| 1. Introductions | |||
| 1. Overall goals of this course.mp4 | 7.79 MB | ||
| 1. Overall goals of this course.vtt | 4.63 KB | ||
| 2. Why and how to simulate data.mp4 | 8.9 MB | ||
| 2. Why and how to simulate data.vtt | 6.32 KB | ||
| 3. What is signal and what is noise.mp4 | 8.45 MB | ||
| 3. What is signal and what is noise.vtt | 3.93 KB | ||
| 4. The importance of visualization.mp4 | 11.44 MB | ||
| 4. The importance of visualization.vtt | 8 KB | ||
| 10. How to become a proactive data scientist | |||
| 1. Proactive vs. reactive data science.mp4 | 6.23 MB | ||
| 1. Proactive vs. reactive data science.vtt | 4.42 KB | ||
| 2. Understand data origins and features.mp4 | 5.36 MB | ||
| 2. Understand data origins and features.vtt | 4.36 KB | ||
| 3. Write down or sketch the important results.mp4 | 8.58 MB | ||
| 3. Write down or sketch the important results.vtt | 5.16 KB | ||
| 4. Don't give up -- every mistake is a learning opportunity!.mp4 | 4.68 MB | ||
| 4. Don't give up -- every mistake is a learning opportunity!.vtt | 2.67 KB | ||
| 11. Conclusions and how to learn more | |||
| 1. Conclusions and how to learn more.mp4 | 4.92 MB | ||
| 1. Conclusions and how to learn more.vtt | 3.16 KB | ||
| 12. Discount coupon for related courses | |||
| 1. Join the community!.html | 553 B | ||
| 2. Bonus Links to related courses.html | 2.53 KB | ||
| 2. Descriptive statistics and basic visualizations | |||
| 1. Course materials for this section (reader, MATLAB code, Python code).html | 68 B | ||
| 1.1 prodata_descriptiveVisualizations.zip.zip | 237.28 KB | ||
| 2. Mean, median, standard deviation, variance.mp4 | 12.33 MB | ||
| 2. Mean, median, standard deviation, variance.vtt | 8.23 KB | ||
| 3. Interquartile range.mp4 | 8.25 MB | ||
| 3. Interquartile range.vtt | 4.54 KB | ||
| 4. Histogram.mp4 | 6.39 MB | ||
| 4. Histogram.vtt | 3.86 KB | ||
| 5. Violin plot.mp4 | 8.71 MB | ||
| 5. Violin plot.vtt | 5.65 KB | ||
| 3. Data distributions | |||
| 1. Course materials for this section (reader, MATLAB code, Python code).html | 76 B | ||
| 1.1 prodata_dataDistributions.zip.zip | 305.14 KB | ||
| 2. Normal and uniform distributions.mp4 | 14.6 MB | ||
| 2. Normal and uniform distributions.vtt | 8.53 KB | ||
| 3. QQ plot.mp4 | 10.85 MB | ||
| 3. QQ plot.vtt | 7.15 KB | ||
| 4. Poisson distribution.mp4 | 12.75 MB | ||
| 4. Poisson distribution.vtt | 7.01 KB | ||
| 5. Log-normal distribution.mp4 | 6.34 MB | ||
| 5. Log-normal distribution.vtt | 3.87 KB | ||
| 6. Measures of distribution quality (SNR and Fano factor).mp4 | 6.67 MB | ||
| 6. Measures of distribution quality (SNR and Fano factor).vtt | 4.43 KB | ||
| 7. Cohen's d for separating distributions.mp4 | 10.69 MB | ||
| 7. Cohen's d for separating distributions.vtt | 6.59 KB | ||
| 4. Time series signals | |||
| 1. Course materials for this section (reader, MATLAB code, Python code).html | 66 B | ||
| 1.1 prodata_TimeSeriesSignals.zip.zip | 653.05 KB | ||
| 2. Sharp transients.mp4 | 8.87 MB | ||
| 2. Sharp transients.vtt | 5.18 KB | ||
| 3. Smooth transients.mp4 | 19.86 MB | ||
| 3. Smooth transients.vtt | 11.75 KB | ||
| 4. Repeating sine, square, and triangle waves.mp4 | 8.31 MB | ||
