This series provides an overview of the data science lifecycle, guiding readers through the stages from problem identification to model deployment and evaluation. Each post explores a different stage, covering topics such as problem framing, data collection and cleaning, data analysis and model building, and model deployment and evaluation. By the end of the series, readers will have a comprehensive understanding of the data science lifecycle and its importance in solving real-world problems.