Learn how to simulate and visualize data for data science, statistics, and machine learning in MATLAB and Python
InstructorMike X Cohen
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This slide presents five exercises for students to practice assessing k-means clustering accuracy, testing blur parameters, and modifying code for asymmetric clusters.
This composite view demonstrates key visualization techniques including violin plots for distribution analysis, comparative wavelet function graphs, and QQ plots for normality assessment.
This slide illustrates the temporal structure of video data through a workflow diagram showing histograms, Python code for signal processing, and frequency domain analysis.