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