Data science life cycle

The data science lifecycle is a process that data scientists follow to solve a business problem using data. It is an iterative process that can be broken down into the following steps:

 

Problem identification: This is the first step in the lifecycle, where the data scientist identifies the business problem that they want to solve.

Business understanding: In this step, the data scientist works with stakeholders to understand the business requirements and the context of the problem.

Data collection: This step involves collecting the data that is needed to solve the problem.

Data cleaning and preparation: This step involves cleaning and preparing the data so that it is ready for analysis.

Exploratory data analysis (EDA): This step involves exploring the data to gain insights into the problem.

Modeling: This step involves building a model that can be used to solve the problem.

Model evaluation: This step involves evaluating the model to ensure that it is accurate and reliable.

Model deployment: This step involves deploying the model into production so that it can be used to make predictions.

Monitoring: This step involves monitoring the model to ensure that it is performing as expected.

Done