to gain understanding and make predictions is part of the model building process.

4.Model Evaluation: During the early stages of research, it is crucial to gauge a model's effectiveness. It also aids in model monitoring.

5.Deployment: In data science, the term ""deployment"" refers to the use of a model to make predictions using fresh data. Typically, a project doesn't finish with the construction of a model.

6. Monitoring and Maintenance: The practice of routinely checking and assessing crucial corporate data to guarantee its quality and confirm that it is being followed according to specified standards and processes is known as maintenance and main.

CRISP-ML(Q) | Learn Data Science using Animation

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