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Applied Statistics with Python: A Complete Learning Experience

Data practitioners need stronger statistical tools to keep pace with the complexity of modern machine‑learning workflows.

I'm offering professional, instructor‑led training courses based on the material from my new book.

If your company is interested in bringing any of this material into an in‑house workshop, please feel free to reach out.

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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
     

Chapter 4: Hypothesis Testing & Statistical Comparison Methods

  • Hypothesis Testing

  • Classical Statistical Tests

  • Practical Tools for Group Comparisons

  • Association Measures

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Chapter 5: Regression & Prediction Diagnostics

  • ML Model Types

  • ML Modeling Overview

  • Classification Metrics

  • Regression Diagnostics

  • Model Fit Diagnostics

  • Statistical Error & Variability

  • ML Context Diagnostics

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Chapter 6: Sampling Designs & Experiments

  • Sampling Designs

  • Representativeness

  • Representative Model Evaluation

  • Foundations of Experiments

  • A/B Testing, Power and Effect Sizes

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Chapter 7: Resampling & Permutation Based Inference

  • Resampling with Bootstrap Methods

  • Resampling with Jackknife Methods

  • Randomization & Permutation Tests

  • Method Comparison

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Chapter 8: Nonparametric Drift Detection & Monitoring

  • Datasets Introduction

  • Statistical Drift Tests

  • Monitoring Over Time

  • Randomization & Permutation Tests

  • Method Comparison

  • Advanced Drift Detection

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Chapter 9: 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
     

Chapter 10: 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

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