Learn how to simulate and visualize data for data science, statistics, and machine learning in MATLAB and Python
InstructorMike X Cohen
Instruction provides both Python and MATLAB code, allowing for cross-platform application of data generation and visualization techniques.
The curriculum includes a wide range of data types, from standard statistical distributions to complex time series signals and 2D image noise.
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.
The curriculum moves from statistical distributions into signal and image processing, which may not align with all learners' specific interests. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
Some MATLAB examples may rely on dedicated toolboxes rather than base language functions. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
One signal from early 2022 suggests the shift toward time series and signals may not align with all interests; while a more recent update date is present, it does not prove these specific sections were modified.