Course curriculum

  • 1

    Introduction

    • Course Introduction

    • Course Overview
  • 2

    Introduction to Statistics

    • Two Branches of Statistics

    • Statistics and Machine Learning

    • Gaussian Distribution

    • Sample vs Population

    • Demo: Gaussian Distributions in Python

    • Measures of Central Tendency

    • Measures of Variability

    • Demo: Calculating the Mean and Median in Python

    • Demo: Variance

    • Randomness in Machine Learning

    • Demo: Random Numbers with Python

    • Demo: Random Numbers with NumPy

    • When to Seed and Controlling for Randomness

    • Law of Large Numbers

    • Central Limit Theory

    • Demo: Central Limit Theory
  • 3

    Hypothesis Testing

    • Statistical Hypothesis Testing

    • Defining P-Value

    • Reject or Fail Null Hypothesis

    • Errors in Hypothesis Testing

    • Statistical Distributions

    • Density Functions

    • Demo: Probability Density function in Python

    • Student's T-Distribution

    • Chi-Squared Distribution and Demo: Chi-Squared Distribution

    • Critical Values

    • One-Tailed and Two-Tailed Tests

    • Demo: Calculating Critical Values

    • Correlation Defined

    • Demo: Strong Positive Correlation

    • Covariance and Covariance Demo

    • Pearson's R Defined and Demo

    • Parametric Statistical Tests

    • Demo: Parametric Significance Tests

    • Effect Size

    • Demo: Pearson's Correlation Between Two Variables

    • Statistical Power Defined

    • Power Analysis: The Core 4

    • Demo: Student’s t-Test Power Analysis
  • 4

    Resampling

    • Data Sampling and Resampling

    • Sampling Errors

    • Statistical Resampling

    • Bootstrap Approach

    • Demo: Bootstrap in Python

    • K-Fold Cross-Validation

    • Demo: K-Fold Cross Validation

    • Variations on Cross-Validation

    • Demo: Train/Test Split
  • 5

    Estimation Statistics

    • Problems with Hypothesis Testing

    • Estimation Statistics Defined

    • Effect Size

    • Interval Estimation

    • Tolerance Intervals

    • Demo: Parametric Tolerance Intervals

    • Confidence Intervals

    • Demo: Confidence Intervals

    • Demo: Non-Parametric Confidence Intervals

    • Prediction Intervals

    • Demo: Prediction Intervals Using Linear Regression