This course introduces doctoral students to foundational and advanced approaches to data analysis for research in technology, policy, and innovation. Through a combination of conceptual discussions, hands-on coding exercises, and applied research activities, students will learn how to collect, clean, manage, analyze, visualize, and interpret diverse forms of data commonly used in interdisciplinary research on technology, policy, and innovation. Topics include data wrangling, exploratory data analysis, statistical analysis, clustering, text analysis, applied natural language processing (NLP), network analysis, geospatial analysis, data visualization, and emerging LLM-assisted research methods. The course emphasizes practical research workflows using Python and contemporary computational tools while critically examining methodological choices, limitations, and ethical considerations in data-driven research. By the end of the course, students will develop the skills to design and conduct independent data analysis projects relevant to technology, society, innovation, and public policy.
[forthcoming] Link to coding demos on GitHub
This course will equip students with technical foundations in data science and AI while focusing on fairness, bias mitigation, and the responsible design and deployment of AI systems. The topics include basics of data science, Python for data science, essential Python packages for data science (Pandas, Numpy), data collection, data cleaning, exploratory data analysis, data visualization, data aggregation and manipulation, statistical analysis and regression, and three special data topics (geospatial data, network data, and text data), and GenAI's role in data science.
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).