📊
Data Science
11 Parts · 38 Chapters
🗺️Territory Map▾
Ch 0 — The Map of Data Science
🎯⚡
∑Mathematical Foundations▾
Ch 1 — Descriptive Statistics
🎯⚡
Ch 2 — Linear Algebra for Data
⚡
Ch 3 — Calculus & Optimization
Ch 4 — Information Theory
🎲Probability & Inference▾
Ch 5 — Probability Foundations
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Ch 6 — Statistical Inference
🎯⚡
Ch 7 — Bayesian Thinking
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Ch 8 — A/B Testing
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🧹Data Wrangling▾
Ch 9 — Data Acquisition & Cleaning
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Ch 10 — Feature Engineering
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Ch 11 — Data Transformation & Scaling
Ch 12 — Data Leakage
🎯⚡
🔍Exploratory Analysis▾
Ch 13 — Exploratory Data Analysis
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Ch 14 — Data Visualization
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Ch 15 — Correlation & Association
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📈Classical Machine Learning▾
Ch 16 — Linear Regression
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Ch 17 — Logistic Regression
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Ch 18 — Regularization
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Ch 19 — Tree-Based Models
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Ch 20 — Support Vector Machines
Ch 21 — Unsupervised Learning
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🎯Model Evaluation & Selection▾
Ch 22 — Bias, Variance & the Learning Curve
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Ch 23 — Cross-Validation & Model Selection
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Ch 24 — Classification Metrics
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🧠Deep Learning▸
🔗Causal Inference▸
⚙️MLOps & Production▸
🚀Ethics & Frontier▸
Chapters read0/38
📊 Evidence path

Data Science

A visual route from probability and data preparation to experiments, models, and production decisions.

11 parts38 chapters48 terms
📊
DataStatsMLEvalMLOps
Data → model → decision
Version 1.0 · 2026

11 Parts · 38 Chapters · From Statistics to Machine Learning

38
Chapters
11
Parts
10
Quizzes
48
Terms
📍 Choose a Learning Path
Analyst Path
Stats, EDA, visualization, A/B testing
ML Engineer Track
Features, models, evaluation, MLOps
Deep Learning Route
Neural nets, transformers, production DL
Causal / Research
Causal inference, fairness, interpretability
🗺️Part 0 — Territory Map
⚡🎯
Ch 0
The Map of Data Science
From raw numbers to decisions — the full stack
∑Part 1 — Mathematical Foundations
⚡🎯
Ch 1
Descriptive Statistics
Mean, median, variance, quantiles — the vocabulary of data
⚡
Ch 2
Linear Algebra for Data
Vectors, matrices, and the geometry behind every model
Ch 3
Calculus & Optimization
Derivatives, gradients, and how models actually learn
Ch 4
Information Theory
Entropy, KL divergence, and mutual information
🎲Part 2 — Probability & Inference
⚡
Ch 5
Probability Foundations
Random variables, distributions, and the laws of chance
⚡🎯
Ch 6
Statistical Inference
Estimators, confidence intervals, and hypothesis tests
⚡
Ch 7
Bayesian Thinking
Priors, likelihoods, and the posterior — updating beliefs
⚡🎯
Ch 8
A/B Testing
Causal inference in the wild — the workhorse of tech
🧹Part 3 — Data Wrangling
⚡
Ch 9
Data Acquisition & Cleaning
The 80% of the job nobody advertises
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Ch 10
Feature Engineering
Turning raw columns into signals
Ch 11
Data Transformation & Scaling
Standardization, normalization, and why it matters
⚡🎯
Ch 12
Data Leakage
The silent killer of real-world ML
🔍Part 4 — Exploratory Analysis
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Ch 13
Exploratory Data Analysis
Look at your data before you model it
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Ch 14
Data Visualization
Communicating insight — grammar of graphics and design
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Ch 15
Correlation & Association
Measuring dependence — and why it isn't causation
📈Part 5 — Classical Machine Learning
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Ch 16
Linear Regression
The workhorse — simple, interpretable, and surprisingly strong
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Ch 17
Logistic Regression
Classification via the sigmoid — calibrated probabilities
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Ch 18
Regularization
Bias-variance trade-off in practice — Ridge, Lasso, Elastic Net
⚡
Ch 19
Tree-Based Models
Decision trees, random forests, and gradient boosting
Ch 20
Support Vector Machines
Maximum margin classifiers and the kernel trick
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Ch 21
Unsupervised Learning
Clustering, dimensionality reduction, and anomaly detection
🎯Part 6 — Model Evaluation & Selection
⚡🎯
Ch 22
Bias, Variance & the Learning Curve
Why models fail — underfitting, overfitting, and the trade-off
⚡
Ch 23
Cross-Validation & Model Selection
Honest estimates of out-of-sample performance
⚡🎯
Ch 24
Classification Metrics
Accuracy is almost never the metric you want
🧠Part 7 — Deep Learning
⚡
Ch 25
Neural Networks
Layers, activations, and universal approximation
Ch 26
Training Neural Networks
Backprop, optimizers, and the tricks that make it work
Ch 27
Convolutional Networks
Learning spatial features from images
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Ch 28
Sequence Models & Transformers
From RNNs to attention — modeling ordered data
🔗Part 8 — Causal Inference
⚡🎯
Ch 29
Causal Inference Fundamentals
Correlation is not causation — but we can still get causation
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Ch 30
Confounding & DAGs
Drawing your assumptions makes them testable
Ch 31
Uplift & Heterogeneous Effects
Who benefits? Not just 'does it work?'
⚙️Part 9 — MLOps & Production
⚡🎯
Ch 32
MLOps Fundamentals
From notebook to production — the unsung 90%
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Ch 33
Model Monitoring & Drift
Models decay — data changes, relationships change
Ch 34
Reproducibility & Experiment Tracking
If you can't reproduce it, it didn't happen
🚀Part 10 — Ethics & Frontier
⚡🎯
Ch 35
Fairness & Bias in ML
Models amplify the biases in their data
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Ch 36
Interpretability & Explainability
Why did the model predict that?
Ch 37
The Data Science Frontier
Where the field is heading