Data Quality Matters
Susan emphasizes the importance of meticulous data preparation before applying automation, arguing that understanding the data can lead to significant improvements in model quality. She shares her methodology for normalizing data, highlighting the need for careful handling of suffixes and the insights that can be gained from working closely with the data. The conversation underscores that while automation can save time, it should not replace the critical thinking and analysis necessary for effective data management.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
SDS 487: Fixing Dirty Data — with Susan Walsh
Related Questions