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[] Udemy - Simulate, understand, & visualize data like a data scientist

  • 收录时间:2020-04-29 13:10:53
  • 文件大小:432MB
  • 下载次数:28
  • 最近下载:2021-01-10 12:33:17
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文件列表

  1. 9. Spatiotemporal structure using forward models/4. Example Simulate human brain (EEG) data.mp4 34MB
  2. 9. Spatiotemporal structure using forward models/2. Forward model 2D sheet.mp4 31MB
  3. 4. Time series signals/3. Smooth transients.mp4 20MB
  4. 9. Spatiotemporal structure using forward models/3. Mixed overlapping forward models.mp4 18MB
  5. 4. Time series signals/6. Dipolar and multipolar chirps.mp4 15MB
  6. 3. Data distributions/2. Normal and uniform distributions.mp4 15MB
  7. 5. Time series noise/5. Multivariable correlated noise.mp4 13MB
  8. 3. Data distributions/4. Poisson distribution.mp4 13MB
  9. 2. Descriptive statistics and basic visualizations/2. Mean, median, standard deviation, variance.mp4 12MB
  10. 5. Time series noise/3. Pink noise (aka 1f aka fractal).mp4 12MB
  11. 1. Introductions/4. The importance of visualization.mp4 11MB
  12. 8. Data clustering in space/2. Clusters in 2D.mp4 11MB
  13. 3. Data distributions/3. QQ plot.mp4 11MB
  14. 3. Data distributions/7. Cohen's d for separating distributions.mp4 11MB
  15. 7. Image noise/4. Perlin noise in 2D.mp4 10MB
  16. 5. Time series noise/2. Seeded reproducible normal and uniform noise.mp4 10MB
  17. 6. Image signals/3. Sine patches and Gabor patches.mp4 9MB
  18. 8. Data clustering in space/3. Clusters in N-D.mp4 9MB
  19. 1. Introductions/2. Why and how to simulate data.mp4 9MB
  20. 4. Time series signals/2. Sharp transients.mp4 9MB
  21. 2. Descriptive statistics and basic visualizations/5. Violin plot.mp4 9MB
  22. 10. How to become a proactive data scientist/3. Write down or sketch the important results.mp4 9MB
  23. 7. Image noise/5. Filtered 2D-FFT noise.mp4 8MB
  24. 1. Introductions/3. What is signal and what is noise.mp4 8MB
  25. 4. Time series signals/4. Repeating sine, square, and triangle waves.mp4 8MB
  26. 2. Descriptive statistics and basic visualizations/3. Interquartile range.mp4 8MB
  27. 5. Time series noise/4. Brownian noise (aka random walk).mp4 8MB
  28. 1. Introductions/1. Overall goals of this course.mp4 8MB
  29. 6. Image signals/4. Geometric shapes.mp4 7MB
  30. 3. Data distributions/6. Measures of distribution quality (SNR and Fano factor).mp4 7MB
  31. 6. Image signals/2. Lines and edges.mp4 7MB
  32. 2. Descriptive statistics and basic visualizations/4. Histogram.mp4 6MB
  33. 3. Data distributions/5. Log-normal distribution.mp4 6MB
  34. 10. How to become a proactive data scientist/1. Proactive vs. reactive data science.mp4 6MB
  35. 4. Time series signals/5. Multicomponent oscillators.mp4 6MB
  36. 10. How to become a proactive data scientist/2. Understand data origins and features.mp4 5MB
  37. 7. Image noise/3. Checkerboard patterns and noise.mp4 5MB
  38. 7. Image noise/2. Image white noise.mp4 5MB
  39. 11. Conclusions and how to learn more/1. Conclusions and how to learn more.mp4 5MB
  40. 10. How to become a proactive data scientist/4. Don't give up -- every mistake is a learning opportunity!.mp4 5MB
  41. 9. Spatiotemporal structure using forward models/1.1 prodata_forwardModels.zip.zip 4MB
  42. 6. Image signals/5. Rings.mp4 4MB
  43. 7. Image noise/1.1 prodata_imageNoise.zip.zip 654KB
  44. 4. Time series signals/1.1 prodata_TimeSeriesSignals.zip.zip 653KB
  45. 5. Time series noise/1.1 prodata_TimeSeriesNoise.zip.zip 474KB
  46. 3. Data distributions/1.1 prodata_dataDistributions.zip.zip 305KB
  47. 8. Data clustering in space/1.1 prodata_dataClusters.zip.zip 279KB
  48. 6. Image signals/1.1 prodata_imageSignals.zip.zip 264KB
  49. 2. Descriptive statistics and basic visualizations/1.1 prodata_descriptiveVisualizations.zip.zip 237KB
  50. 9. Spatiotemporal structure using forward models/4. Example Simulate human brain (EEG) data.vtt 15KB
