Deep statistics refers to statistical endeavors that go deeper than developing methods and applying them to solve problems in data science. It explores and develops statistical theory and insights to contribute to the building of foundations for data science. The course provides a deep dive into statistical foundations and insights for multi-source, multi-phase, and multi-resolution learning, interwoven with case studies on using AI and Earth Observations (EO) for sustainable developments (e.g., global poverty). Foundational issues are framed as inevitable trade-offs for data science: between data quality and quantity, between statistical and computational efficiencies, and between robustness and relevance of learning methods and findings. Practical questions examined include handling messy and private data, drawing causal conclusions from AI-EO data, and translating scientific insights into policies.
Recommended Prep: A student should preferably have at least one of the following background: – Foundational and theoretical proficiency: at the level of STATS 210, 211. (Strong interest in statistical theory and foundational issues.) – Data analytical and computational skills: basic data science skills and being able use image data. (Strong skills in working with real data, data management, and computational. Preferably having skills in Python.)