Every Eumathe book is a live web book — code cells that run, animations that play, examples that you can edit. No PDF, no download. Buy once, get every update for life.
Books for the BA track.
From pandas and NumPy to linear regression, CAPM, Fama-French, and clustering for customer segmentation. Worked examples on real market data.
From pandas to alpha — the working analyst's toolkit. Series and DataFrames in depth, markets as data objects, simple and multi-factor regression, Fama-French, regularized regression, gradient boosting, walk-forward backtesting.
Code-first journey from classical statistics through causal inference, Bayesian methods, time series, clustering, pattern recognition, embeddings, foundation models, and interpretability.
Schemas → data models → ontologies, in pure Python (NetworkX, rdflib, Neo4j, pgvector). Entity resolution, bitemporal modelling, GraphRAG, GNNs on knowledge graphs, EU AI Act compliance.
Quant-finance methodology in book form.
The statistics of not fooling yourself. Information Coefficient diagnostics, drift detection and retraining, multiple-testing corrections (Bonferroni, BH, Deflated Sharpe), and the methodology discipline used by top hedge funds.
Networks, text, and the modern AI stack.
Network analysis (nodes, edges, centrality, PageRank, community detection, network formation) plus text analytics (topic models, sentiment, cascades), all browser-executable.
LLMs, foundation models, multimodal analysis, dynamic networks, knowledge graphs, agents, vector DBs, recommenders, MLOps, misinformation, deepfake detection, financial systemic risk.
What "lifetime updates" means: when Prof. Wan adds a chapter, fixes errata, refreshes an example with newer data, or rewrites a section based on reader feedback, your access shows the latest version automatically — no re-purchase, no migration.
Premium subscription unlocks every book and course, plus the contextual AI tutor.