[GigaCourse.com] Udemy - Probability for Statistics and Data Science

Creation Time	April 21, 2025, 4:47 a.m.
Last Access Time	Nov. 7, 2025, 9:26 p.m.
File Size	2.5 GB
Keywords	and Statistics Probability for Data - Udemy GigaCourse Science com
Total Requests	63
Total Files	153
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4. Distributions/29. Practical Example Distributions.mp4 157.5 MB
3. Bayesian Inference/22. Practical Example Bayesian Inference.mp4 145.0 MB
2. Combinatorics/20. Practical Example Combinatorics.mp4 134.4 MB
5. Tie-ins to Other Fields/1. Tie-ins to Finance.mp4 98.8 MB
4. Distributions/3. What are the two main types of distributions based on the type of data we have.mp4 91.6 MB
1. Introduction to Probability/2. What is the probability formula.mp4 85.9 MB
4. Distributions/15. What is a Continuous Distribution.mp4 84.2 MB
5. Tie-ins to Other Fields/2. Tie-ins to Statistics.mp4 77.2 MB
1. Introduction to Probability/4. How to compute expected values.mp4 75.7 MB
4. Distributions/1. What is a probability distribution.mp4 73.4 MB
4. Distributions/11. What is the Binomial Distribution.mp4 68.9 MB
5. Tie-ins to Other Fields/3. Tie-ins to Data Science.mp4 63.5 MB
1. Introduction to Probability/6. What is a probability frequency distribution.mp4 61.6 MB
1. Introduction to Probability/8. What is a complement.mp4 59.1 MB
2. Combinatorics/11. What are combinations and how are they similar to variations.mp4 57.3 MB
3. Bayesian Inference/7. What is the union of sets A and B.mp4 57.2 MB
4. Distributions/13. What is the Poisson Distribution.mp4 55.8 MB
1. Introduction to Probability/1. What does the course cover.mp4 52.7 MB
3. Bayesian Inference/20. When do we use Bayes' Theorem in Real Life.mp4 50.0 MB
4. Distributions/27. What is the Logistic Distribution.mp4 50.0 MB
3. Bayesian Inference/18. How do we derive the Multiplication Rule formula.mp4 49.0 MB
4. Distributions/19. Standardizing a Normal Distribution.mp4 47.9 MB
3. Bayesian Inference/3. What are the different ways two events can interact with one another.mp4 47.4 MB
3. Bayesian Inference/13. What is the difference between P(AB) and P(BA).mp4 45.8 MB
3. Bayesian Inference/1. What is a set.mp4 45.5 MB
4. Distributions/17. What is a Normal Distribution.mp4 43.7 MB
2. Combinatorics/9. What if we couldn't use certain values more than once.mp4 43.1 MB
2. Combinatorics/3. When do we use Permutations.mp4 41.5 MB
2. Combinatorics/17. What is the chance of a single ticket winning the lottery.mp4 41.3 MB
2. Combinatorics/13. What is symmetry in Combinations.mp4 40.3 MB
4. Distributions/25. What is an Exponential Distribution.mp4 40.2 MB
2. Combinatorics/19. A Summary of Combinatorics.mp4 38.3 MB
2. Combinatorics/5. Solving Factorials.mp4 36.1 MB
3. Bayesian Inference/15. Conditional Probability in Real-Life.mp4 34.9 MB
3. Bayesian Inference/11. What does it mean to for two events to be dependent.mp4 34.8 MB
4. Distributions/9. What is the Bernoulli Distribution.mp4 34.1 MB
2. Combinatorics/7. Why can we use certain values more than once.mp4 34.0 MB
2. Combinatorics/15. How do we combine combinations of events with separate sample spaces.mp4 33.0 MB
3. Bayesian Inference/16. How do we apply the additive rule.mp4 26.9 MB
3. Bayesian Inference/5. What is the intersection of sets A and B.mp4 26.9 MB
4. Distributions/23. What is a Chi Squared Distribution.mp4 26.4 MB
3. Bayesian Inference/9. Are all complements mutually exclusive.mp4 25.4 MB
4. Distributions/7. What is the Discrete Uniform Distribution.mp4 24.4 MB
4. Distributions/5. Discrete Distributions and their characteristics..srt 22.7 MB
4. Distributions/5. Discrete Distributions and their characteristics..mp4 22.7 MB
4. Distributions/21. What is a Student's T Distribution.mp4 22.0 MB
2. Combinatorics/1. Why are combinatorics useful.mp4 16.2 MB
4. Distributions/29.3 FIFA19.csv 8.6 MB
