Event  Date  Description  Course Materials 

Preparation 
Sunday March 22 
Preparation Tutorials 
Python tutorial 
A0 Due 
Saturday March 28 
Assignment #0 due Python, NumPy, and Matplotlib 

Lecture 1 
Sunday March 29 
Data Analysis & Visualization Pandas dataframes 

A1 Due 
Saturday April 18 
Assignment #1 due Pandas & Seaborn 

Lecture 2 
Sunday April 19 
Statistical Inference Parameter estimation 

Lecture 3 
Sunday April 26 
Bayesian Inference Bayesian vs frequentist inference 

A2 Due 
Saturday May 02 
Assignment #2 due Statistical inference 

Lecture 4 
Sunday May 03 
Generalized Linear Models 1 Normal linear regression 

Lecture 5 
Sunday May 10 
Generalized Linear Models 2 Binomial classification 

A3 Due 
Saturday May 16 
Assignment #3 due Generalized linear models 

Lecture 6 
Sunday May 17 
Population Genetics Discretetime models for change in allele frequencies 

A4 Due 
Saturday May 23 
Assignment #4 due Discrete time models 

Lecture 7 
Sunday May 24 
Population Dynamics 1 Deterministic continuoustime models for population growth 

Lecture 8 
Sunday May 31 
Population Dynamics 2 Deterministic continuoustime models for species interactions 

A5 Due 
Saturday June 06 
Assignment #5 due Deterministic continuoustime models 

Lecture 9 
Sunday June 07 
Population Dynamics 3 Stochastic continuoustime models for molecular dynamics 

Lecture 10 
Sunday June 14 
Approximate Bayesian computation Likelihoodfree fitting of complex stochastic models 

Lecture 11 
Sunday June 21 
Feed Forward Neural networks Multinomial classification: Softmax model 

A6 Due 
Saturday June 27 
Assignment #6 due Stochastic continuoustime models 

Lecture 12 
Sunday June 28 
Density Estimation Histograms 

A7 Due 
Saturday July 04 
Assignment #7 due Artificial neural networks 

Proposal Due 
Saturday July 11 
Proposal due 

Project Due 
Sunday August 30 
Final project due 