| 4. Repeating sine, square, and triangle waves.vtt | 3.92 KB | ||
| 5. Multicomponent oscillators.mp4 | 6.21 MB | ||
| 5. Multicomponent oscillators.vtt | 3.61 KB | ||
| 6. Dipolar and multipolar chirps.mp4 | 15.43 MB | ||
| 6. Dipolar and multipolar chirps.vtt | 8.58 KB | ||
| 5. Time series noise | |||
| 1. Course materials for this section (reader, MATLAB code, Python code).html | 69 B | ||
| 1.1 prodata_TimeSeriesNoise.zip.zip | 474.12 KB | ||
| 2. Seeded reproducible normal and uniform noise.mp4 | 9.59 MB | ||
| 2. Seeded reproducible normal and uniform noise.vtt | 5.24 KB | ||
| 3. Pink noise (aka 1f aka fractal).mp4 | 12.12 MB | ||
| 3. Pink noise (aka 1f aka fractal).vtt | 6.53 KB | ||
| 4. Brownian noise (aka random walk).mp4 | 8.04 MB | ||
| 4. Brownian noise (aka random walk).vtt | 4.59 KB | ||
| 5. Multivariable correlated noise.mp4 | 13.22 MB | ||
| 5. Multivariable correlated noise.vtt | 7.91 KB | ||
| 6. Image signals | |||
| 1. Course materials for this section (reader, MATLAB code, Python code).html | 70 B | ||
| 1.1 prodata_imageSignals.zip.zip | 263.57 KB | ||
| 2. Lines and edges.mp4 | 6.51 MB | ||
| 2. Lines and edges.vtt | 3.74 KB | ||
| 3. Sine patches and Gabor patches.mp4 | 9.18 MB | ||
| 3. Sine patches and Gabor patches.vtt | 4.85 KB | ||
| 4. Geometric shapes.mp4 | 7.3 MB | ||
| 4. Geometric shapes.vtt | 3.38 KB | ||
| 5. Rings.mp4 | 3.82 MB | ||
| 5. Rings.vtt | 2.88 KB | ||
| 7. Image noise | |||
| 1. Course materials for this section (reader, MATLAB code, Python code).html | 72 B | ||
| 1.1 prodata_imageNoise.zip.zip | 654.15 KB | ||
| 2. Image white noise.mp4 | 5.06 MB | ||
| 2. Image white noise.vtt | 2.8 KB | ||
| 3. Checkerboard patterns and noise.mp4 | 5.15 MB | ||
| 3. Checkerboard patterns and noise.vtt | 3.27 KB | ||
| 4. Perlin noise in 2D.mp4 | 9.93 MB | ||
| 4. Perlin noise in 2D.vtt | 4.63 KB | ||
| 5. Filtered 2D-FFT noise.mp4 | 8.48 MB | ||
| 5. Filtered 2D-FFT noise.vtt | 3.97 KB | ||
| 8. Data clustering in space | |||
| 1. Course materials for this section (reader, MATLAB code, Python code).html | 73 B | ||
| 1.1 prodata_dataClusters.zip.zip | 279.1 KB | ||
| 2. Clusters in 2D.mp4 | 10.93 MB | ||
| 2. Clusters in 2D.vtt | 6.55 KB | ||
| 3. Clusters in N-D.mp4 | 8.95 MB | ||
| 3. Clusters in N-D.vtt | 2.05 KB | ||
| 9. Spatiotemporal structure using forward models | |||
| 1. Course materials for this section (reader, MATLAB code, Python code).html | 116 B | ||
| 1.1 prodata_forwardModels.zip.zip | 4.19 MB | ||
| 2. Forward model 2D sheet.mp4 | 31.41 MB | ||
| 2. Forward model 2D sheet.vtt | 9.29 KB | ||
| 3. Mixed overlapping forward models.mp4 | 18.41 MB | ||
| 3. Mixed overlapping forward models.vtt | 4.99 KB | ||
| 4. Example Simulate human brain (EEG) data.mp4 | 33.73 MB | ||
| 4. Example Simulate human brain (EEG) data.vtt | 15.49 KB | ||
| [Tutorialsplanet.NET].url | 128 B |
Udemy - Simulate, understand, & visualize data like a data scientist [TP]
Learn how to simulate and visualize data for data science, statistics, and machine learning in MATLAB and Python
For more Udemy Courses: https://tutorialsplanet.net
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