Data Glacier · Education Series

Advanced Analytics

Complete Mastery Book

A free, open-source textbook covering statistical foundations through production ML systems. Every chapter: concept → use cases → implementation → research frontiers. Written to teach, not just list.

Read the Book ↓ About & Credits

Open Textbook · 2026

Advanced
Analytics

Complete Mastery Book

Statistical Foundations

EDA & Feature Engineering

Regression & Prediction

Classification

Time Series

Clustering

Causal Inference

Bayesian Analytics

Graph Analytics

NLP & Text

MLOps

Data Pipelines

MIT License · Free Forever

Data Glacier

Chapters

Published

80+

Code Examples

40+

Research Papers

Free

Always

Every Chapter Follows This Structure

No chapter is just theory. No chapter is just code. Each one is a complete, self-contained learning unit built around four sections.

🧠

Concept

Deep theory, mental models, the real "why" — not just definitions

🏭

Use Cases

Real industry examples by sector — when to use and when not to

⚙

Implementation

Full annotated Python, production patterns, pitfalls, exercises

🔬

Research

Landmark papers, state-of-art results, open problems, future directions

✅ How to use this book

Read each chapter fully before moving on. After implementation sections, type and run the code yourself — not copy-paste. After research sections, read at least one paper abstract. That discipline compounded over 12 chapters produces genuine expertise.

All 12 Chapters

3 of 12 published · Updated regularly

#	Title & Description	Status
Ch 01 Part I	Statistical Foundations & Probability Theory Distributions, hypothesis testing, Bayesian vs frequentist, the central limit theorem, p-values done right, multiple testing, the replication crisis. ConceptCodeResearch	✓ Ready
Ch 02 Part I	Exploratory Data Analysis & Feature Engineering Systematic EDA, distribution diagnostics, missing data mechanisms, outlier handling, five families of feature engineering, target leakage, SHAP-based selection. ConceptCodeResearch	✓ Ready
Ch 03 Part I	Regression & Predictive Modeling (Beyond Basics) OLS assumptions and all five violations, regularization (Ridge/Lasso/Elastic Net), GLMs, GAMs, quantile regression, heteroskedasticity, and robust standard errors. ConceptCodeResearch	✓ 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. ConceptCodeResearch	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. ConceptCodeResearch	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. ConceptCodeResearch	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. ConceptCodeResearch	Coming soon
Ch 08 Part III	Bayesian Analytics Prior/posterior/likelihood, MCMC sampling, PyMC from scratch, Bayesian A/B testing, hierarchical models, Bayesian vs frequentist in real decisions. ConceptCodeResearch	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. ConceptCodeResearch	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. ConceptCodeResearch	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. ConceptCodeResearch	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. ConceptCodeResearch	Coming soon

About the Author

Surya Tripathi

Data Science & AI Leader, IBM · Contributing Expert, Data Glacier Education

LinkedIn → Full Credits →

Surya is a Data Science and AI Leader at IBM, contributing to Data Glacier’s education vertical to bring industry-grade rigour to open learning. This book was designed to close the gap between academic statistics and what data practitioners actually need in production. Chapters are co-developed with Claude (Anthropic). Read more about the development process →

Prerequisites & Setup

Basic Python and high-school statistics. Everything else is built from first principles. All examples run on a laptop with 8 GB RAM using free, open-source libraries.

# Create a clean environment
conda create -n analytics python=3.11
conda activate analytics

# Core stack — used across 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 built-in datasets (sklearn, seaborn) or freely downloadable public data. No paid sources required. All examples are reproducible without any cloud environment.