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udemy-probability-for-statistics-and-data-science

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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..mp4 23MB
  45. 4. Distributions/21. What is a Student's T Distribution.mp4 22MB
  46. 2. Combinatorics/1. Why are combinatorics useful.mp4 16MB
  47. 3. Bayesian Inference/22.1 CDS_2017-2018 Hamilton_jp2.zip 13MB
  48. 4. Distributions/29.3 FIFA19 (post).csv 9MB
  49. 4. Distributions/29.4 FIFA19.csv 9MB
  50. 4. Distributions/1.1 Course Notes - Probability Distributions_jp2.zip 8MB
  51. 1. Introduction to Probability/2.1 Section 1 Course Notes_jp2.zip 4MB
  52. 4. Distributions/15.1 Solving Integrals_jp2.zip 3MB
  53. 3. Bayesian Inference/1.1 Section 3 Course Notes_jp2.zip 2MB
  54. 2. Combinatorics/20.2 Additional Exercises Combinatorics Solutions_jp2.zip 2MB
  55. 2. Combinatorics/11.1 Combinations With Repetition_jp2.zip 2MB
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  248. .____padding_file/209 1MB
  249. 2. Combinatorics/1.1 Section 2 Course Notes_jp2.zip 1MB
  250. 4. Distributions/17.1 Normal Distribution - Expected Value and Variance_jp2.zip 1MB
  251. .____padding_file/124 1MB
  252. .____padding_file/33 1MB
  253. 3. Bayesian Inference/22.1 CDS_2017-2018 Hamilton_abbyy.gz 1MB
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  263. 5. Tie-ins to Other Fields/1.2 Probability in Finance Solutions_jp2.zip 1MB
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  271. 3. Bayesian Inference/22.1 CDS_2017-2018 Hamilton.pdf 845KB
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  275. 2. Combinatorics/20.1 Additional Exercises Combinatorics_jp2.zip 789KB
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  287. 4. Distributions/13.1 Poisson - Expected Value and Variance_jp2.zip 496KB
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  289. 4. Distributions/1.1 Course Notes - Probability Distributions.pdf 448KB
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  292. 3. Bayesian Inference/1.1 Section 3 Course Notes.pdf 386KB
  293. 5. Tie-ins to Other Fields/1.1 Probability in Finance Homework_jp2.zip 381KB
  294. 1. Introduction to Probability/2.1 Section 1 Course Notes.pdf 371KB
  295. 2. Combinatorics/13.1 Symmetry Explained_jp2.zip 360KB
  296. 4. Distributions/15.1 Solving Integrals.pdf 344KB
  297. .____padding_file/228 266KB
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  299. 2. Combinatorics/20.2 Additional Exercises Combinatorics Solutions.pdf 246KB
  300. 2. Combinatorics/1.1 Section 2 Course Notes.pdf 226KB
  301. 2. Combinatorics/11.1 Combinations With Repetition.pdf 224KB
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  304. 2. Combinatorics/20.2 Additional Exercises Combinatorics Solutions_abbyy.gz 185KB
  305. 5. Tie-ins to Other Fields/1.2 Probability in Finance Solutions.pdf 184KB
  306. 2. Combinatorics/20.2 Additional Exercises Combinatorics Solutions_djvu.xml 162KB
  307. .____padding_file/36 159KB
  308. 4. Distributions/13.1 Poisson - Expected Value and Variance.pdf 146KB
  309. 4. Distributions/17.1 Normal Distribution - Expected Value and Variance.pdf 144KB
  310. 3. Bayesian Inference/22.2 Bayesian Homework _jp2.zip 141KB
  311. .____padding_file/225 136KB
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  315. 5. Tie-ins to Other Fields/1.1 Probability in Finance Homework.pdf 111KB
  316. 4. Distributions/17.1 Normal Distribution - Expected Value and Variance_djvu.xml 109KB
