Academic Experience

Co-taught the undergraduate and postgraduate Data Visualization for ML module. Responsibilities covered the full teaching lifecycle: - **Revamped learning material** — redesigned and updated lecture content and lab materials for the 2025/26 cohort - **Delivered lectures** — presented core topics including data preprocessing, ML visualization pipelines, and interpretation techniques - **Designed and delivered lab sessions** — hands-on Python labs covering visualization libraries and ML model output analysis - **Created slide decks** — produced all lecture slides for the 2026 delivery - **Paper marking** — assessed and graded student assignments and examinations

**Courses Taught:** 1. **Introduction to Data Visualization with Power BI** — Data sources, visualizations, color theory, Gestalt principles, DAX expressions, dashboards. 2. **Excel for Data Science** — Data cleaning, PivotTables, time-series analysis, statistical analysis, Solver. 3. **Data Preprocessing & Cleaning** — Missing data, outlier detection, one-hot encoding, categorical variables. 4. **Data Splitting & Cross Validation** — Train/validation/test sets, overfitting, precision/recall/F1, confusion matrix.

**Courses Taught (Pearson BTEC):** - **Fundamentals of AI & Intelligent Systems** (K/618/5660) — AI types, applications, NLP, conversational AI, ethics. - **Big Data & Visualization** (J/618/6251) — Python for big data, Jupyter, Power BI, DAX, data governance. - **Business Intelligence** (M/615/1641) — BI systems, data warehouses, OLAP, descriptive & predictive analytics. **Additional Responsibilities:** Lab environment configuration, coursework and assessment preparation aligned with Pearson BTEC standards.