All my publications will be found in the following links
Katleho Makatjane
Open Researcher and Contributor ID: orcid.org/0000-0002-3687-4098
Google Scholar: scholar.google.com/citations?user=o-yYS2oAAAAJ&hl=en
Katleho Makatjane: katlehomakatjane.owlstown.net/
Research Interests (Financial Engineering Focus)
My research focuses on the intersection of statistics, financial engineering, machine learning, and quantitative risk analysis. I am particularly interested in developing predictive and probabilistic models for financial markets, risk management, and decision-making under uncertainty.
Key areas of interest include:
Financial Engineering
Quantitative Finance
Financial Econometrics
Time Series Analysis & Forecasting
Volatility Modelling
Risk Measurement (VaR and Expected Shortfall)
Extreme Value Theory
Machine Learning for Finance
Explainable Artificial Intelligence (XAI)
Algorithmic and Data-Driven Trading
Energy and Financial Market Forecasting
This five-week non-credit short course provides a practical introduction to data management and analytics, equipping participants with the skills to collect, organise, analyse, and communicate data effectively. The course covers data preparation, SQL fundamentals, statistical analysis, predictive modelling, data visualisation, and ethical data management using industry-relevant tools such as Python, R, SQL, Power BI, and Tableau. Through hands-on exercises and a capstone project, participants develop the analytical and problem-solving skills needed to transform data into meaningful insights for research, business, and evidence-based decision-making.
This course provides students with practical knowledge of business intelligence and modern data analytics for evidence-based decision-making. Students learn to collect, prepare, analyse, visualise, and interpret data using industry-standard tools such as Power BI, Tableau, Python, R, and SQL. The course covers data warehousing, exploratory and predictive analytics, dashboard development, time series analysis, and data governance, enabling students to transform raw data into actionable insights that support strategic business and organisational decisions
This course introduces students to the ethical, legal, and professional dimensions of working with data throughout its lifecycle. It examines topics such as data privacy, governance, algorithmic fairness, bias detection, and responsible artificial intelligence, while exploring national and international data protection regulations. Through real-world case studies from finance, healthcare, telecommunications, and government, students develop the skills to identify ethical risks, evaluate responsible data practices, and make informed decisions that promote transparency, accountability, and public trust in data-driven systems.