  317. 2. Combinatorics/20.1 Additional Exercises Combinatorics.pdf 107KB
  318. 4. Distributions/15.1 Solving Integrals_djvu.xml 104KB
  319. 3. Bayesian Inference/1.1 Section 3 Course Notes_abbyy.gz 101KB
  320. 3. Bayesian Inference/1.1 Section 3 Course Notes_djvu.xml 97KB
  321. 3. Bayesian Inference/22.1 CDS_2017-2018 Hamilton_djvu.txt 92KB
  322. 2. Combinatorics/11.1 Combinations With Repetition_abbyy.gz 92KB
  323. 4. Distributions/15.1 Solving Integrals_abbyy.gz 91KB
  324. 2. Combinatorics/13.1 Symmetry Explained.pdf 85KB
  325. 2. Combinatorics/11.1 Combinations With Repetition_djvu.xml 83KB
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  328. 4. Distributions/17.1 Normal Distribution - Expected Value and Variance_abbyy.gz 77KB
  329. 1. Introduction to Probability/2.1 Section 1 Course Notes_djvu.xml 71KB
  330. 2. Combinatorics/1.1 Section 2 Course Notes_djvu.xml 70KB
  331. 2. Combinatorics/1.1 Section 2 Course Notes_abbyy.gz 66KB
  332. 2. Combinatorics/20.1 Additional Exercises Combinatorics_abbyy.gz 58KB
  333. 4. Distributions/13.1 Poisson - Expected Value and Variance_djvu.xml 53KB
  334. 2. Combinatorics/20.1 Additional Exercises Combinatorics_djvu.xml 47KB
  335. 4. Distributions/13.1 Poisson - Expected Value and Variance_abbyy.gz 42KB
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  337. 3. Bayesian Inference/22.3 Bayesian Homework - Solutions_abbyy.gz 34KB
  338. 3. Bayesian Inference/22.3 Bayesian Homework - Solutions.pdf 30KB
  339. .____padding_file/138 29KB
  340. 5. Tie-ins to Other Fields/1.2 Probability in Finance Solutions_abbyy.gz 27KB
  341. 3. Bayesian Inference/22.2 Bayesian Homework .pdf 27KB
  342. 3. Bayesian Inference/22.3 Bayesian Homework - Solutions_djvu.xml 26KB
  343. 5. Tie-ins to Other Fields/1.2 Probability in Finance Solutions_djvu.xml 25KB
  344. 4. Distributions/29.2 Daily Views (post).xlsx 20KB
  345. Udemy - Probability for Statistics and Data Science_torrent.txt 20KB
  346. 4. Distributions/29. Practical Example Distributions.srt 20KB
  347. 3. Bayesian Inference/22. Practical Example Bayesian Inference.srt 19KB
  348. .____padding_file/195 17KB
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  350. 2. Combinatorics/20.2 Additional Exercises Combinatorics Solutions_djvu.txt 14KB
  351. 2. Combinatorics/20. Practical Example Combinatorics.srt 14KB
  352. 2. Combinatorics/13.1 Symmetry Explained_djvu.xml 13KB
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  354. 5. Tie-ins to Other Fields/1. Tie-ins to Finance.srt 10KB
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  358. 4. Distributions/3. What are the two main types of distributions based on the type of data we have.srt 9KB
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  360. 1. Introduction to Probability/2. What is the probability formula.srt 9KB
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  362. 4. Distributions/15. What is a Continuous Distribution.srt 9KB
  363. 5. Tie-ins to Other Fields/2. Tie-ins to Statistics.srt 8KB
  364. 4. Distributions/11. What is the Binomial Distribution.srt 8KB
  365. 3. Bayesian Inference/22.2 Bayesian Homework _abbyy.gz 8KB
  366. 4. Distributions/1. What is a probability distribution.srt 8KB
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  368. 3. Bayesian Inference/20. When do we use Bayes' Theorem in Real Life.srt 7KB
  369. 1. Introduction to Probability/8. What is a complement.srt 7KB
