Program Requirements

All master’s degrees in the Department of Applied Mathematics and Statistics ordinarily require a minimum of two semesters of registration as a full-time resident graduate student.

  • Online Data Ethics course: Students must take an approved online data ethics course.
  • Complete training on the responsible and ethical conduct of research.  Please see WSE Policy on the Responsible Conduct of Research
  • Complete training on academic ethics.

A course grade of B- or better is required to meet all course requirements.

Core Requirements
EN.553.636Introduction to Data Science4.0
Core Areas
Select one course in each of the four Core Areas:12-16
Statistics
Introduction to Statistics
Bayesian Statistics
Statistical Theory
Statistical Theory II
Machine Learning
Statistical Machine Learning: Methods, Theory, and Applications
Machine Learning I
Machine Learning
Statistical Machine Learning
Optimization
Nonlinear Optimization I
Nonlinear Optimization II
Convex Optimization
Computing
Computing for Applied Mathematics
Parallel Programming
Focus Areas
Select three courses in one of the following focus areas:9-12
Computational Medicine
Introduction to Bioinformatics
Bioinformatics: Tools for Genome Analysis
Gene Expression Data Analysis and Visualization
Computational Molecular Medicine
Algorithms for Bioinformatics
Foundations of Algorithms
Computational Genomics
Analysis of Gene Expression and High-Content Biological Data
Computational Machine Learning
Statistical Machine Learning: Methods, Theory, and Applications
Machine Learning for Signal Processing
EN.520.647Information Theory3
Compressed Sensing and Sparse Recovery
Random Signal Analysis
Machine Learning I
EN.553.743
(EN.553.743 Graphical Models)
Machine Learning
Machine Learning: Data to Models
Machine Learning: Optimization
Causal Inference
EN.601.679
(EN.601.679 Machine Learning: Representation Learning)
Statistical Machine Learning
Computer Vision
Compressed Sensing and Sparse Recovery
Computer Vision
Machine Learning: Deep Learning
Vision as Bayesian Inference
Computational Finance
Stochastic Processes and Applications to Finance
Stochastic Processes and Applications to Finance II
Equity Markets and Quantitative Trading
Investment Science
Introduction to Financial Derivatives
Interest Rate and Credit Derivatives
Risk Measurement/Management in Financial Markets
Quantitative Portfolio Theory and Performance Analysis
Financial Engineering and Structured Products
Advanced Equity Derivatives
Commodity Markets and Trade Finance
Mathematics of Data Science
Monte Carlo Methods
Introduction to Convexity
High-Dimensional Approximation, Probability, and Statistical Learning
Machine Learning I
Nonlinear Optimization I
Nonlinear Optimization II
Stochastic Search & Optimization
Convex Optimization
Combinatorial Optimization
Matrix Analysis and Linear Algebra
Randomized and Big Data Algorithms
Approximation Algorithms
Language and Speech
Semantics I
Semantics II
Syntax I
Phonology I
Information Extraction
Speech and Auditory Processing by Humans and Machines
Natural Language Processing
Machine Learning: Linguistic & Sequence Modeling
Statistical Theory
Statistical Machine Learning: Methods, Theory, and Applications
Bayesian Statistics
Statistical Theory
Statistical Theory II
Topics in Statistical Pattern Recognition
Distribution-free statistics and Resampling Methods
High-Dimensional Approximation, Probability, and Statistical Learning
Statistical Pattern Recognition Theory & Methods
Causal Inference
Statistical Machine Learning
Elective
The program requires the student to take one elective course. To maximize a student's flexibility in choosing this course, the student may choose any course offered at JHU that is directly or indirectly related to data science. The elective course must be approved by the student's advisor as well as the Internal Oversight Committee3
Capstone Experience
EN.553.806Capstone Experience in Data Science (EN.553.806 Capstone Experience) 13 - 10
Total Credits34-48