Interactive slides that execute Python in your browser, an AI tutor that answers questions about the page you are reading, and live six-week cohorts that turn self-study into a finished credential.
An approach Prof. Xuhu Wan has been refining since 2012, when he first introduced live, runnable code in a Year-1 Business Statistics course. Every course on this site is self-developed and offered to the public. Students registered in his university course can access the matching materials here.
One Python · two Statistics. Run in your browser, no install required.
Pure Python — variables, control flow, functions, collections, files. Coding practice in every chapter. 5 ch · ~6 hours.
Open course → Statistics · BeginnerEDA → probability → random variables → sampling distributions. Animation-rich, light on code. 6 ch.
Open course → Statistics · InferenceConfidence intervals, hypothesis testing, simple linear regression. Visual intuition over code. 7 ch.
Open course →
The courses below build on the foundations with deeper material —
full interactive slides and coding practice.
HKUST students registered in the corresponding course — available after sign-up and sign in with your school email.
From DataFrames to decision models. Covers pandas, NumPy, SciPy, Matplotlib in depth; linear regression with CAPM and Fama-French; clustering for customer segmentation. Worked examples use real market data.
From pandas to alpha — the working analyst's toolkit. Markets as data objects, regression and CAPM, Fama-French factors, regularized regression, gradient boosting, walk-forward backtesting. Based on the book Modern Business Analytics.
For working programmers. OOP at depth — descriptors, metaclasses, ABCs, Protocols — a structural view of class morphisms, and Python on the network (sockets, HTTP, async I/O). Coding practice in every chapter.
A code-first journey from classical statistics through causal inference, Bayesian methods, time series, clustering, pattern recognition, embeddings, foundation models, and interpretability. Based on Advanced Business Analytics.
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.
Network analysis (nodes, edges, centrality, PageRank, community detection) 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.
Schemas → data models → ontologies, in pure Python (NetworkX, rdflib, Neo4j, pgvector). Entity resolution, bitemporal modelling, GraphRAG, GNNs on knowledge graphs, EU AI Act compliance.
Upcoming course slides will cover Quantitative Trading and backtests of published algorithms — read the paper, run the backtest in your browser, see whether the alpha survives.
See the full catalogue — Quantitative Trading, Risk Management, Network & Social Media Analytics →
Every course has a companion interactive textbook — full prose, derivations, extended worked examples, and live code cells. Read in your browser; no PDF, no download.
Each chapter is structured prose with code cells interleaved. Click Run on any cell to execute it in your browser; edit numbers, re-run, and watch the chart change.
Predict-then-reveal cells force active recall. Debug-yourself exercises have hidden tests that pass only when your fix is correct. Spaced-repetition cards bring back what you've forgotten.
The AI tutor lives on every page. It knows the chapter you're on, the code you just ran, and the errors you just saw — so its answers are specific to your context, not generic. And the slides themselves update with your questions — over a cohort, the deck grows the explanations the cohort actually asked for.
Eumathe Academy is the personal teaching channel of Prof. Xuhu Wan. Every course is developed independently — designed from scratch for self-directed learners, expanded with the historical and economic context that textbooks usually omit, and made fully interactive so you can learn at your own pace.