A hands-on and professional study journey based on the book
Practical Statistics for Data Scientists: 50 Essential Concepts
by Peter Bruce and Andrew Bruce
This repository translates key statistical concepts into real-world Python implementations.
This project serves as both a learning log and a technical showcase, designed to:
- Solidify foundational knowledge in applied statistics
- Build a professional portfolio using real logs and datasets
- Explore statistical thinking through code and visualizations
- Apply each topic to real-world cases (e.g., incidents, SLIs/SLOs, system behavior)
- Use tools such as Poetry, MkDocs, and Taskipy for reproducibility and structure
- Python 3.10+
- Poetry: dependency and virtual environment management
- Taskipy: task automation (
mkdocs, notebooks) - MkDocs: project documentation
- Jupyter Notebooks: analysis and visualizations
| Chapter | Title | Notebook Link |
|---|---|---|
| 1 | Exploratory Data Analysis | 1.1_exploratory_data_analysis.ipynb |
| 1 (pt.2) | Location & Variability Estimators | 1.2_location_variability_estimators.ipynb |
| 1 (pt.3) | Categorical, Correlation & Multivariate | 1.3_categorical_correlation_multivariate.ipynb |
| 1 (pt.4) | Distribution Shape & Threshold Design | 1.4_distribution_analysis_thresholds.ipynb |
| 2 | Sampling Bias & Mean | 2.1_sampling_bias_mean.ipynb |
.
├── notebooks/ # Daily study notebooks (1 per topic/day)
├── src/ # Reusable Python modules (e.g., statistics, visuals)
├── data/ # Datasets used in analysis
├── logs/ # Daily progress logs
├── docs/ # MkDocs documentation site
├── pyproject.toml # Poetry config
├── mkdocs.yml # Documentation configuration
└── tasks.py # Taskipy command shortcuts# Install dependencies
poetry install
# Activate environment
source $(poetry env info --path)/bin/activate
# Launch notebooks
poetry run task notebook
# Serve docs
poetry run task docsMIT © 2025 — Built with ♥ for learning, reliability, and reproducibility
This study project is supported by the following resources:
- Bruce, P., & Bruce, A. (2020). Practical Statistics for Data Scientists: 50 Essential Concepts (2nd ed.). O’Reilly Media.
- Google SRE Team. (2016). Site Reliability Engineering: How Google Runs Production Systems. O'Reilly Media.
https://sre.google/books/ - LogHub Dataset Repository — Apache Logs.
https://github.com/logpai/loghub - ANEEL Open Data — Reclamações nos 1º e 2º Níveis da Distribuidora.
https://dadosabertos.aneel.gov.br/dataset/reclamacoes-no-1o-e-2o-niveis-da-distribuidora