Teaching

I am teaching two courses per semester at the University of the Western Cape. Please sign up for courses on the university website.

STA 211: Statistical Distribution Theory

Term: 1 to 2 (February -May)

Offered: 2026

Statistical Distribution Theory introduces students to the mathematical foundations of probability and statistical inference through the study of random variables and probability distributions. The course develops a rigorous understanding of discrete and continuous probability distributions, moments and moment generating functions, multivariate distributions, and their applications in statistical modelling and inference.

Students learn to derive and interpret probability models, calculate probabilities for real-world experiments, and understand the theoretical principles underlying statistical methods. The module also develops computational skills through practical sessions using SAS and R, enabling students to manipulate data, perform statistical analyses, and produce professional statistical reports.

The course combines lectures, tutorials, and computer laboratories to strengthen students' analytical, computational, and problem-solving skills. Emphasis is placed on connecting statistical theory with practical applications, preparing students for advanced studies in statistics, data science, actuarial science, machine learning, and quantitative research.

COF 711: Time Series Analysis and Forecasting

Term: 3 to 4 (July to December)

Offered: 2026

Time Series and Forecasting equips students with the theoretical knowledge and practical skills required to analyse time-dependent data and develop reliable forecasting models for decision-making. The course introduces the fundamental concepts of time series analysis, including trend, seasonality, cyclical variation, autocorrelation, stationarity, and stochastic processes, while emphasising the interpretation and modelling of real-world temporal data.

Students learn to construct, estimate, and evaluate forecasting models using techniques such as exponential smoothing, decomposition methods, and ARIMA models. Practical sessions are conducted using SAS, enabling students to manage data, fit forecasting models, assess model performance, and communicate results through professional statistical analyses and reports.

The course integrates statistical theory with hands-on computational applications, preparing students to address forecasting challenges in business, finance, economics, public health, energy, and the social sciences. Emphasis is placed on developing analytical thinking, statistical reasoning, and computational proficiency to support evidence-based decision-making and advanced study in statistics and data science.

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