[Tutorialsplanet.NET] Udemy - Simulate, understand, & visualize data like a data scientist
File List
- 9. Spatiotemporal structure using forward models/4. Example Simulate human brain (EEG) data.mp4 33.7 MB
- 9. Spatiotemporal structure using forward models/2. Forward model 2D sheet.mp4 31.4 MB
- 4. Time series signals/3. Smooth transients.mp4 19.9 MB
- 9. Spatiotemporal structure using forward models/3. Mixed overlapping forward models.mp4 18.4 MB
- 4. Time series signals/6. Dipolar and multipolar chirps.mp4 15.4 MB
- 3. Data distributions/2. Normal and uniform distributions.mp4 14.6 MB
- 5. Time series noise/5. Multivariable correlated noise.mp4 13.2 MB
- 3. Data distributions/4. Poisson distribution.mp4 12.7 MB
- 2. Descriptive statistics and basic visualizations/2. Mean, median, standard deviation, variance.mp4 12.3 MB
- 5. Time series noise/3. Pink noise (aka 1f aka fractal).mp4 12.1 MB
- 1. Introductions/4. The importance of visualization.mp4 11.4 MB
- 8. Data clustering in space/2. Clusters in 2D.mp4 10.9 MB
- 3. Data distributions/3. QQ plot.mp4 10.9 MB
- 3. Data distributions/7. Cohen's d for separating distributions.mp4 10.7 MB
- 7. Image noise/4. Perlin noise in 2D.mp4 9.9 MB
- 5. Time series noise/2. Seeded reproducible normal and uniform noise.mp4 9.6 MB
- 6. Image signals/3. Sine patches and Gabor patches.mp4 9.2 MB
- 8. Data clustering in space/3. Clusters in N-D.mp4 8.9 MB
- 1. Introductions/2. Why and how to simulate data.mp4 8.9 MB
- 4. Time series signals/2. Sharp transients.mp4 8.9 MB
- 2. Descriptive statistics and basic visualizations/5. Violin plot.mp4 8.7 MB
- 10. How to become a proactive data scientist/3. Write down or sketch the important results.mp4 8.6 MB
- 7. Image noise/5. Filtered 2D-FFT noise.mp4 8.5 MB
- 1. Introductions/3. What is signal and what is noise.mp4 8.4 MB
- 4. Time series signals/4. Repeating sine, square, and triangle waves.mp4 8.3 MB
- 2. Descriptive statistics and basic visualizations/3. Interquartile range.mp4 8.2 MB
- 5. Time series noise/4. Brownian noise (aka random walk).mp4 8.0 MB
- 1. Introductions/1. Overall goals of this course.mp4 7.8 MB
- 6. Image signals/4. Geometric shapes.mp4 7.3 MB
- 3. Data distributions/6. Measures of distribution quality (SNR and Fano factor).mp4 6.7 MB
- 6. Image signals/2. Lines and edges.mp4 6.5 MB
- 2. Descriptive statistics and basic visualizations/4. Histogram.mp4 6.4 MB
- 3. Data distributions/5. Log-normal distribution.mp4 6.3 MB
- 10. How to become a proactive data scientist/1. Proactive vs. reactive data science.mp4 6.2 MB
- 4. Time series signals/5. Multicomponent oscillators.mp4 6.2 MB
- 10. How to become a proactive data scientist/2. Understand data origins and features.mp4 5.4 MB
- 7. Image noise/3. Checkerboard patterns and noise.mp4 5.2 MB
- 7. Image noise/2. Image white noise.mp4 5.1 MB
- 11. Conclusions and how to learn more/1. Conclusions and how to learn more.mp4 4.9 MB
- 10. How to become a proactive data scientist/4. Don't give up -- every mistake is a learning opportunity!.mp4 4.7 MB
- 9. Spatiotemporal structure using forward models/1.1 prodata_forwardModels.zip.zip 4.2 MB
- 6. Image signals/5. Rings.mp4 3.8 MB
- 7. Image noise/1.1 prodata_imageNoise.zip.zip 654.2 KB
- 4. Time series signals/1.1 prodata_TimeSeriesSignals.zip.zip 653.1 KB
- 5. Time series noise/1.1 prodata_TimeSeriesNoise.zip.zip 474.1 KB
- 3. Data distributions/1.1 prodata_dataDistributions.zip.zip 305.1 KB
- 8. Data clustering in space/1.1 prodata_dataClusters.zip.zip 279.1 KB
- 6. Image signals/1.1 prodata_imageSignals.zip.zip 263.6 KB
- 2. Descriptive statistics and basic visualizations/1.1 prodata_descriptiveVisualizations.zip.zip 237.3 KB
- 9. Spatiotemporal structure using forward models/4. Example Simulate human brain (EEG) data.vtt 15.5 KB
- 4. Time series signals/3. Smooth transients.vtt 11.7 KB
- 9. Spatiotemporal structure using forward models/2. Forward model 2D sheet.vtt 9.3 KB
