Stats10X Builder Series

Learn Statistics by building 10 hands-on, visual, and interactive projects inspired by key topics in foundational probability and statistics.

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About This Repo

This repository is my personal project to master Statistics by building real, practical projects aligned with core statistical ideas like probability, distributions, sampling, hypothesis testing, and more.

Instead of just reading or memorizing formulas, I’m simulating, visualizing, and applying statistical reasoning to concrete examples.

This is the Statistics counterpart to my Project10X – The Backend Builder Series, and it follows the same project-driven learning approach I used for my Linear Algebra 10X Builder Series.

I designed this repo to be useful not only for myself but also for anyone who wants to learn statistics through hands-on projects and practical coding.

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My Goals (And Maybe Yours Too)

• Build strong statistical intuition through simulation and visualization
• Learn practical statistics concepts by implementing them from scratch
• Make abstract concepts tangible: sampling distributions, p-values, the Central Limit Theorem, and more
• Lay a solid foundation for data science, machine learning, and scientific computing
• Practice writing clean, reproducible code with a math-first mindset

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Projects Overview

No.	Core Project	Focus Area	Outcome / Learning Goal
1	Descriptive Stats Analyzer	Summary Statistics	Compute and interpret central tendency and spread from real data
2	Data Visualization Dashboard	Visualizing Distributions	Create compelling visual summaries using plots and correlation maps
3	Probability Simulator	Basic Probability & Simulation	Simulate randomness and visualize probabilities
4	Distribution Explorer	Distributions (Binomial, Normal, etc.)	Model, compare, and interpret theoretical vs real-world distributions
5	Sampling & CLT Demo	Sampling & Central Limit Theorem	Understand random sampling and visualize the CLT
6	Hypothesis Testing Engine	Confidence Intervals & Hypothesis Testing Basics	Conduct tests on population claims and estimate error
7	t-tests & ANOVA Toolkit	Group Comparison Tests	Perform t-tests and ANOVA to compare groups
8	Simple Linear Regression Explorer	Basic Predictive Modeling	Fit and evaluate a single-feature linear model
9	Multiple Regression Studio	Multivariable Modeling	Work with multiple predictors and address assumptions
10	Categorical Data & Capstone Project	Categorical Analysis & ML Context	Apply stats to real classification problems and summarize full stack

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How I Use This Repo (And How You Can Too)

1. Pick a project based on the topic you're currently studying in statistics.
2. Read accompanying theory from your favorite stats book or course (OpenIntro, Khan Academy, etc.).
3. Work through the project scripts or notebooks, following the README and guided tasks.
4. Explore interactive features where available (like CLT sliders or p-value simulators).
5. Try the stretch goals to deepen your understanding.
6. Commit your notes and findings to build your own knowledge base.

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Tools & Libraries I Use

I use and recommend starting with these:

• Python 3.10+
• Jupyter Notebook / JupyterLab
• NumPy
• Pandas
• Matplotlib
• Seaborn

Optional additions:

• SciPy for statistical functions
• ipywidgets for interactivity
• pytest for testing your logic
• Plotly for rich, interactive visuals

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Contributions & Feedback

This is my personal learning journey, but I welcome feedback, suggestions, and pull requests! If you create your own variation or improve a project, feel free to contribute.

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License

This project is licensed under the MIT License. See LICENSE for details.

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Made by RM Villa