This course develops doctoral-level competency in both qualitative and quantitative analysis methods as applied to research in the AI domain, equipping learners with the analytical toolkit required to investigate significant problems of applied and agentic AI practice. Building on the research design foundations of TECH 805, learners develop proficiency in selecting, applying, and critically evaluating analysis methods appropriate to their doctoral research question and contribution type – spanning quantitative methods (statistical inference, effect size, uncertainty quantification, benchmark analysis, reproducibility validation), qualitative methods (thematic analysis, grounded theory, case study, expert interview, discourse analysis), mixed-method designs, and AI-specific evaluation approaches (LLM-as-judge, human evaluation, automated metrics, red-teaming). The course emphasizes methodological fit, analytical validity, and the responsible and defensible interpretation of results in the context of doctoral AI research.