- 4. Time series signals/6. Dipolar and multipolar chirps.vtt 8.6 KB
- 3. Data distributions/2. Normal and uniform distributions.vtt 8.5 KB
- 2. Descriptive statistics and basic visualizations/2. Mean, median, standard deviation, variance.vtt 8.2 KB
- 1. Introductions/4. The importance of visualization.vtt 8.0 KB
- 5. Time series noise/5. Multivariable correlated noise.vtt 7.9 KB
- 3. Data distributions/3. QQ plot.vtt 7.1 KB
- 3. Data distributions/4. Poisson distribution.vtt 7.0 KB
- 3. Data distributions/7. Cohen's d for separating distributions.vtt 6.6 KB
- 8. Data clustering in space/2. Clusters in 2D.vtt 6.5 KB
- 5. Time series noise/3. Pink noise (aka 1f aka fractal).vtt 6.5 KB
- 1. Introductions/2. Why and how to simulate data.vtt 6.3 KB
- 2. Descriptive statistics and basic visualizations/5. Violin plot.vtt 5.7 KB
- 5. Time series noise/2. Seeded reproducible normal and uniform noise.vtt 5.2 KB
- 4. Time series signals/2. Sharp transients.vtt 5.2 KB
- 10. How to become a proactive data scientist/3. Write down or sketch the important results.vtt 5.2 KB
- 9. Spatiotemporal structure using forward models/3. Mixed overlapping forward models.vtt 5.0 KB
- 6. Image signals/3. Sine patches and Gabor patches.vtt 4.9 KB
- 7. Image noise/4. Perlin noise in 2D.vtt 4.6 KB
- 1. Introductions/1. Overall goals of this course.vtt 4.6 KB
- 5. Time series noise/4. Brownian noise (aka random walk).vtt 4.6 KB
- 2. Descriptive statistics and basic visualizations/3. Interquartile range.vtt 4.5 KB
- 3. Data distributions/6. Measures of distribution quality (SNR and Fano factor).vtt 4.4 KB
- 10. How to become a proactive data scientist/1. Proactive vs. reactive data science.vtt 4.4 KB
- 10. How to become a proactive data scientist/2. Understand data origins and features.vtt 4.4 KB
- 7. Image noise/5. Filtered 2D-FFT noise.vtt 4.0 KB
- 1. Introductions/3. What is signal and what is noise.vtt 3.9 KB
- 4. Time series signals/4. Repeating sine, square, and triangle waves.vtt 3.9 KB
- 3. Data distributions/5. Log-normal distribution.vtt 3.9 KB
- 2. Descriptive statistics and basic visualizations/4. Histogram.vtt 3.9 KB
- 6. Image signals/2. Lines and edges.vtt 3.7 KB
- 4. Time series signals/5. Multicomponent oscillators.vtt 3.6 KB
- 6. Image signals/4. Geometric shapes.vtt 3.4 KB
- 7. Image noise/3. Checkerboard patterns and noise.vtt 3.3 KB
- 11. Conclusions and how to learn more/1. Conclusions and how to learn more.vtt 3.2 KB
- 6. Image signals/5. Rings.vtt 2.9 KB
- 7. Image noise/2. Image white noise.vtt 2.8 KB
- 10. How to become a proactive data scientist/4. Don't give up -- every mistake is a learning opportunity!.vtt 2.7 KB
- 12. Discount coupon for related courses/2. Bonus Links to related courses.html 2.5 KB
- 8. Data clustering in space/3. Clusters in N-D.vtt 2.1 KB
- 12. Discount coupon for related courses/1. Join the community!.html 553 bytes
- [Tutorialsplanet.NET].url 128 bytes
- 9. Spatiotemporal structure using forward models/1. Course materials for this section (reader, MATLAB code, Python code).html 116 bytes
- 3. Data distributions/1. Course materials for this section (reader, MATLAB code, Python code).html 76 bytes
- 8. Data clustering in space/1. Course materials for this section (reader, MATLAB code, Python code).html 73 bytes
- 7. Image noise/1. Course materials for this section (reader, MATLAB code, Python code).html 72 bytes
- 6. Image signals/1. Course materials for this section (reader, MATLAB code, Python code).html 70 bytes
- 5. Time series noise/1. Course materials for this section (reader, MATLAB code, Python code).html 69 bytes
- 2. Descriptive statistics and basic visualizations/1. Course materials for this section (reader, MATLAB code, Python code).html 68 bytes
- 4. Time series signals/1. Course materials for this section (reader, MATLAB code, Python code).html 66 bytes
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