Data-Driven Decision Making

  1. Statistical and Analytical Foundations
    1. Probability Theory
      1. Basic Probability Concepts
        1. Probability Distributions
          1. Discrete Distributions
            1. Continuous Distributions
              1. Normal Distribution
                1. Binomial Distribution
                2. Conditional Probability
                  1. Bayes' Theorem
                    1. Law of Large Numbers
                      1. Central Limit Theorem
                      2. Descriptive Statistics
                        1. Data Types and Scales
                          1. Summary Statistics
                            1. Data Visualization Fundamentals
                              1. Distribution Analysis
                              2. Inferential Statistics
                                1. Sampling Theory
                                  1. Sampling Methods
                                    1. Sample Size Determination
                                      1. Sampling Error
                                      2. Hypothesis Testing Framework
                                        1. Type I and Type II Errors
                                          1. Power Analysis
                                            1. p-Values and Significance
                                            2. Confidence Intervals
                                              1. Statistical Tests
                                                1. Parametric Tests
                                                  1. Non-Parametric Tests
                                                    1. Goodness of Fit Tests
                                                  2. Regression Analysis
                                                    1. Simple Linear Regression
                                                      1. Multiple Linear Regression
                                                        1. Regression Assumptions
                                                          1. Model Diagnostics
                                                            1. Regularization Techniques
                                                              1. Ridge Regression
                                                                1. Lasso Regression
                                                                  1. Elastic Net
                                                                2. Experimental Design
                                                                  1. Design Principles
                                                                    1. Randomization
                                                                      1. Control Groups
                                                                        1. Blocking and Stratification
                                                                          1. Factorial Designs
                                                                            1. A/B Testing Methodology
                                                                            2. Time Series Analysis
                                                                              1. Time Series Components
                                                                                1. Stationarity Testing
                                                                                  1. Autocorrelation Analysis
                                                                                    1. Forecasting Methods
                                                                                      1. Moving Averages
                                                                                        1. Exponential Smoothing
                                                                                          1. ARIMA Models
                                                                                        2. Machine Learning Fundamentals
                                                                                          1. Supervised Learning
                                                                                            1. Classification Algorithms
                                                                                              1. Decision Trees
                                                                                                1. Random Forest
                                                                                                  1. Support Vector Machines
                                                                                                    1. Naive Bayes
                                                                                                      1. k-Nearest Neighbors
                                                                                                      2. Regression Algorithms
                                                                                                        1. Linear Regression
                                                                                                          1. Polynomial Regression
                                                                                                            1. Tree-Based Methods
                                                                                                          2. Unsupervised Learning
                                                                                                            1. Clustering Methods
                                                                                                              1. k-Means
                                                                                                                1. Hierarchical Clustering
                                                                                                                  1. DBSCAN
                                                                                                                  2. Dimensionality Reduction
                                                                                                                    1. Principal Component Analysis
                                                                                                                      1. Factor Analysis
                                                                                                                        1. t-SNE
                                                                                                                      2. Model Evaluation
                                                                                                                        1. Cross-Validation Techniques
                                                                                                                          1. Performance Metrics
                                                                                                                            1. Classification Metrics
                                                                                                                              1. Regression Metrics
                                                                                                                              2. Overfitting and Underfitting
                                                                                                                                1. Bias-Variance Tradeoff
                                                                                                                                2. Ensemble Methods
                                                                                                                                  1. Bagging
                                                                                                                                    1. Boosting
                                                                                                                                      1. Stacking

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                                                                                                                                  2. The DDDM Framework and Process

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                                                                                                                                  4. Tools and Technologies