Scientific Computing with Python

Event	Date	Description	Course Materials
Preparation	Sunday March 22	Preparation Tutorials Environment setup	Python tutorial NumPy tutorial Matplotlib tutorial Azure notebooks guide Anaconda download
A0 Due	Saturday March 28	Assignment #0 due Python, NumPy, and Matplotlib No submission or grade	Assignment Solution
Lecture 1	Sunday March 29	Data Analysis & Visualization Pandas dataframes Seaborn: statistical visualizations	Notebook
A1 Due	Saturday April 18	Assignment #1 due Pandas & Seaborn	Assignment Solution
Lecture 2	Sunday April 19	Statistical Inference Parameter estimation Maximum likelihood	Notebook Seeing Theory Book: Think Stats
Lecture 3	Sunday April 26	Bayesian Inference Bayesian vs frequentist inference Markov chain Monte Carlo emcee	Notebook
A2 Due	Saturday May 02	Assignment #2 due Statistical inference	Assignment Solution
Lecture 4	Sunday May 03	Generalized Linear Models 1 Normal linear regression Poisson regression Bayesian regression Tennis analytics	Notebook
Lecture 5	Sunday May 10	Generalized Linear Models 2 Binomial classification Logistic model Titanic survival prediction COVID-19 survival prediction	Logistic notebook
A3 Due	Saturday May 16	Assignment #3 due Generalized linear models	Assignment Solution
Lecture 6	Sunday May 17	Population Genetics Discrete-time models for change in allele frequencies Haldane's model / Wright-Fisher model / Kimura's diffusion equation approximation	Notebook Book: Otto & Day
A4 Due	Saturday May 23	Assignment #4 due Discrete time models	Assignment Solution
Lecture 7	Sunday May 24	Population Dynamics 1 Deterministic continuous-time models for population growth Model fitting Country population size / microbial growth curves	Notebook
Lecture 8	Sunday May 31	Population Dynamics 2 Deterministic continuous-time models for species interactions Equilibria and stability analysis Numerical integration SymPy: symbolic mathematics Lotka-Volterra predator-prey equations	Notebook Stability analysis
A5 Due	Saturday June 06	Assignment #5 due Deterministic continuous-time models	Assignment Solution
Lecture 9	Sunday June 07	Population Dynamics 3 Stochastic continuous-time models for molecular dynamics Gillespie algorithm Numba: JIT for scientific Python Protein production model	Notebook
Lecture 10	Sunday June 14	Approximate Bayesian computation Likelihood-free fitting of complex stochastic models Markov chain Monte Carlo Animal social networks	Notebook
Lecture 11	Sunday June 21	Feed Forward Neural networks Multinomial classification: Softmax model Multinomial classification: FFN Digit image recognition	Softmax notebook FFN notebook
A6 Due	Saturday June 27	Assignment #6 due Stochastic continuous-time models Approximate Bayesian computation	Assignment Solution
Lecture 12	Sunday June 28	Density Estimation Histograms Kernel density estimation Mixture models Conditional density estimation	Notebook
A7 Due	Saturday July 04	Assignment #7 due Artificial neural networks Keras	Assignment Solution
Proposal Due	Saturday July 11	Proposal due	Final project guidelines
Project Due	Sunday August 30	Final project due	Final project guidelines

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