Learn the essential steps and techniques for mastering data preparation in data science projects through this comprehensive conference talk. Explore the critical importance of data quality in AI and machine learning systems, and discover why data scientists spend up to 80% of their time on data preparation and cleaning. Gain insights into the seven key steps for effective data munging and wrangling, including sourcing, preparing, transforming, and cleaning data. Understand best practices for implementing a robust data preparation process to ensure clean, consistent, and accurate data for training machine learning models. Enhance your skills in handling the crucial pre-processing phase of AI/ML projects and improve the predictive power of your trained models.