Andrew Stewart is an instructor in the Johns Hopkins University Engineering for Professionals Data Science program, where he teaches Data Science. His academic interests center on statistical reasoning, systems thinking, and the scientific foundations of modeling. He is particularly interested in how data science operates as a discipline of structured inquiry within complex technological and organizational environments.
Professionally, Andrew works at the intersection of applied machine learning, experimentation systems, and analytics architecture. He has led teams and initiatives spanning product analytics, causal experimentation, measurement design, and MLOps infrastructure, with a focus on building scalable research and validation frameworks inside technology organizations. His work emphasizes hypothesis-driven development, reproducible analytical workflows, and the integration of quantitative insight into decision-making systems.
In his teaching, Andrew approaches data science as a scientific discipline rather than a purely technical toolkit. He encourages students to treat modeling as part of a broader research lifecycle – from problem formulation and uncertainty quantification to validation, documentation, and communication. His goal is to help cultivate data science leaders who can design rigorous analytical systems, reason clearly under uncertainty, and guide organizations toward evidence-based decision-making.
Education History
- B S, Biological Sciences, University of Maryland
- M S, Computer Science, Johns Hopkins University
Work Experience
Lecturer, Johns Hopkins University
Professional Organizations
ACM
Courses
Data Science
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