Let's Get Started

• Welcome!
• What Will You Learn in This Course?
• How Can You Get the Most Out of It?

Descriptive Statistics

• Introduction
• Mean
• Median
• Mode
• Mean or Median?
• Skewness
• Practice: Skewness
• Solution: Skewness
• Range and IQR
• Sample Versus Population
• Variance and Standard Deviation
• Impact of Scaling and Shifting
• Statistical Moments

Distributions

• What Is Distribution?
• Normal Distribution
• Z-Scores
• Practice: Normal Distribution
• Solution: Normal Distribution

Probability Theory

• Introduction
• Probability Basics
• Calculating Simple Probabilities
• Practice: Simple Probabilities
• Quick Solution: Simple Probabilities
• Detailed Solution: Simple Probabilities
• Rule of Addition
• Practice: Rule of Addition
• Quick Solution: Rule of Addition
• Detailed Solution: Rule of Addition
• Rule of Multiplication
• Practice: Rule of Multiplication
• Solution: Rule of Multiplication
• Bayes Theorem
• Bayes Theorem - Practical Example
• Expected Value
• Practice: Expected Value
• Solution: Expected Value
• Law of Large Numbers
• Central Limit Theorem - Theory
• Central Limit Theorem - Intuition
• Central Limit Theorem - Challenge
• Central Limit Theorem - Exercise
• Central Limit Theorem - Solution
• Binomial Distribution
• Poisson Distribution
• Real-Life Problems

Hypothesis Testing

• Introduction
• What Is a Hypothesis?
• Significance Level and P-Value
• Type I and Type II Errors
• Confidence Intervals and Margin of Error
• Excursion: Calculating Sample Size and Power
• Performing the Hypothesis Test
• Practice: Hypothesis Test
• Solution: Hypothesis Test
• t-test and t-distribution
• Proportion Testing
• Important p-z Pairs

Regressions

• Introduction
• Linear Regression
• Correlation Coefficient
• Practice: Correlation
• Solution: Correlation
• Practice: Linear Regression
• Solution: Linear Regression
• Residual, MSE, and MAE
• Practice: MSE and MAE
• Solution: MSE and MAE
• Coefficient of Determination
• Root Mean Square Error
• Practice: RMSE
• Solution: RMSE

Advanced Regression and Machine Learning Algorithms

• Multiple Linear Regression
• Overfitting
• Polynomial Regression
• Logistic Regression
• Decision Trees
• Regression Trees
• Random Forests
• Dealing with Missing Data

ANOVA (Analysis of Variance)

• ANOVA - Basics and Assumptions
• One-Way ANOVA
• F-Distribution
• Two-Way ANOVA – Sum of Squares
• Two-Way ANOVA – F-Ratio and Conclusions

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