Advanced Analytics · 2026 Edition
Advanced Analytics
Complete Mastery Book
From statistical foundations to production ML systems. Every chapter covers concept, use cases, full implementation, and research frontiers — written to teach, not just list.
Every Chapter Follows This Structure
No chapter is just theory. No chapter is just code. Each one is a complete, self-contained learning unit.
🧠
Concept
Deep theory, mental models, the "why" behind every method — not just definitions
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Use Cases
Real industry examples by sector, when to use each approach, and when not to
⚙️
Implementation
Full annotated Python code, production patterns, common pitfalls, and exercises
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Research
Landmark papers, state-of-art results, open problems, and where the field is heading
How to use this book
Read each chapter fully before moving on. After every implementation section, type and run the code yourself — not copy-paste. After every research section, read at least one paper's abstract and introduction. That discipline compounded over 12 chapters produces genuine expertise.
All 12 Chapters
Chapters are published sequentially. Each is a complete, deep treatment — not a survey or a link list.
| # | Title & Description | Status |
|---|---|---|
| Ch 01 Part I |
Distributions, hypothesis testing, Bayesian vs frequentist, the central limit theorem, p-values done right, multiple testing, the replication crisis.
|
✓ Ready |
| Ch 02 Part I |
Systematic EDA, distribution diagnostics, missing data mechanisms, outlier handling, five families of feature engineering, target leakage, SHAP-based selection.
|
✓ Ready |
| Ch 03 Part I |
OLS assumptions and violations, regularization, polynomial regression, GAMs, quantile regression, heteroskedasticity, and model interpretation.
|
✓ Ready |
| Ch 04 Part II |
Classification & Imbalanced Learning
Decision trees to gradient boosting, ROC/AUC deep dive, SMOTE and advanced resampling, threshold optimization, multi-label classification.
|
Coming soon |
| Ch 05 Part II |
Time Series Analysis & Forecasting
Stationarity, ARIMA, SARIMA, Prophet, temporal cross-validation, anomaly detection, neural forecasting with N-BEATS and Temporal Fusion Transformer.
|
Coming soon |
| Ch 06 Part II |
Clustering & Dimensionality Reduction
K-means internals, DBSCAN, hierarchical clustering, PCA from scratch, t-SNE vs UMAP, cluster validation, and when unsupervised learning fails.
|
Coming soon |
| Ch 07 Part III |
Causal Inference & A/B Testing
Correlation vs causation, DAGs, RCTs, difference-in-differences, instrumental variables, propensity scoring, the statistics of experiments done correctly.
|
Coming soon |
| Ch 08 Part III |
Bayesian Analytics
Prior/posterior/likelihood, MCMC sampling, PyMC from scratch, Bayesian A/B testing, hierarchical models, and Bayesian vs frequentist in real decisions.
|
Coming soon |
| Ch 09 Part III |
Graph Analytics & Network Analysis
Graph theory, centrality measures, community detection, PageRank, NetworkX and PyG, knowledge graphs, graph neural networks.
|
Coming soon |
| Ch 10 Part IV |
NLP & Text Analytics
TF-IDF to transformers, sentiment analysis, topic modeling, NER, text classification pipelines, and using LLMs for analytics tasks.
|
Coming soon |
| Ch 11 Part IV |
ML in Production & MLOps
Model serving, drift detection, retraining pipelines, A/B model testing, feature stores, monitoring, and the full MLOps stack.
|
Coming soon |
| Ch 12 Part IV |
Analytics Engineering & Data Pipelines
dbt, data modeling, Medallion architecture, streaming analytics, data quality, the modern data stack, and analytics as code.
|
Coming soon |
Prerequisites & Setup
What You Need
Basic Python (variables, functions, loops, classes) and a memory of high-school statistics. Everything else is built here from first principles — no prior ML experience required.
Python Environment
Every chapter uses this standard stack. Install it once, use it everywhere.
# Create a dedicated environment conda create -n analytics python=3.11 conda activate analytics # Core stack — all chapters pip install numpy pandas scipy scikit-learn matplotlib seaborn pip install statsmodels plotly jupyter ipykernel # Chapter-specific (install when you reach that chapter) pip install xgboost lightgbm catboost # Ch 3, 4 pip install prophet neuralprophet # Ch 5 pip install umap-learn hdbscan # Ch 6 pip install pymc arviz # Ch 8 pip install networkx pyvis # Ch 9 pip install transformers datasets # Ch 10 pip install mlflow evidently # Ch 11 pip install dbt-core sqlalchemy # Ch 12
About the datasets
Every code example uses either built-in datasets (sklearn, seaborn) or freely downloadable public datasets. No paid data required. All examples run on a laptop with 8GB RAM.