4. Distributions/29.6 FIFA19 (post).csv 8.6 MB
3. Bayesian Inference/22.2 CDS_2017-2018 Hamilton.pdf 845.3 KB
4. Distributions/1.1 Course Notes - Probability Distributions.pdf 448.1 KB
3. Bayesian Inference/1.1 Section 3 Course Notes.pdf 386.0 KB
1. Introduction to Probability/2.1 Section 1 Course Notes.pdf 371.1 KB
4. Distributions/15.1 Solving Integrals.pdf 343.9 KB
2. Combinatorics/20.2 Additional Exercises Combinatorics Solutions.pdf 245.7 KB
2. Combinatorics/1.1 Section 2 Course Notes.pdf 226.1 KB
2. Combinatorics/11.1 Combinations With Repetition.pdf 223.8 KB
5. Tie-ins to Other Fields/1.2 Probability in Finance Solutions.pdf 184.5 KB
4. Distributions/13.1 Poisson - Expected Value and Variance.pdf 146.0 KB
4. Distributions/17.1 Normal Distribution - Expected Value and Variance.pdf 144.1 KB
5. Tie-ins to Other Fields/1.1 Probability in Finance Homework.pdf 110.7 KB
2. Combinatorics/20.1 Additional Exercises Combinatorics.pdf 106.6 KB
2. Combinatorics/13.1 Symmetry Explained.pdf 85.0 KB
3. Bayesian Inference/22.3 Bayesian Homework - Solutions.pdf 30.4 KB
3. Bayesian Inference/22.1 Bayesian Homework .pdf 27.3 KB
4. Distributions/29.2 Daily Views (post).xlsx 20.2 KB
4. Distributions/29. Practical Example Distributions.srt 19.9 KB
3. Bayesian Inference/22. Practical Example Bayesian Inference.srt 19.3 KB
4. Distributions/29.4 Customers_Membership (post).xlsx 15.6 KB
2. Combinatorics/20. Practical Example Combinatorics.srt 14.0 KB
5. Tie-ins to Other Fields/1. Tie-ins to Finance.srt 9.8 KB
4. Distributions/29.1 Customers_Membership.xlsx 9.7 KB
4. Distributions/29.5 Daily Views.xlsx 9.5 KB
4. Distributions/3. What are the two main types of distributions based on the type of data we have.srt 9.5 KB
1. Introduction to Probability/2. What is the probability formula.srt 8.9 KB
4. Distributions/15. What is a Continuous Distribution.srt 8.7 KB
5. Tie-ins to Other Fields/2. Tie-ins to Statistics.srt 8.4 KB
4. Distributions/11. What is the Binomial Distribution.srt 8.3 KB
4. Distributions/1. What is a probability distribution.srt 7.5 KB
3. Bayesian Inference/20. When do we use Bayes' Theorem in Real Life.srt 7.2 KB
1. Introduction to Probability/8. What is a complement.srt 6.7 KB
1. Introduction to Probability/4. How to compute expected values.srt 6.7 KB
5. Tie-ins to Other Fields/3. Tie-ins to Data Science.srt 6.6 KB
4. Distributions/13. What is the Poisson Distribution.srt 6.6 KB
1. Introduction to Probability/6. What is a probability frequency distribution.srt 6.4 KB
1. Introduction to Probability/1. What does the course cover.srt 5.6 KB
2. Combinatorics/11. What are combinations and how are they similar to variations.srt 5.6 KB
3. Bayesian Inference/7. What is the union of sets A and B.srt 5.5 KB
4. Distributions/19. Standardizing a Normal Distribution.srt 5.3 KB
3. Bayesian Inference/1. What is a set.srt 5.1 KB
4. Distributions/27. What is the Logistic Distribution.srt 5.1 KB
3. Bayesian Inference/13. What is the difference between P(AB) and P(BA).srt 4.9 KB
4. Distributions/17. What is a Normal Distribution.srt 4.7 KB
3. Bayesian Inference/18. How do we derive the Multiplication Rule formula.srt 4.6 KB
2. Combinatorics/9. What if we couldn't use certain values more than once.srt 4.5 KB
3. Bayesian Inference/3. What are the different ways two events can interact with one another.srt 4.4 KB
2. Combinatorics/13. What is symmetry in Combinations.srt 4.3 KB
2. Combinatorics/17. What is the chance of a single ticket winning the lottery.srt 4.1 KB
4. Distributions/25. What is an Exponential Distribution.srt 4.1 KB
2. Combinatorics/3. When do we use Permutations.srt 4.1 KB
4. Distributions/9. What is the Bernoulli Distribution.srt 3.9 KB
2. Combinatorics/15. How do we combine combinations of events with separate sample spaces.srt 3.7 KB
2. Combinatorics/19. A Summary of Combinatorics.srt 3.7 KB
