Learning Statistics with Python
HKUST Business School · ISOM 5650
Learning Statistics
with Python
Explore statistical ideas through Python and real examples.
Read the book, run the slides, and put each idea into practice.
Taught by Prof. Xuhu Wan · Fall 2026
Course content
Choose a section to explore its slides, or open its practice. Each chapter ends with a quiz.
Chapter 0
Basics of Python
Python as a calculator, numbers, strings, lists, tuples, sets, dictionaries, control flow, comprehensions, functions, error handling, modules, NumPy arrays and SciPy probabilities.
5 sections · Section slides & practice
Chapter 1
Data Structures and Methods of Series
Series foundations: structures, arithmetic, summaries, cleaning, selection, time-series features and labelled charts, following a numbered learning path.
5 sections · Section slides & practice
Chapter 2
DataFrames
A numbered data-engineering path: table foundations, evidence-led audit and repair, grouped tables, validated joins, feature pipelines and research applications, with a 3.5-hour classroom route.
5 sections · Section slides & practice
Chapter 3
Reshaping Statistics
Statistics across business applications: distributions of customer and operating measurements, sampling uncertainty, experiments and multiple comparisons, association and cohort prediction evaluation, followed by portfolio loss and tail risk.
5 sections · Section slides & practice
Chapter 4
Statistical Predictive Models
Business prediction across delivery, inventory, customers, fraud and finance: target definitions, losses and decision costs; OLS, inference, model selection, nonlinear learners, causal designs, attention and walk-forward evaluation.
6 sections · Section slides & practice
Chapter 5
Bayesian Methods for Investment Research
Bayesian foundations for investment research: priors, posterior and predictive checks, robust regression, sequential learning, hierarchical stock signals, model uncertainty and walk-forward portfolio evidence.
6 sections · Section slides & practice
Chapter 6
Time Series Models for Volatility and Risk
Time-series models for volatility: clustering, ARCH/GARCH, asymmetric shocks, variance forecasts, VaR and volatility targeting. Stationarity, chronological evaluation, relationships and hidden states provide the supporting tools.
6 sections · Section slides & practice
Chapter 7
Modern Statistical Learning in Practice
Ranking, prediction intervals, partial effects, covariance and cluster allocation, event labels and selection uncertainty. Seven sections connect the statistical methods to a revisable, source-linked coding reference.
7 sections · Section slides & practice
Learn by doing
Read the section, predict what its code will do, then run and change the examples. Use section practice to check your understanding and finish with the chapter quiz.
For fuller explanations and review, use the companion book.
Open interactive book ↗