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[] Udemy - Probability for Statistics and Data Science

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  • 文件大小:3GB
  • 下载次数:31
  • 最近下载:2021-01-06 12:10:02
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文件列表

  1. 4. Distributions/29. Practical Example Distributions.mp4 157MB
  2. 3. Bayesian Inference/22. Practical Example Bayesian Inference.mp4 145MB
  3. 2. Combinatorics/20. Practical Example Combinatorics.mp4 134MB
  4. 5. Tie-ins to Other Fields/1. Tie-ins to Finance.mp4 99MB
  5. 4. Distributions/3. What are the two main types of distributions based on the type of data we have.mp4 92MB
  6. 1. Introduction to Probability/2. What is the probability formula.mp4 86MB
  7. 4. Distributions/15. What is a Continuous Distribution.mp4 84MB
  8. 5. Tie-ins to Other Fields/2. Tie-ins to Statistics.mp4 77MB
  9. 1. Introduction to Probability/4. How to compute expected values.mp4 76MB
  10. 4. Distributions/1. What is a probability distribution.mp4 73MB
  11. 4. Distributions/11. What is the Binomial Distribution.mp4 69MB
  12. 5. Tie-ins to Other Fields/3. Tie-ins to Data Science.mp4 63MB
  13. 1. Introduction to Probability/6. What is a probability frequency distribution.mp4 62MB
  14. 1. Introduction to Probability/8. What is a complement.mp4 59MB
  15. 2. Combinatorics/11. What are combinations and how are they similar to variations.mp4 57MB
  16. 3. Bayesian Inference/7. What is the union of sets A and B.mp4 57MB
  17. 4. Distributions/13. What is the Poisson Distribution.mp4 56MB
  18. 1. Introduction to Probability/1. What does the course cover.mp4 53MB
  19. 3. Bayesian Inference/20. When do we use Bayes' Theorem in Real Life.mp4 50MB
  20. 4. Distributions/27. What is the Logistic Distribution.mp4 50MB
  21. 3. Bayesian Inference/18. How do we derive the Multiplication Rule formula.mp4 49MB
  22. 4. Distributions/19. Standardizing a Normal Distribution.mp4 48MB
  23. 3. Bayesian Inference/3. What are the different ways two events can interact with one another.mp4 47MB
  24. 3. Bayesian Inference/13. What is the difference between P(AB) and P(BA).mp4 46MB
  25. 3. Bayesian Inference/1. What is a set.mp4 46MB
  26. 4. Distributions/17. What is a Normal Distribution.mp4 44MB
  27. 2. Combinatorics/9. What if we couldn't use certain values more than once.mp4 43MB
  28. 2. Combinatorics/3. When do we use Permutations.mp4 41MB
  29. 2. Combinatorics/17. What is the chance of a single ticket winning the lottery.mp4 41MB
  30. 2. Combinatorics/13. What is symmetry in Combinations.mp4 40MB
  31. 4. Distributions/25. What is an Exponential Distribution.mp4 40MB
  32. 2. Combinatorics/19. A Summary of Combinatorics.mp4 38MB
  33. 2. Combinatorics/5. Solving Factorials.mp4 36MB
  34. 3. Bayesian Inference/15. Conditional Probability in Real-Life.mp4 35MB
  35. 3. Bayesian Inference/11. What does it mean to for two events to be dependent.mp4 35MB
  36. 4. Distributions/9. What is the Bernoulli Distribution.mp4 34MB
  37. 2. Combinatorics/7. Why can we use certain values more than once.mp4 34MB
  38. 2. Combinatorics/15. How do we combine combinations of events with separate sample spaces.mp4 33MB
  39. 3. Bayesian Inference/16. How do we apply the additive rule.mp4 27MB
  40. 3. Bayesian Inference/5. What is the intersection of sets A and B.mp4 27MB
  41. 4. Distributions/23. What is a Chi Squared Distribution.mp4 26MB
  42. 3. Bayesian Inference/9. Are all complements mutually exclusive.mp4 25MB
  43. 4. Distributions/7. What is the Discrete Uniform Distribution.mp4 24MB
  44. 4. Distributions/5. Discrete Distributions and their characteristics..srt 23MB
  45. 4. Distributions/5. Discrete Distributions and their characteristics..mp4 23MB
  46. 4. Distributions/21. What is a Student's T Distribution.mp4 22MB
  47. 2. Combinatorics/1. Why are combinatorics useful.mp4 16MB
  48. 4. Distributions/29.3 FIFA19.csv 9MB
