This class covers useful concepts that often are not covered in data science books. We cover the following topics:

• Feature engineering
• Dealing with class imbalance
• Structuring databases for data science
• Model pipelining

• Machine Learning Design Patterns by Lakshmanan, Robinson, & Munn, Chapters 2 and 25
• 10 Techniques to deal with Imbalanced Classes in Machine Learning
• Consider Gold, Silver, and Bronze for your Data, Not Just the Olympics