3. Bayesian Inference/15. Conditional Probability in Real-Life.srt 3.5 KB
2. Combinatorics/7. Why can we use certain values more than once.srt 3.5 KB
3. Bayesian Inference/11. What does it mean to for two events to be dependent.srt 3.5 KB
2. Combinatorics/5. Solving Factorials.srt 3.3 KB
4. Distributions/21. What is a Student's T Distribution.srt 2.8 KB
4. Distributions/23. What is a Chi Squared Distribution.srt 2.8 KB
3. Bayesian Inference/16. How do we apply the additive rule.srt 2.7 KB
4. Distributions/7. What is the Discrete Uniform Distribution.srt 2.7 KB
3. Bayesian Inference/9. Are all complements mutually exclusive.srt 2.5 KB
3. Bayesian Inference/5. What is the intersection of sets A and B.srt 2.5 KB
2. Combinatorics/1. Why are combinatorics useful.srt 1.3 KB
Readme.txt 962 bytes
1. Introduction to Probability/3. What is the probability formula.html 154 bytes
1. Introduction to Probability/5. How to compute expected values.html 154 bytes
1. Introduction to Probability/7. What is a probability frequency distribution.html 154 bytes
1. Introduction to Probability/9. What is a complement.html 154 bytes
2. Combinatorics/10. Computing Variations without Repetition.html 154 bytes
2. Combinatorics/12. What are combinations and how are they similar to variations.html 154 bytes
2. Combinatorics/14. What is symmetry in Combinations.html 154 bytes
2. Combinatorics/16. How do we combine combinations of events with separate sample spaces.html 154 bytes
2. Combinatorics/18. What is the chance of winning the lottery.html 154 bytes
2. Combinatorics/2. Why are combinatorics useful.html 154 bytes
2. Combinatorics/4. When do we use Permutations.html 154 bytes
2. Combinatorics/6. Solving Factorials.html 154 bytes
2. Combinatorics/8. Why can we use certain values more than once.html 154 bytes
3. Bayesian Inference/10. Are all complements mutually exclusive.html 154 bytes
3. Bayesian Inference/12. What does it mean to for two events to be dependent.html 154 bytes
3. Bayesian Inference/14. What is the difference between P(AB) and P(BA).html 154 bytes
3. Bayesian Inference/17. How do we apply the additive rule.html 154 bytes
3. Bayesian Inference/19. How do we interpret the Multiplication Rule Formula.html 154 bytes
3. Bayesian Inference/2. What is a set.html 154 bytes
3. Bayesian Inference/21. Bayes' Theorem.html 154 bytes
3. Bayesian Inference/4. What are the different ways two events can interact with one another.html 154 bytes
3. Bayesian Inference/6. What is the intersection of sets A and B.html 154 bytes
3. Bayesian Inference/8. What is the union of sets A and B.html 154 bytes
4. Distributions/10. What is the Bernoulli Distribution.html 154 bytes
4. Distributions/12. What is the Binomial Distribution.html 154 bytes
4. Distributions/14. What is the Poisson Distribution.html 154 bytes
4. Distributions/16. What is a Continuous Distribution.html 154 bytes
4. Distributions/18. What is a Normal Distribution.html 154 bytes
4. Distributions/2. What is a probability distribution.html 154 bytes
4. Distributions/20. How do we Standardize a Normal Distribution.html 154 bytes
4. Distributions/22. What is a Student's T Distribution.html 154 bytes
4. Distributions/24. What is a Chi-Squared Distribution.html 154 bytes
4. Distributions/26. What is an Exponential Distribution.html 154 bytes
4. Distributions/28. What is a Logistic Distribution.html 154 bytes
4. Distributions/4. What are the two main types of distributions based on the type of data we have.html 154 bytes
4. Distributions/6. Discrete Distributions and Their Characteristics..html 154 bytes
4. Distributions/8. What is the Discrete Uniform Distribution.html 154 bytes
[GigaCourse.com].url 49 bytes

[GigaCourse.com] Udemy - Probability for Statistics and Data Science

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