  49. 4. Distributions/29.6 FIFA19 (post).csv 9MB
  50. 3. Bayesian Inference/22.2 CDS_2017-2018 Hamilton.pdf 845KB
  51. 4. Distributions/1.1 Course Notes - Probability Distributions.pdf 448KB
  52. 3. Bayesian Inference/1.1 Section 3 Course Notes.pdf 386KB
  53. 1. Introduction to Probability/2.1 Section 1 Course Notes.pdf 371KB
  54. 4. Distributions/15.1 Solving Integrals.pdf 344KB
  55. 2. Combinatorics/20.2 Additional Exercises Combinatorics Solutions.pdf 246KB
  56. 2. Combinatorics/1.1 Section 2 Course Notes.pdf 226KB
  57. 2. Combinatorics/11.1 Combinations With Repetition.pdf 224KB
  58. 5. Tie-ins to Other Fields/1.2 Probability in Finance Solutions.pdf 184KB
  59. 4. Distributions/13.1 Poisson - Expected Value and Variance.pdf 146KB
  60. 4. Distributions/17.1 Normal Distribution - Expected Value and Variance.pdf 144KB
  61. 5. Tie-ins to Other Fields/1.1 Probability in Finance Homework.pdf 111KB
  62. 2. Combinatorics/20.1 Additional Exercises Combinatorics.pdf 107KB
  63. 2. Combinatorics/13.1 Symmetry Explained.pdf 85KB
  64. 3. Bayesian Inference/22.3 Bayesian Homework - Solutions.pdf 30KB
  65. 3. Bayesian Inference/22.1 Bayesian Homework .pdf 27KB
  66. 4. Distributions/29.2 Daily Views (post).xlsx 20KB
  67. 4. Distributions/29. Practical Example Distributions.srt 20KB
  68. 3. Bayesian Inference/22. Practical Example Bayesian Inference.srt 19KB
  69. 4. Distributions/29.4 Customers_Membership (post).xlsx 16KB
  70. 2. Combinatorics/20. Practical Example Combinatorics.srt 14KB
  71. 5. Tie-ins to Other Fields/1. Tie-ins to Finance.srt 10KB
  72. 4. Distributions/29.1 Customers_Membership.xlsx 10KB
  73. 4. Distributions/29.5 Daily Views.xlsx 10KB
  74. 4. Distributions/3. What are the two main types of distributions based on the type of data we have.srt 9KB
  75. 1. Introduction to Probability/2. What is the probability formula.srt 9KB
  76. 4. Distributions/15. What is a Continuous Distribution.srt 9KB
  77. 5. Tie-ins to Other Fields/2. Tie-ins to Statistics.srt 8KB
  78. 4. Distributions/11. What is the Binomial Distribution.srt 8KB
  79. 4. Distributions/1. What is a probability distribution.srt 8KB
  80. 3. Bayesian Inference/20. When do we use Bayes' Theorem in Real Life.srt 7KB
  81. 1. Introduction to Probability/8. What is a complement.srt 7KB
  82. 1. Introduction to Probability/4. How to compute expected values.srt 7KB
  83. 5. Tie-ins to Other Fields/3. Tie-ins to Data Science.srt 7KB
  84. 4. Distributions/13. What is the Poisson Distribution.srt 7KB
  85. 1. Introduction to Probability/6. What is a probability frequency distribution.srt 6KB
  86. 1. Introduction to Probability/1. What does the course cover.srt 6KB
  87. 2. Combinatorics/11. What are combinations and how are they similar to variations.srt 6KB
  88. 3. Bayesian Inference/7. What is the union of sets A and B.srt 6KB
  89. 4. Distributions/19. Standardizing a Normal Distribution.srt 5KB
  90. 3. Bayesian Inference/1. What is a set.srt 5KB
  91. 4. Distributions/27. What is the Logistic Distribution.srt 5KB
  92. 3. Bayesian Inference/13. What is the difference between P(AB) and P(BA).srt 5KB
  93. 4. Distributions/17. What is a Normal Distribution.srt 5KB
  94. 3. Bayesian Inference/18. How do we derive the Multiplication Rule formula.srt 5KB
  95. 2. Combinatorics/9. What if we couldn't use certain values more than once.srt 5KB
  96. 3. Bayesian Inference/3. What are the different ways two events can interact with one another.srt 4KB
  97. 2. Combinatorics/13. What is symmetry in Combinations.srt 4KB
  98. 2. Combinatorics/17. What is the chance of a single ticket winning the lottery.srt 4KB
  99. 4. Distributions/25. What is an Exponential Distribution.srt 4KB
  100. 2. Combinatorics/3. When do we use Permutations.srt 4KB
  101. 4. Distributions/9. What is the Bernoulli Distribution.srt 4KB
  102. 2. Combinatorics/15. How do we combine combinations of events with separate sample spaces.srt 4KB
