This series will provide an in-depth exploration of the data science lifecycle. It will cover the key stages involved, including data acquisition and preparation, exploratory data analysis and feature engineering, model building and evaluation, as well as deployment and maintenance. Each post will delve into the specific techniques and considerations within each stage, equipping readers with a comprehensive understanding of the entire lifecycle. Whether you're new to data science or looking to enhance your skills, this series will guide you through the process of turning raw data into valuable insights.