📐
Foundations First
Math, data types, probability, and distributions, every concept built from first principles before anything is assumed.
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Explore & Discover
The heart of analysis: describe, visualize, and interrogate data to find what actually matters, the discovery process, step by step.
🧹
Clean & Prepare Data
The real work behind every result: deduplicating, fixing missing values, taming outliers, transforming, and encoding messy data.
🎲
Probability & Uncertainty
The mathematics of chance: sample spaces and the rules, Bayes' theorem, the named distributions, and the normal curve and Central Limit Theorem behind every test.
🔬
Inference & Testing
Decide what is real: confidence intervals, z / t / ANOVA / chi-square and nonparametric tests, A/B testing, and choosing the right one.
📈
Regression to ML
From simple linear and logistic regression through regularization and ensembles into a full, modern machine-learning workflow.
🤖
Deep Learning & AI
Where statistics meets modern AI: neural networks, NLP and large language models, recommendation systems, and models in production.
🎓
Graduate-Level Depth
For when you want the real math: joint and conditional densities, transformations of random variables, estimation theory, and the inequalities behind the LLN and CLT.
🧩
Putting It All Together
Hands-on case studies that take real, messy datasets from raw file to finished analysis, the whole pipeline, start to finish.
🐍
Hands-On Python
Every chapter pairs with a runnable Colab / Jupyter notebook, NumPy, pandas, scikit-learn, statsmodels, so you build as you read.
👁️
Learn by Seeing
Hundreds of infographic cards, each idea shown, explained plainly, then walked through in code.