  103. 2. Combinatorics/19. A Summary of Combinatorics.srt 4KB
  104. 3. Bayesian Inference/15. Conditional Probability in Real-Life.srt 3KB
  105. 2. Combinatorics/7. Why can we use certain values more than once.srt 3KB
  106. 3. Bayesian Inference/11. What does it mean to for two events to be dependent.srt 3KB
  107. 2. Combinatorics/5. Solving Factorials.srt 3KB
  108. 4. Distributions/21. What is a Student's T Distribution.srt 3KB
  109. 4. Distributions/23. What is a Chi Squared Distribution.srt 3KB
  110. 3. Bayesian Inference/16. How do we apply the additive rule.srt 3KB
  111. 4. Distributions/7. What is the Discrete Uniform Distribution.srt 3KB
  112. 3. Bayesian Inference/9. Are all complements mutually exclusive.srt 3KB
  113. 3. Bayesian Inference/5. What is the intersection of sets A and B.srt 2KB
  114. 2. Combinatorics/1. Why are combinatorics useful.srt 1KB
  115. Readme.txt 962B
  116. 1. Introduction to Probability/3. What is the probability formula.html 154B
  117. 1. Introduction to Probability/5. How to compute expected values.html 154B
  118. 1. Introduction to Probability/7. What is a probability frequency distribution.html 154B
  119. 1. Introduction to Probability/9. What is a complement.html 154B
  120. 2. Combinatorics/10. Computing Variations without Repetition.html 154B
  121. 2. Combinatorics/12. What are combinations and how are they similar to variations.html 154B
  122. 2. Combinatorics/14. What is symmetry in Combinations.html 154B
  123. 2. Combinatorics/16. How do we combine combinations of events with separate sample spaces.html 154B
  124. 2. Combinatorics/18. What is the chance of winning the lottery.html 154B
  125. 2. Combinatorics/2. Why are combinatorics useful.html 154B
  126. 2. Combinatorics/4. When do we use Permutations.html 154B
  127. 2. Combinatorics/6. Solving Factorials.html 154B
  128. 2. Combinatorics/8. Why can we use certain values more than once.html 154B
  129. 3. Bayesian Inference/10. Are all complements mutually exclusive.html 154B
  130. 3. Bayesian Inference/12. What does it mean to for two events to be dependent.html 154B
  131. 3. Bayesian Inference/14. What is the difference between P(AB) and P(BA).html 154B
  132. 3. Bayesian Inference/17. How do we apply the additive rule.html 154B
  133. 3. Bayesian Inference/19. How do we interpret the Multiplication Rule Formula.html 154B
  134. 3. Bayesian Inference/2. What is a set.html 154B
  135. 3. Bayesian Inference/21. Bayes' Theorem.html 154B
  136. 3. Bayesian Inference/4. What are the different ways two events can interact with one another.html 154B
  137. 3. Bayesian Inference/6. What is the intersection of sets A and B.html 154B
  138. 3. Bayesian Inference/8. What is the union of sets A and B.html 154B
  139. 4. Distributions/10. What is the Bernoulli Distribution.html 154B
  140. 4. Distributions/12. What is the Binomial Distribution.html 154B
  141. 4. Distributions/14. What is the Poisson Distribution.html 154B
  142. 4. Distributions/16. What is a Continuous Distribution.html 154B
  143. 4. Distributions/18. What is a Normal Distribution.html 154B
  144. 4. Distributions/2. What is a probability distribution.html 154B
  145. 4. Distributions/20. How do we Standardize a Normal Distribution.html 154B
  146. 4. Distributions/22. What is a Student's T Distribution.html 154B
  147. 4. Distributions/24. What is a Chi-Squared Distribution.html 154B
  148. 4. Distributions/26. What is an Exponential Distribution.html 154B
  149. 4. Distributions/28. What is a Logistic Distribution.html 154B
  150. 4. Distributions/4. What are the two main types of distributions based on the type of data we have.html 154B
  151. 4. Distributions/6. Discrete Distributions and Their Characteristics..html 154B
  152. 4. Distributions/8. What is the Discrete Uniform Distribution.html 154B
  153. [GigaCourse.com].url 49B