Chapter 4: Probability & Statistics¶
Reason about uncertainty and design statistically sound experiments—essential for ML and data science.
Metadata¶
| Field | Value |
|---|---|
| Track | Foundation |
| Time | 8 hours |
| Prerequisites | Chapter 1 (Python Fundamentals) |
Learning Objectives¶
- Reason about uncertainty using probability
- Understand distributions and their properties
- Apply Bayes' theorem and Bayesian thinking
- Design and interpret hypothesis tests
- Build and evaluate A/B tests
- Distinguish correlation from causation
What's Included¶
Notebooks¶
| Notebook | Description |
|---|---|
01_introduction.ipynb | Probability fundamentals, conditional probability |
02_intermediate.ipynb | Distributions, Bayes' theorem, Central Limit Theorem |
03_advanced.ipynb | Hypothesis testing, A/B testing, experiment design |
Scripts¶
probability_toolkit.py— Probability and statistical functions
Exercises¶
- 5 exercises with solutions (in
solutions/branch)
SVG Diagrams¶
- 3 visual diagrams for distributions and concepts
Read Online¶
You can read the full chapter content right here on the website:
- 04.1 Introduction -- Probability basics, conditional probability, law of large numbers
- 04.2 Intermediate -- Distributions, Bayes theorem, Central Limit Theorem
- 04.3 Advanced -- Hypothesis testing, confidence intervals, A/B testing capstone
Or try the code in the Playground.
How to Use This Chapter¶
Quick Start
Follow these steps to get coding in minutes.
1. Clone and install dependencies
git clone https://github.com/luigipascal/berta-chapters.git
cd berta-chapters
pip install -r requirements.txt
2. Navigate to the chapter
3. Launch Jupyter
GitHub Folder
All chapter materials live in: chapters/chapter-04-probability-statistics/
SciPy & Pandas
This chapter uses scipy and pandas. Both are in requirements.txt.
Created by Luigi Pascal Rondanini | Generated by Berta AI