Statistics is the discipline that concerns the collection, organization, analysis, interpretation and presentation of data. In applying #statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. This course is the comprehensive explanation of all statistics which very crucial for data science as well.

⭐️ Table of Contents ⭐️
⌨️ (0:00) Lesson 1: Getting started with statistics
⌨️ (16:57) Lesson 2: Data Classification
⌨️ (40:32) Lesson 3: The process of statistical study
⌨️ (1:05:30) Lesson 4: Frequency distribution
⌨️ (1:28:48) Lesson 5: Graphical displays of data
⌨️ (2:05:34) Lesson 6: Analyzing graph
⌨️ (2:17:25) Lesson 7: Measures of Center
⌨️ (2:48:20) Lesson 8: Measures of Dispersion
⌨️ (3:19:27) Lesson 9: Measures of relative position
⌨️ (3:44::09) Lesson 10: Introduction to probability
⌨️ (4:02:15) Lesson 11: Addition rules for probability
⌨️ (4:16:7) Lesson 12: Multiplication rules for probability
⌨️ (4:33:18) Lesson 13: Combinations and permutations
⌨️ (4:46:11) Lesson 14: Combining probability and counting techniques
⌨️ (4:57:09) Lesson 15: Discreate distribution
⌨️ (5:21:08) Lesson 16: The binomial distribution
⌨️ (5:43:10) Lesson 17: The poisson distribution
⌨️ (6:01:15) Lesson 18: The hypergeometric
⌨️ (6:21:10) Lesson 19: The uniform distribution
⌨️ (6:46:59) Lesson 20: The exponential distribution
⌨️ (7:02:01) Lesson 21: The normal distribution
⌨️ (7:21:06) Lesson 22: Approximating the binomial
⌨️ (7:42:36) Lesson 23: The central limit theorem
⌨️ (7:56:54) Lesson 24: The distribution of sample mean
⌨️ (8:22:03) Lesson 25: The distribution of sample proportion
⌨️ (8:41:50) Lesson 26: Confidence interval
⌨️ (9:09:32) Lesson 27: The theory of hypothesis testing
⌨️ (9:53:50) Lesson 28: Handling proportions
⌨️ (10:21:38) Lesson 29: Discrete distributing matching
⌨️ (10:50:05) Lesson 30: Categorical independence
⌨️ (11:11:53) Lesson 31: Analysis of variance