Slides; Github coding demos (EST 371, EST 610)
This course introduces doctoral students to foundational and advanced approaches to data analysis for research in technology, policy, and innovation. Topics including qualitative data analysis, data collection, cleaning, preparation, statistical analysis, causal inference, geospatial analysis, network analysis, text analysis and NLP, and data analysis pipeline involving AI.
Topics include basics of data science, Python for data science, data collection, data cleaning, EDA, data visualization, data aggregation and manipulation, statistical analysis and regression, and special data topics such as geospatial data and network data.
In this course, we examine AI's role in four grand challenges: sustainability (AI's role in addressing climate and energy challenges, and AI's environmental impact), health (AI's role in healthcare, global health, mental health, and water, food, and sanitation systems), security (AI's role in physical security and cyber security), and joy of living (AI's role in education and future of work, as well as AI future and human thriving).