Abstract: Lesson 1: Data Modeling
Familiarize yourself with relational databases and the one-to-many relationships between a unit of analysis and detailed data. Learn about entities and the 4 basic table types.
Lesson 2: Signal Types
Discover 13 different signal types to add to your feature engineering toolkit. Practice coding each of these signal types in Python, by combining joins, filters, transformations, and aggregations.
Lesson 3: Primary Entities and Feature Lists
Learn how the primary entity of a feature list must align with your use case, and how the primary entity of each feature must be coherent with the feature list. Create a feature list containing a diverse set of intuitive signals that improve model performance.
By the end of this session, you will be able to confidently and efficiently code with the open-source FeatureByte library, to declare state-of-the-art features with a diverse range of feature types.
Bio: Colin is a seasoned data scientist who has worked in the finance, healthcare, security, oil and gas, government, telecommunications, and marketing industries. He has a keen interest in exploring the relationship between humans and AI and has contributed to projects on AI ethics, governance, and the future of work. His work has gained global recognition from the World Economic Forum, and he has contributed to several important initiatives, including the Singapore government's official AI strategy, PDPC AI Governance and Ethics Guidelines, and the Monetary Authority of Singapore Veritas Initiative. In addition to his professional work, Colin is a dedicated healthcare advocate who volunteers for cancer research.