Statistics and Mathematics for Data Science and Data Analytics

Table of contents

1. Chapter 1 : Let's Get Started
   1. Welcome!
   2. What Will You Learn in This Course?
   3. How Can You Get the Most Out of It?
2. Chapter 2 : Descriptive Statistics
   1. Introduction
   2. Mean
   3. Median
   4. Mode
   5. Mean or Median?
   6. Skewness
   7. Practice: Skewness
   8. Solution: Skewness
   9. Range and IQR
   10. Sample Versus Population
   11. Variance and Standard Deviation
   12. Impact of Scaling and Shifting
   13. Statistical Moments
3. Chapter 3 : Distributions
   1. What Is Distribution?
   2. Normal Distribution
   3. Z-Scores
   4. Practice: Normal Distribution
   5. Solution: Normal Distribution
4. Chapter 4 : Probability Theory
   1. Introduction
   2. Probability Basics
   3. Calculating Simple Probabilities
   4. Practice: Simple Probabilities
   5. Quick Solution: Simple Probabilities
   6. Detailed Solution: Simple Probabilities
   7. Rule of Addition
   8. Practice: Rule of Addition
   9. Quick Solution: Rule of Addition
   10. Detailed Solution: Rule of Addition
   11. Rule of Multiplication
   12. Practice: Rule of Multiplication
   13. Solution: Rule of Multiplication
   14. Bayes Theorem
   15. Bayes Theorem - Practical Example
   16. Expected Value
   17. Practice: Expected Value
   18. Solution: Expected Value
   19. Law of Large Numbers
   20. Central Limit Theorem - Theory
   21. Central Limit Theorem - Intuition
   22. Central Limit Theorem - Challenge
   23. Central Limit Theorem - Exercise
   24. Central Limit Theorem - Solution
   25. Binomial Distribution
   26. Poisson Distribution
   27. Real-Life Problems
5. Chapter 5 : Hypothesis Testing
   1. Introduction
   2. What Is a Hypothesis?
   3. Significance Level and P-Value
   4. Type I and Type II Errors
   5. Confidence Intervals and Margin of Error
   6. Excursion: Calculating Sample Size and Power
   7. Performing the Hypothesis Test
   8. Practice: Hypothesis Test
   9. Solution: Hypothesis Test
   10. t-test and t-distribution
   11. Proportion Testing
   12. Important p-z Pairs
6. Chapter 6 : Regressions
   1. Introduction
   2. Linear Regression
   3. Correlation Coefficient
   4. Practice: Correlation
   5. Solution: Correlation
   6. Practice: Linear Regression
   7. Solution: Linear Regression
   8. Residual, MSE, and MAE
   9. Practice: MSE and MAE
   10. Solution: MSE and MAE
   11. Coefficient of Determination
   12. Root Mean Square Error
   13. Practice: RMSE
   14. Solution: RMSE
7. Chapter 7 : Advanced Regression and Machine Learning Algorithms
   1. Multiple Linear Regression
   2. Overfitting
   3. Polynomial Regression
   4. Logistic Regression
   5. Decision Trees
   6. Regression Trees
   7. Random Forests
   8. Dealing with Missing Data
8. Chapter 8 : ANOVA (Analysis of Variance)
   1. ANOVA - Basics and Assumptions
   2. One-Way ANOVA
   3. F-Distribution
   4. Two-Way ANOVA – Sum of Squares
   5. Two-Way ANOVA – F-Ratio and Conclusions
9. Chapter 9 : Wrap Up
   1. Wrap Up