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365 DS - Advanced Stastistical Methods in Python

Linear regression​

  • Welcome to Advanced Statistics!
  • Welcome to the Course
  • Introduction to Regression Analysis
  • The Linear Regression Model
  • Correlation vs Regression
  • Geometrical Representation of the Linear Regression Model
  • Python Packages Installation
  • First Regression in Python
  • First Regression in Python Exercise
  • Using Seaborn for Graphs
  • How to Interpret the Regression Table
  • Decomposition of Variability
  • What is the OLS?
  • R-Squared

Multiple Linear Regression​

  • Multiple Linear Regression
  • Adjusted R-Squared
  • Multiple Linear Regression Exercise
  • Test for Significance of the Model (F-Test)
  • OLS Assumptions
  • A1: Linearity
  • A2: No Endogeneity
  • A3: Normality and Homoscedasticity
  • A4: No Autocorrelation
  • A5: No Multicollinearity
  • Dealing with Categorical Data - Dummy Variables
  • Dealing with Categorical Data - Dummy Variables Exercise
  • Making Predictions with the Linear Regression

Linear Regression with sklearn​

  • What is sklearn?
  • Game Plan for sklearn
  • Simple Linear Regression with sklearn
  • Simple Linear Regression with sklearn - Summary Table
  • A Note on Normalization
  • Multiple Linear Regression with sklearn
  • Adjusted R-Squared
  • Adjusted R-Squared Exercise
  • Feature Selection through p-values (F-regression)
  • A Note on Calculation of P-Values with sklearn
  • Creating a Summary Table with the p-values
  • Multiple Linear Regression - Exercise
  • Feature Scaling
  • Feature Selection through Standardization
  • Making Predictions with Standardized Coefficients
  • Feature Scaling - Exercise
  • Underfitting and Overfitting
  • Training and Testing

Linear Regression - Practical Example​

  • Practical Example (Part 1)
  • Practical Example (Part 2)
  • A Note on Multicollinearity
  • Practical Example (Part 3)
  • Dummies and VIF - Exercise
  • Practical Example (Part 4)
  • Dummy Variables Interpretation - Exercise
  • Practical Example (Part 5)
  • Linear Regression - Exercise

Logistic Regression​

  • Introduction to Logistic Regression
  • A Simple Example in Python
  • Logistic vs Logit Function
  • Building a Logistic Regression
  • Bulding a Logistic Regression Exercise
  • An Invaluable Coding Tip
  • Understanding Logistic Regression Tables
  • Understanding Logistic Regression Tables - Exercise
  • What do the Odds Actually Mean
  • Binary Predictors in a Logistic Regression
  • Binary Predictors in a Logistic Regression - Exercise
  • Calculating the Accuracy of the Model
  • Calculating the Accuracy of the Model - Exercise
  • Underfitting and Overfitting
  • Testing the Model
  • Testing the Model - Exercise

Cluster Analysis (Basics and Prerequisites)​

  • Introduction to Cluster Analysis
  • Some Examples of Clusters
  • Difference between Classification and Clustering
  • Math Prerequisites

K-Means Clustering​

  • K-Means Clustering
  • A Simple Example of Clustering
  • A Simple Example of Clustering - Exercise
  • Clustering Categorical Data
  • Clustering Categorical Data - Exercise
  • How to Choose the Number of Clusters
  • How to Choose the Number of Clusters - Exercise
  • Pros and Cons of K-Means Clustering
  • To Standardize or to not Standardize
  • Relationship between Clustering and Regression
  • Market Segmentation with Cluster Analysis (Part 1)
  • Market Segmentation with Cluster Analysis (Part 2)
  • How is Clustering Useful?
  • Exercise - Species Segmentation with Cluster Analysis (Part 1)
  • Exercise - Species Segmentation with Cluster Analysis (Part 2)

Other Types of Clustering​

  1. Types of Clustering
  2. Dendrogram
  3. Heatmaps

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