outbreak_science_2

Outbreak science 2

This course will focus on the dynamics, simulation, and inference of: the Kermack-McKendrick model (including age of infection), meta-population, and network models. The course will emphasize both the theory of advanced epidemic models and mathematical/statistical programming.

January 2025 · tom mcandrew
dataexploration

Data Exploration in Python

This course provides an introduction to the fundamentals of programming in Python. Students will gain experience designing, implementing, and testing their Python code, as well as in using Jupyter Notebooks, and IPython for statistics and data analysis. Multiple programming paradigms will be explored. The course covers Python data types, input, and output, and control flow in the context of preparing, cleaning, transforming, and manipulating data. In addition, students will use Python to conduct exploratory data analyses, including computing descriptive statistics

January 2025 · tom mcandrew
outbreak_science

Outbreak science

This course aims to introduce students to models that describe the spread of a pathogen through a population, and how models can support public health decisions. Students will be expected to complete mathematical/statistical exercises and write code that simulates infectious processes.

November 2024 · tom mcandrew
phds2

Population Health Data Science 2 (Regression)

Regression; Likelihood; Fisher Information; Regression

November 2022 · tom mcandrew
phds1

Population Health Data Science 1 (Probability)

Univariate statistics;Probability;Sets

November 2022 · tom mcandrew