| 1. Bonus lecture.html | 2.7 KB | ||
| 1. Course materials for this section (reader, MATLAB code, Python code).html | 102.4 B | ||
| 1. Following along in Python, MATLAB, or Octave.mp4 | 45.5 MB | ||
| 1. Following along in Python, MATLAB, or Octave.srt | 9.1 KB | ||
| 1.1 genvisdata_dataClusters.zip | 131.3 KB | ||
| 1.1 genvisdata_descriptives.zip | 230.1 KB | ||
| 1.1 genvisdata_distributions.zip | 199.2 KB | ||
| 1.1 genvisdata_forwardModels.zip | 4 MB | ||
| 1.1 genvisdata_imageNoise.zip | 310.8 KB | ||
| 1.1 genvisdata_imageSignals.zip | 150.4 KB | ||
| 1.1 genvisdata_timeSeriesNoise.zip | 162.7 KB | ||
| 1.1 genvisdata_timeSeriesSignals.zip | 152.5 KB | ||
| 2. Clusters in 2D.mp4 | 82.7 MB | ||
| 2. Clusters in 2D.srt | 18.5 KB | ||
| 2. Forward model 2D sheet.mp4 | 87.6 MB | ||
| 2. Forward model 2D sheet.srt | 16.4 KB | ||
| 2. Image white noise.mp4 | 67.9 MB | ||
| 2. Image white noise.srt | 12.5 KB | ||
| 2. Lines and edges.mp4 | 54.8 MB | ||
| 2. Lines and edges.srt | 13.7 KB | ||
| 2. Mean, median, standard deviation, variance.mp4 | 72.9 MB | ||
| 2. Mean, median, standard deviation, variance.srt | 21.3 KB | ||
| 2. Normal and uniform distributions.mp4 | 82.8 MB | ||
| 2. Normal and uniform distributions.srt | 20.4 KB | ||
| 2. Overall goals of this course.mp4 | 29.8 MB | ||
| 2. Overall goals of this course.srt | 6.2 KB | ||
| 2. Seeded reproducible normal and uniform noise.mp4 | 61.6 MB | ||
| 2. Seeded reproducible normal and uniform noise.srt | 12.2 KB | ||
| 2. Sharp transients.mp4 | 65.9 MB | ||
| 2. Sharp transients.srt | 15.3 KB | ||
| 3. Checkerboard patterns and noise.mp4 | 48.8 MB | ||
| 3. Checkerboard patterns and noise.srt | 12.5 KB | ||
| 3. Clusters in N-D.mp4 | 35.1 MB | ||
| 3. Clusters in N-D.srt | 6.1 KB | ||
| 3. Histogram.mp4 | 34.3 MB | ||
| 3. Histogram.srt | 9 KB | ||
| 3. Mixed overlapping forward models.mp4 | 90.1 MB | ||
| 3. Mixed overlapping forward models.srt | 13.4 KB | ||
| 3. Pink noise (aka 1f aka fractal).mp4 | 93.1 MB | ||
| 3. Pink noise (aka 1f aka fractal).srt | 18.9 KB | ||
| 3. QQ plot.mp4 | 36.7 MB | ||
| 3. QQ plot.srt | 11.8 KB | ||
| 3. Sine patches and Gabor patches.mp4 | 86 MB | ||
| 3. Sine patches and Gabor patches.srt | 16.9 KB | ||
| 3. Smooth transients.mp4 | 108.3 MB | ||
| 3. Smooth transients.srt | 25.2 KB | ||
| 3. Why and how to simulate data.mp4 | 24.8 MB | ||
| 3. Why and how to simulate data.srt | 7.2 KB | ||
| 4. Brownian noise (aka random walk).mp4 | 57.3 MB | ||
| 4. Brownian noise (aka random walk).srt | 14.2 KB | ||
| 4. Example Simulate human brain (EEG) data.mp4 | 122.5 MB | ||
| 4. Example Simulate human brain (EEG) data.srt | 21.8 KB | ||
| 4. Geometric shapes.mp4 | 67 MB | ||
| 4. Geometric shapes.srt | 15.8 KB | ||
| 4. Interquartile range.mp4 | 50.9 MB | ||
| 4. Interquartile range.srt | 10.4 KB | ||
| 4. Perlin noise in 2D.mp4 | 56.4 MB | ||
| 4. Perlin noise in 2D.srt | 11.3 KB | ||
| 4. Poisson distribution.mp4 | 71.7 MB | ||
| 4. Poisson distribution.srt | 15.9 KB | ||
| 4. Repeating sine, square, and triangle waves.mp4 | 70.1 MB | ||
| 4. Repeating sine, square, and triangle waves.srt | 13.6 KB | ||
| 4. What is signal and what is noise.mp4 | 26 MB | ||
| 4. What is signal and what is noise.srt | 4.3 KB | ||
| 5. Filtered 2D-FFT noise.mp4 | 51.1 MB | ||
| 5. Filtered 2D-FFT noise.srt | 9.3 KB | ||
| 5. Log-normal distribution.mp4 | 40.9 MB | ||
| 5. Log-normal distribution.srt | 10.4 KB | ||
| 5. Multicomponent oscillators.mp4 | 33.8 MB | ||
| 5. Multicomponent oscillators.srt | 7.6 KB | ||
| 5. Multivariable correlated noise.mp4 | 65.2 MB | ||
| 5. Multivariable correlated noise.srt | 15.3 KB | ||
| 5. Rings.mp4 | 55 MB | ||
| 5. Rings.srt | 14 KB | ||
| 5. The importance of visualization.mp4 | 32.4 MB | ||
| 5. The importance of visualization.srt | 9.5 KB | ||
| 5. Violin plot.mp4 | 38.6 MB | ||
| 5. Violin plot.srt | 11.1 KB | ||
| 6. Dipolar and multipolar chirps.mp4 | 110.3 MB | ||
| 6. Dipolar and multipolar chirps.srt | 22.3 KB | ||
| 6. Measures of distribution quality (SNR and Fano factor).mp4 | 38.9 MB | ||
| 6. Measures of distribution quality (SNR and Fano factor).srt | 11.6 KB | ||
| 7. Cohen's d for separating distributions.mp4 | 52.1 MB | ||
| 7. Cohen's d for separating distributions.srt | 12.7 KB | ||
| Readme.txt | 921.6 B | ||
| [GigaCourse.com].url | 0 B | ||
| ▲ 93 total files | |||
Udemy - Generate and visualize data in Python and MATLAB
Generate and visualize data in Python and MATLAB Data science is quickly becoming one of the most important skills in industry, academia, marketing, and science. Most data-science courses teach analysis methods, but there are many methods; which method do you use for which data? The answer to that question comes from understanding data. That is the focus of this course.
For more Udemy Courses: https://gigacourse.com
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