Teaching

Subjects I teach.

Four core areas, taught to MBA students, doctoral researchers, working professionals, and faculty cohorts. Each is a real syllabus — built around the workflow a learner will use the day after the course ends.

Business Analytics

End-to-end analytics with R and Python — from data wrangling to predictive and prescriptive modelling.

Sample topics

Statistics & probability foundations
Data wrangling with R (tidyverse) and Python (pandas)
Regression, classification, clustering
Predictive & prescriptive modelling
Reproducible reporting with Quarto / RMarkdown

Audience: MBA, executive cohorts, faculty development

Data Science

Foundations, supervised and unsupervised learning, and applied case studies for business decisions.

Sample topics

Python for data science
Supervised learning: linear, tree-based, ensembles
Unsupervised learning: PCA, clustering
Model evaluation & cross-validation
Case studies on real business datasets

Audience: MBA analytics specialisation, working professionals

HR Analytics

People metrics, workforce dashboards, and evidence-based HR using Excel, Power BI, and R.

Sample topics

Headcount, attrition, engagement metrics
Predictive attrition modelling
Compensation & pay equity analysis
HR dashboards in Power BI / Tableau
Evidence-based people decisions

Audience: HR professionals, MBA HR specialisation

Business Intelligence

Interactive dashboards and BI reports with Power BI and Tableau — turning data into decision-ready stories.

Sample topics

Power BI fundamentals — Power Query, data modelling, DAX
Power BI dashboards, slicers, drill-through & RLS
Tableau fundamentals — calculations, LOD, parameters
Tableau dashboard design & storytelling
Publishing & governance on Power BI Service / Tableau Cloud

Audience: BI practitioners, MBA analytics, certification candidates