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# Generate and visualize data in Python and MATLAB

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Learn how to simulate and visualize data for data science, statistics, and machine learning in MATLAB and Python

## What does this course teach you?

Introductions
Following along in Python, MATLAB, or Octave Overall goals of this course Why and how to simulate data What is "signal" and what is "noise"? The importance of visualization
Descriptive statistics and basic visualizations
Course materials for this section (reader, MATLAB code, Python code) Mean, median, standard deviation, variance Histogram Interquartile range Violin plot
Data distributions
Course materials for this section (reader, MATLAB code, Python code) Normal and uniform distributions QQ plot Poisson distribution Log-normal distribution Measures of distribution quality (SNR and Fano factor) Cohen's d for separating distributions
Time series signals
Course materials for this section (reader, MATLAB code, Python code) Sharp transients Smooth transients Repeating: sine, square, and triangle waves Multicomponent oscillators Dipolar and multipolar chirps
Time series noise
Course materials for this section (reader, MATLAB code, Python code) Seeded reproducible normal and uniform noise Pink noise (aka 1/f aka fractal) Brownian noise (aka random walk) Multivariable correlated noise
Image signals
Course materials for this section (reader, MATLAB code, Python code) Lines and edges Sine patches and Gabor patches Geometric shapes Rings
Image noise
Course materials for this section (reader, MATLAB code, Python code) Image white noise Checkerboard patterns and noise Perlin noise in 2D Filtered 2D-FFT noise
Data clustering in space
Course materials for this section (reader, MATLAB code, Python code) Clusters in 2D Clusters in N-D
Spatiotemporal structure using forward models
Course materials for this section (reader, MATLAB code, Python code) Forward model: 2D sheet Mixed overlapping forward models Example: Simulate human brain (EEG) data
Bonus section
Bonus lecture

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