  51. 4. Time series signals/3. Smooth transients.vtt 12KB
  52. 9. Spatiotemporal structure using forward models/2. Forward model 2D sheet.vtt 9KB
  53. 4. Time series signals/6. Dipolar and multipolar chirps.vtt 9KB
  54. 3. Data distributions/2. Normal and uniform distributions.vtt 9KB
  55. 2. Descriptive statistics and basic visualizations/2. Mean, median, standard deviation, variance.vtt 8KB
  56. 1. Introductions/4. The importance of visualization.vtt 8KB
  57. 5. Time series noise/5. Multivariable correlated noise.vtt 8KB
  58. 3. Data distributions/3. QQ plot.vtt 7KB
  59. 3. Data distributions/4. Poisson distribution.vtt 7KB
  60. 3. Data distributions/7. Cohen's d for separating distributions.vtt 7KB
  61. 8. Data clustering in space/2. Clusters in 2D.vtt 7KB
  62. 5. Time series noise/3. Pink noise (aka 1f aka fractal).vtt 7KB
  63. 1. Introductions/2. Why and how to simulate data.vtt 6KB
  64. 2. Descriptive statistics and basic visualizations/5. Violin plot.vtt 6KB
  65. 5. Time series noise/2. Seeded reproducible normal and uniform noise.vtt 5KB
  66. 4. Time series signals/2. Sharp transients.vtt 5KB
  67. 10. How to become a proactive data scientist/3. Write down or sketch the important results.vtt 5KB
  68. 9. Spatiotemporal structure using forward models/3. Mixed overlapping forward models.vtt 5KB
  69. 6. Image signals/3. Sine patches and Gabor patches.vtt 5KB
  70. 7. Image noise/4. Perlin noise in 2D.vtt 5KB
  71. 1. Introductions/1. Overall goals of this course.vtt 5KB
  72. 5. Time series noise/4. Brownian noise (aka random walk).vtt 5KB
  73. 2. Descriptive statistics and basic visualizations/3. Interquartile range.vtt 5KB
  74. 3. Data distributions/6. Measures of distribution quality (SNR and Fano factor).vtt 4KB
  75. 10. How to become a proactive data scientist/1. Proactive vs. reactive data science.vtt 4KB
  76. 10. How to become a proactive data scientist/2. Understand data origins and features.vtt 4KB
  77. 7. Image noise/5. Filtered 2D-FFT noise.vtt 4KB
  78. 1. Introductions/3. What is signal and what is noise.vtt 4KB
  79. 4. Time series signals/4. Repeating sine, square, and triangle waves.vtt 4KB
  80. 3. Data distributions/5. Log-normal distribution.vtt 4KB
  81. 2. Descriptive statistics and basic visualizations/4. Histogram.vtt 4KB
  82. 6. Image signals/2. Lines and edges.vtt 4KB
  83. 4. Time series signals/5. Multicomponent oscillators.vtt 4KB
  84. 6. Image signals/4. Geometric shapes.vtt 3KB
  85. 7. Image noise/3. Checkerboard patterns and noise.vtt 3KB
  86. 11. Conclusions and how to learn more/1. Conclusions and how to learn more.vtt 3KB
  87. 6. Image signals/5. Rings.vtt 3KB
  88. 7. Image noise/2. Image white noise.vtt 3KB
  89. 10. How to become a proactive data scientist/4. Don't give up -- every mistake is a learning opportunity!.vtt 3KB
  90. 12. Discount coupon for related courses/2. Bonus Links to related courses.html 3KB
  91. 8. Data clustering in space/3. Clusters in N-D.vtt 2KB
  92. 12. Discount coupon for related courses/1. Join the community!.html 553B
  93. [Tutorialsplanet.NET].url 128B
  94. 9. Spatiotemporal structure using forward models/1. Course materials for this section (reader, MATLAB code, Python code).html 116B
  95. 3. Data distributions/1. Course materials for this section (reader, MATLAB code, Python code).html 76B
  96. 8. Data clustering in space/1. Course materials for this section (reader, MATLAB code, Python code).html 73B
  97. 7. Image noise/1. Course materials for this section (reader, MATLAB code, Python code).html 72B
  98. 6. Image signals/1. Course materials for this section (reader, MATLAB code, Python code).html 70B
  99. 5. Time series noise/1. Course materials for this section (reader, MATLAB code, Python code).html 69B
  100. 2. Descriptive statistics and basic visualizations/1. Course materials for this section (reader, MATLAB code, Python code).html 68B
  101. 4. Time series signals/1. Course materials for this section (reader, MATLAB code, Python code).html 66B