Udemy - Statistics For Data Science And Business Analysis

Udemy.Statistics.for.Data.Science.and.Business.Analysis-UDUMMY
Title:   Statistics for Data Science and Business Analysis
URL:     https://www.udemy.com/course/statistics-for-data-science-and-business-analysis/
Length:  4:48:26
Subs:    de-DE, en-US, es-ES, fr-FR, id-ID, it-IT, pl-PL, pt-BR, ro-RO
Notes:
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Headline:
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Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis
Contents:
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1. Introduction
 - 1. What does the course cover? => 03:54
 - 2. Download all resources => Article (not included)
2. Sample or population data?
 - 3. Understanding the difference between a population and a sample => 04:02
3. The fundamentals of descriptive statistics
 - 4. The various types of data we can work with => 04:33
 - 5. Levels of measurement => 03:43
 - 6. Categorical variables. Visualization techniques for categorical variables => 04:52
 - 7. Categorical variables. Visualization techniques. Exercise => Article (not included)
 - 8. Numerical variables. Using a frequency distribution table => 03:09
 - 9. Numerical variables. Using a frequency distribution table. Exercise => Article (not included)
 - 10. Histogram charts => 02:14
 - 11. Histogram charts. Exercise => Article (not included)
 - 12. Cross tables and scatter plots => 04:44
 - 13. Cross tables and scatter plots. Exercise => Article (not included)
4. Measures of central tendency, asymmetry, and variability
 - 14. The main measures of central tendency: mean, median and mode => 04:20
 - 15. Mean, median and mode. Exercise => Article (not included)
 - 16. Measuring skewness => 02:37
 - 17. Skewness. Exercise => Article (not included)
 - 18. Measuring how data is spread out: calculating variance => 05:55
 - 19. Variance. Exercise => Article (not included)
 - 20. Standard deviation and coefficient of variation => 04:40
 - 21. Standard deviation and coefficient of variation. Exercise => Article (not included)
 - 22. Calculating and understanding covariance => 03:23
 - 23. Covariance. Exercise => Article (not included)
 - 24. The correlation coefficient => 03:17
 - 25. Correlation coefficient => Article (not included)
5. Practical example: descriptive statistics
 - 26. Practical example => 16:15
 - 27. Practical example: descriptive statistics => Article (not included)
6. Distributions
 - 28. Introduction to inferential statistics => 01:00
 - 29. What is a distribution? => 04:33
 - 30. The Normal distribution => 03:54
 - 31. The standard normal distribution => 03:30
 - 32. Standard Normal Distribution. Exercise => Article (not included)
 - 33. Understanding the central limit theorem => 04:20
 - 34. Standard error => 01:27
7. Estimators and estimates
 - 35. Working with estimators and estimates => 03:07
 - 36. Confidence intervals - an invaluable tool for decision making => 02:41
 - 39. Confidence interval clarifications => 04:38
 - 40. Student's T distribution => 03:22
 - 41. Calculating confidence intervals within a population with an unknown variance => 04:36
 - 42. Population variance unknown. T-score. Exercise => Article (not included)
 - 43. What is a margin of error and why is it important in Statistics? => 04:52
8. Confidence intervals: advanced topics
 - 44. Calculating confidence intervals for two means with dependent samples => 06:04
 - 45. Confidence intervals. Two means. Dependent samples. Exercise => Article (not included)
 - 46. Calculating confidence intervals for two means with independent samples (part 1) => 04:31
 - 47. Confidence intervals. Two means. Independent samples (Part 1). Exercise => Article (not included)
 - 48. Calculating confidence intervals for two means with independent samples (part 2) => 03:57
 - 49. Confidence intervals. Two means. Independent samples (Part 2). Exercise => Article (not included)
 - 50. Calculating confidence intervals for two means with independent samples (part 3) => 01:27
9. Practical example: inferential statistics
 - 51. Practical example: inferential statistics => 10:06
 - 52. Practical example: inferential statistics => Article (not included)
10. Hypothesis testing: Introduction
 - 53. The null and the alternative hypothesis => 05:52
 - 54. Further reading on null and alternative hypotheses => Article (not included)
 - 55. Establishing a rejection region and a significance level => 07:05
 - 56. Type I error vs Type II error => 04:14
11. Hypothesis testing: Let's start testing!
 - 59. What is the p-value and why is it one of the most useful tools for statisticians => 04:13
 - 60. Test for the mean. Population variance unknown => 04:48
 - 61. Test for the mean. Population variance unknown. Exercise => Article (not included)
 - 62. Test for the mean. Dependent samples => 05:18
 - 63. Test for the mean. Dependent samples. Exercise => Article (not included)
 - 64. Test for the mean. Independent samples (Part 1) => 04:22
 - 65. Test for the mean. Independent samples (Part 1) => Article (not included)
 - 66. Test for the mean. Independent samples (Part 2) => 04:26
 - 67. Test for the mean. Independent samples (Part 2). Exercise => Article (not included)
12. Practical example: hypothesis testing
 - 68. Practical example: hypothesis testing => 07:16
 - 69. Practical example: hypothesis testing => Article (not included)
13. The fundamentals of regression analysis
 - 70. Introduction to regression analysis => 01:02
 - 71. Correlation and causation => 04:12
 - 72. The linear regression model made easy => 05:50
 - 73. What is the difference between correlation and regression? => 01:43
 - 74. A geometrical representation of the linear regression model => 01:25
 - 75. A practical example - Reinforced learning => 05:45
14. Subtleties of regression analysis
 - 76. Decomposing the linear regression model - understanding its nuts and bolts => 03:37
 - 77. What is R-squared and how does it help us? => 05:24
 - 78. The ordinary least squares setting and its practical applications => 02:23
 - 79. Studying regression tables => 04:54
 - 80. Regression tables. Exercise => Article (not included)
 - 81. The multiple linear regression model => 02:55
 - 82. The adjusted R-squared => 05:24
 - 83. What does the F-statistic show us and why do we need to understand it? => 02:01
15. Assumptions for linear regression analysis
 - 84. OLS assumptions => 02:21
 - 85. A1. Linearity => 01:50
 - 86. A2. No endogeneity => 04:09
 - 87. A3. Normality and homoscedasticity => 05:47
 - 88. A4. No autocorrelation => 03:14
 - 89. A5. No multicollinearity => 03:26
16. Dealing with categorical data
 - 90. Dummy variables => 05:03
17. Practical example: regression analysis
 - 91. Practical example: regression analysis => 14:09
18. Bonus lecture
 - 92. Bonus lecture: Next steps => Article (not included)

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