This series provides a comprehensive overview of the data science lifecycle and its significance in decision-making. It covers the five stages involved: problem definition, data collection, data preparation, model building, and model deployment. Each stage is discussed, including techniques such as problem formulation, data cleaning, model evaluation, and deployment strategies.