Max Liu Education
Education
Focused on applied mathematics, statistics, stochastic processes, scientific computing, and quantitative financial modelling.
BSc Mathematics & Statistics
Newcastle University, United Kingdom
2022 - 2026
Upper Second Class Honours (2:1)
Core Skills
Financial uncertainty modelling
Interpretable statistical modelling
Temporal forecasting analysis
Machine learning
Data analysis
Focus
In this education page, Focus highlights the coursework areas that best translate my mathematics and statistics training into applied analysis, research, and financial modelling.
01
Statistics & Analysis
Statistical modelling, inference, validation, and experiment design.
Applied Linear Models & Statistical Inference
This module explored statistical modelling as a way to explain relationships, test assumptions, and make reliable decisions from data.
- Regression modelling: linear and multiple linear regression
- Statistical inference: hypothesis testing, uncertainty, predictor significance
- Model validation: diagnostics, assumptions, and reliability checks
- Designed experiments: treatment comparison and controlled variation
- Generalized linear models: modelling non-standard outcome types
It comes to answer a question: How can we use statistical models to explain relationships, test assumptions, and make reliable decisions from data?
Time Series Analysis
This module explored time-dependent data through trend analysis, stationarity, autocorrelation, lagged relationships, forecasting models, and diagnostic evaluation.
- Analysed trends, seasonal patterns, and changes over time
- Studied stationarity and transformations for reliable modelling
- Used autocorrelation and lag structures to identify temporal dependence
- Built forecasting models to estimate future values
- Evaluated model reliability through diagnostics and forecast accuracy
It comes to answer a question: How can historical patterns in time-dependent data be used to understand change and support forecasting?
Big Data Analytics & Machine Learning Foundations
This module explored supervised and unsupervised learning methods, including regularised regression, variable selection, dimensionality reduction, and model evaluation for complex datasets.
- Built supervised learning models for prediction and classification tasks
- Studied unsupervised learning methods to discover hidden structure in data
- Applied regularisation techniques such as LASSO and Elastic Net for variable selection and overfitting control
- Used dimensionality reduction methods to simplify high-dimensional data while preserving key information
- Evaluated model performance to compare predictive reliability and generalisation
It comes to answer a question: How can we build predictive models and extract useful structure from high-dimensional data?
Statistical Computing with R & Python
This module developed programming-based approaches to statistical problem-solving using R and Python, focusing on implementing statistical methods, analysing datasets, validating computational results, and communicating findings through tables, plots, and reproducible workflows.
- Used R and Python to solve statistical problems and analyse datasets
- Implemented statistical methods through code rather than relying only on manual calculation
- Built reproducible workflows for data analysis, model fitting, and result interpretation
- Used visualisation and summary outputs to communicate statistical findings
- Strengthened computational thinking for applied statistics and data-driven analysis
It comes to answer a question: How can statistical methods be implemented, tested, and communicated through code?
02
Finance
Stochastic modelling, derivatives, volatility, and simulation.
Stochastic Financial Modelling & Option Pricing
Applied continuous-time financial models to understand risky assets, option pricing, volatility estimation, and Monte Carlo-based stock price simulation.
- continuous-time financial models
- risk-free and risky asset dynamics
- option trading
- volatility estimation
It comes to answer a question: How can uncertainty in financial markets be modelled, simulated, and priced?