GigaCourse com Udemy - Probability for Statistics and Data Science

1. 1. Introduction to Probability/1. What does the course cover.mp452.67MB
2. 1. Introduction to Probability/2. What is the probability formula.mp485.87MB
3. 1. Introduction to Probability/4. How to compute expected values.mp475.73MB
4. 1. Introduction to Probability/6. What is a probability frequency distribution.mp461.6MB
5. 1. Introduction to Probability/8. What is a complement.mp459.1MB
6. 2. Combinatorics/1. Why are combinatorics useful.mp416.17MB
7. 2. Combinatorics/11. What are combinations and how are they similar to variations.mp457.3MB
8. 2. Combinatorics/13. What is symmetry in Combinations.mp440.27MB
9. 2. Combinatorics/15. How do we combine combinations of events with separate sample spaces.mp433.04MB
10. 2. Combinatorics/17. What is the chance of a single ticket winning the lottery.mp441.31MB
11. 2. Combinatorics/19. A Summary of Combinatorics.mp438.3MB
12. 2. Combinatorics/20. Practical Example Combinatorics.mp4134.37MB
13. 2. Combinatorics/3. When do we use Permutations.mp441.48MB
14. 2. Combinatorics/5. Solving Factorials.mp436.14MB
15. 2. Combinatorics/7. Why can we use certain values more than once.mp433.96MB
16. 2. Combinatorics/9. What if we couldn't use certain values more than once.mp443.07MB
17. 3. Bayesian Inference/1. What is a set.mp445.51MB
18. 3. Bayesian Inference/11. What does it mean to for two events to be dependent.mp434.78MB
19. 3. Bayesian Inference/13. What is the difference between P(AB) and P(BA).mp445.84MB
20. 3. Bayesian Inference/15. Conditional Probability in Real-Life.mp434.9MB
21. 3. Bayesian Inference/16. How do we apply the additive rule.mp426.94MB
22. 3. Bayesian Inference/18. How do we derive the Multiplication Rule formula.mp449.05MB
23. 3. Bayesian Inference/20. When do we use Bayes' Theorem in Real Life.mp450MB
24. 3. Bayesian Inference/22. Practical Example Bayesian Inference.mp4144.95MB
25. 3. Bayesian Inference/3. What are the different ways two events can interact with one another.mp447.44MB
26. 3. Bayesian Inference/5. What is the intersection of sets A and B.mp426.92MB
27. 3. Bayesian Inference/7. What is the union of sets A and B.mp457.2MB
28. 3. Bayesian Inference/9. Are all complements mutually exclusive.mp425.39MB
29. 4. Distributions/1. What is a probability distribution.mp473.35MB
30. 4. Distributions/11. What is the Binomial Distribution.mp468.88MB
31. 4. Distributions/13. What is the Poisson Distribution.mp455.79MB
32. 4. Distributions/15. What is a Continuous Distribution.mp484.16MB
33. 4. Distributions/17. What is a Normal Distribution.mp443.74MB
34. 4. Distributions/19. Standardizing a Normal Distribution.mp447.91MB
35. 4. Distributions/21. What is a Student's T Distribution.mp421.99MB
36. 4. Distributions/23. What is a Chi Squared Distribution.mp426.37MB
37. 4. Distributions/25. What is an Exponential Distribution.mp440.16MB
38. 4. Distributions/27. What is the Logistic Distribution.mp449.98MB
39. 4. Distributions/29. Practical Example Distributions.mp4157.46MB
40. 4. Distributions/3. What are the two main types of distributions based on the type of data we have.mp491.61MB
41. 4. Distributions/5. Discrete Distributions and their characteristics..mp422.68MB
42. 4. Distributions/7. What is the Discrete Uniform Distribution.mp424.39MB
43. 4. Distributions/9. What is the Bernoulli Distribution.mp434.15MB
44. 5. Tie-ins to Other Fields/1. Tie-ins to Finance.mp498.82MB
45. 5. Tie-ins to Other Fields/2. Tie-ins to Statistics.mp477.19MB
46. 5. Tie-ins to Other Fields/3. Tie-ins to Data Science.mp463.46MB