Data science projects typically follow a defined methodology that includes collecting and exploring data, preparing it for modeling, building and evaluating models, and deploying results. The data science life cycle involves iterative experimentation to gain insights, develop solutions, and continuously improve results. Effective communication of findings is important at each stage of the process.