The Data Science group in the Applied Mathematics and Statistics Department at Johns Hopkins University is a vibrant community of researchers whose interests span classical statistics, machine learning, optimization, network inference, and computational biology.  Our group’s interdisciplinary approach incorporates theory and practice and unites researchers across different backgrounds to develop cutting-edge methodologies for analyzing complex data sets. From working on real-time delivery of precision healthcare to identifying structural changes in billion-node networks to helping food banks operate more effectively to everything in between, the Data Science group at Johns Hopkins is at the forefront of the field, advancing our understanding of data science and its potential to shape our world for the better.

Related Courses

Complete descriptions appear in the course catalog.
View the semester
course schedule.

EN.553.767: Iterative Algorithms in Machine Learning: Theory and Applications 

EN.553.669: Large-Scale Optimization for Data Science

EN.553.432/632: Bayesian Statistics

EN.553.733: Nonparametric Bayesian Statistics

EN.553.743: Equivariant Machine Learning

EN.553.413/613: Applied Statistics and Data Analysis

EN.553.450/650: Computational Molecular Medicine

EN.553.742: Statistical Inference on Random Graphs

EN.553.740: Machine Learning I

EN.553.741: Machine Learning II

EN.553.662: Optimization for Data Science

EN.553.436/636: Introduction to Data Science

EN 553.633: Monte Carlo Methods

EN 553.763: Stochastic Search & Optimization

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Research and academic opportunities in data science