Explain the data science lifecycle and its key phases.

The data science lifecycle is a structured approach to solving business problems using data-driven methods and iterative processes.\n\n• Business Understanding: Define objectives and success criteria\n• Data Acquisition: Collect from databases, APIs, web scraping\n• Data Preparation: Clean, transform, handle missing values\n• Exploratory Analysis: Understand patterns, correlations, distributions\n• Modeling: Build and train predictive models\n• Evaluation: Validate model performance and business impact\n• Deployment: Implement in production systems\n\nExample: E-commerce recommendation system starts with understanding user behavior goals, collecting clickstream data, cleaning and feature engineering, building collaborative filtering models, testing accuracy, and deploying real-time recommendations.