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Motivation to do Track 11
This learning path is designed to develop the following skills:
The journey map of Track 11
Badges
Sample
P-value
Test of Hypothesis
Normal Distrib,
Student T Distrib,
Non-parametric tests
A/B
Testing
Real data
1. Concepts & Definitions
1.1. A Review on Parametric Statistics
1.2. Parametric tests for Hypothesis Testing
1.3. Parametric vs. Non-Parametric Test
1.4. One sample z-test and their relation with two-sample z-test
1.5. One sample t-test and their relation with two-sample t-test
1.6. Welch's two-sample t-test: two populations with different variances
1.7. Non-Parametric test for Hypothesis Testing: Mann-Whitney U Test
1.8. Non-Parametric test for Hypothesis Testing: Wilcoxon Sign-Rank Test
1.9. Non-Parametric test for Hypothesis Testing: Wilcoxon Sign Test
1.10. Non-Parametric test for Hypothesis Testing: Chi-Square Goodness-of-Fit
1.11. Non-Parametric test for Hypothesis Testing: Kolmogorov-Smirnov
1.12. Non-Parametric for comparing machine learning
2. Problem & Solution
2.1. Using Wilcoxon Sign Test to compare clustering methods
2.2. Using Wilcoxon Sign-Rank Test to compare clustering methods
2.3. What is A/B testing and how to combine with hypothesis testing?
2.4. Using Chi-Square fit to check if Benford-Law holds or not
2.5. Using Kolmogorov-Smirnov fit to check if Pareto principle holds or not
1. Concepts & Definitions
2. Problem & Solution
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