Applied Statistics with Python: A Complete Learning Experience

Modern machine‑learning workflows demand stronger statistical foundations than most practitioners currently receive. I offer instructor‑led training courses built around my new book and its accompanying Python notebooks, designed to close that gap.

​

If your company or academic institution is interested, feel free to reach out.

​

Part I: Foundations

​

Chapter 1: Random Events, Variables & Probability Modeling

• Random Events & Variables

• Introduction to Probability Distributions

• Populations and Samples

• Core Probability Distributions
   

Chapter 2: Distribution Families & Shapes

• Bounded and Unbounded Support

• Distributions With a Single Parameter

• Distributions With Two Parameters

• Distributions With Three Parameters

• Comparing Distributions

• Multivariate Distributions and Variable Relationships
   

Chapter 3: Sampling & Estimators

• Introduction to Statistical Inference

• Estimation & Sources of Uncertainty

• Simulation Data

• Sampling Distributions

• Exact Sampling Distributions for Inference

• Estimator Behavior

• Variability & Error Measures
   

Part II: Core Statistical Tools

 

Chapter 4: Hypothesis Testing & Statistical Comparison Methods

• Hypothesis Testing

• Classical Statistical Tests

• Practical Tools for Group Comparisons

• Association Measures

​

Chapter 5: Regression & Prediction Diagnostics

• ML Model Types

• ML Modeling Overview

• Classification Metrics

• Regression Diagnostics

• Model Fit Diagnostics

• Statistical Error & Variability

• ML Context Diagnostics

​

Chapter 6: Sampling Designs & Experiments

• Sampling Designs

• Representativeness

• Representative Model Evaluation

• Foundations of Experiments

• A/B Testing, Power and Effect Sizes

​

Chapter 7: Resampling & Permutation Based Inference

• Resampling with Bootstrap Methods

• Resampling with Jackknife Methods

• Randomization & Permutation Tests

• Method Comparison

​

Part III: Reliability, Drift & Temporal Behavior

​

Chapter 8: Survival Curves & Reliability Modeling

• Datasets Introduction

• Foundations of Reliability Modeling

• Survival Analysis Essentials

• Hazard-Based Insights for ML Reliability

• Modeling Failure Risk

• Reliability-Driven Operational Decisions

​

Chapter 9: Nonparametric Drift Detection & Monitoring

• Datasets Introduction

• Statistical Drift Tests

• Monitoring Over Time

• Randomization & Permutation Tests

• Method Comparison

• Advanced Drift Detection

​

Chapter 10: Parametric Drift Detection & Monitoring

• Datasets Introduction

• Parametric Distributions

• Model Selection

• Model Evaluation

• Drift Signals in Parametric Models

• Tail & Percentile Behavior

• Reliability Impact

• Monitoring Over Time

​