  370. 1. Introduction to Probability/4. How to compute expected values.srt 7KB
  371. 5. Tie-ins to Other Fields/1.1 Probability in Finance Homework_djvu.xml 7KB
  372. 5. Tie-ins to Other Fields/3. Tie-ins to Data Science.srt 7KB
  373. 4. Distributions/13. What is the Poisson Distribution.srt 7KB
  374. 1. Introduction to Probability/6. What is a probability frequency distribution.srt 6KB
  375. 2. Combinatorics/11.1 Combinations With Repetition_djvu.txt 6KB
  376. 3. Bayesian Inference/1.1 Section 3 Course Notes_djvu.txt 6KB
  377. 1. Introduction to Probability/1. What does the course cover.srt 6KB
  378. 2. Combinatorics/11. What are combinations and how are they similar to variations.srt 6KB
  379. 3. Bayesian Inference/7. What is the union of sets A and B.srt 6KB
  380. 4. Distributions/19. Standardizing a Normal Distribution.srt 5KB
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  382. 3. Bayesian Inference/1. What is a set.srt 5KB
  383. 4. Distributions/15.1 Solving Integrals_djvu.txt 5KB
  384. 4. Distributions/27. What is the Logistic Distribution.srt 5KB
  385. 5. Tie-ins to Other Fields/1.1 Probability in Finance Homework_abbyy.gz 5KB
  386. 3. Bayesian Inference/13. What is the difference between P(AB) and P(BA).srt 5KB
  387. 4. Distributions/17. What is a Normal Distribution.srt 5KB
  388. 3. Bayesian Inference/18. How do we derive the Multiplication Rule formula.srt 5KB
  389. 2. Combinatorics/20.1 Additional Exercises Combinatorics_djvu.txt 5KB
  390. 2. Combinatorics/9. What if we couldn't use certain values more than once.srt 5KB
  391. 3. Bayesian Inference/3. What are the different ways two events can interact with one another.srt 4KB
  392. 4. Distributions/1.1 Course Notes - Probability Distributions_scandata.xml 4KB
  393. 2. Combinatorics/13. What is symmetry in Combinations.srt 4KB
  394. 2. Combinatorics/17. What is the chance of a single ticket winning the lottery.srt 4KB
  395. 2. Combinatorics/1.1 Section 2 Course Notes_djvu.txt 4KB
  396. 4. Distributions/17.1 Normal Distribution - Expected Value and Variance_djvu.txt 4KB
  397. 4. Distributions/25. What is an Exponential Distribution.srt 4KB
  398. 2. Combinatorics/3. When do we use Permutations.srt 4KB
  399. 4. Distributions/9. What is the Bernoulli Distribution.srt 4KB
  400. 2. Combinatorics/15. How do we combine combinations of events with separate sample spaces.srt 4KB
  401. 2. Combinatorics/19. A Summary of Combinatorics.srt 4KB
  402. 3. Bayesian Inference/15. Conditional Probability in Real-Life.srt 3KB
  403. 2. Combinatorics/7. Why can we use certain values more than once.srt 3KB
  404. 3. Bayesian Inference/11. What does it mean to for two events to be dependent.srt 3KB
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  407. 2. Combinatorics/5. Solving Factorials.srt 3KB
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  410. 4. Distributions/13.1 Poisson - Expected Value and Variance_djvu.txt 3KB
  411. 4. Distributions/23. What is a Chi Squared Distribution.srt 3KB
  412. 3. Bayesian Inference/16. How do we apply the additive rule.srt 3KB
  413. 4. Distributions/7. What is the Discrete Uniform Distribution.srt 3KB
  414. 3. Bayesian Inference/9. Are all complements mutually exclusive.srt 3KB
  415. 3. Bayesian Inference/22.3 Bayesian Homework - Solutions_djvu.txt 3KB
  416. 3. Bayesian Inference/5. What is the intersection of sets A and B.srt 2KB
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