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Advanced Computational Methods for Disease Modelling
RDS 203

Course Information

Description

This is an advanced course on statistical and computational methods that are applicable for disease modelling in public health and medicine. Students will learn to apply state-of-the-art methods related to three core modules: 1) Numerical Methods, 2) Simulation-based Inference, and 3) High Performance Computing.
Prerequisites include a course in mathematical modeling (RDS 285 or RDS 288), probability and statistics (BST 201 or ID 201 or BST 206), and basic knowledge of mathematical notation and reasoning. Prior programming experience (e.g., R, Python, C++, Java) is strongly recommended.

Prerequisites: (RDS 285 or RDS 288) and (BST 201 or ID 201 or BST 206).

School Harvard Chan School
Credits 2.5
Cross Reg

Available for Harvard Cross Registration

Course Component Lecture
Instruction Mode In Person
Grading Basis HSPH Ordinal
Course Requirements Pre-requisites: (RDS 285 or RDS 288) and (BST 201 or ID 201 or BST 206).