Where data meets investing.
bexazaa is an Irish-based platform built around one practical question: how do machine learning tools actually change the way people make investment decisions? We run group sessions and private lessons for learners at different stages, from curious beginners to working analysts.
What shaped this platform
When bexazaa started in 2018, most investment education focused on theory - portfolio allocation, risk ratios, valuation models. Machine learning was treated as a separate discipline, something for software engineers rather than investors.
The gap between the two fields was wide enough that even experienced analysts struggled to evaluate ML-based signals critically. Our curriculum grew out of that gap, designed to give learners the conceptual grounding and hands-on practice to bridge it themselves.
Every course module is built around real datasets and documented model failures, not just success stories. We think understanding where a model breaks down teaches more than any polished case study.
Four stages, one coherent path
Foundations
Statistical thinking, data types, and how ML models are trained - without assuming a coding background.
Model mechanics
Regression, classification, and time-series methods applied to financial datasets from equity and fixed income markets.
Critical evaluation
Overfitting, look-ahead bias, and regime shifts - the practical failure modes that matter most in live portfolios.
Applied practice
Instructor-led sessions where learners test their own model assumptions against new data and peer review.
The people behind the curriculum
Our instructors come from quantitative research, asset management, and applied data science - not just academia. Each brings a specific area of focus, which is why our programme covers the full pipeline from raw data to decision-making.
Orla Fennelly
Lead Instructor - Quant Methods
Orla spent eight years in fixed income research before moving into education full-time. She focuses on time-series modelling and the practical interpretation of ML outputs in portfolio contexts.
Darragh Slattery
Instructor - ML Engineering
Darragh leads the applied modules, with a focus on feature engineering and model validation pipelines.
Síofra Ní Bhriain
Instructor - Data Literacy
Síofra designs the foundational modules for learners without a technical background, making statistical concepts accessible without oversimplifying them.
Pádraig Ó Treasaigh
Instructor - Risk & Evaluation
Pádraig focuses on model risk and the critical evaluation stage - helping learners spot the assumptions that tend to break under real market conditions.