Study Notes

8 open learning journals — continuously updated on GitHub

Python for Finance

Fundamentals → data pipelines → applied ML. A finance-first approach to Python programming and data analysis.

MySQL for Finance

Core SQL to advanced analytics. Relational schemas, CTEs, window functions, and Python–SQL integration.

MySQLSQLAnalytics

Statistical analysis, data modelling, and financial visualisation using R — from syntax to applied finance.

RStatisticsVisualisation

LLMs for Finance

Large Language Models, PyTorch, and GenAI applied to financial analytics, reporting automation, and data extraction.

LLMPyTorchGenAI

Machine Learning for Finance

Regression, classification, and ensemble models for financial forecasting, risk assessment, and portfolio optimisation.

Scikit-learnForecastingRisk

Advanced Excel for Finance

Power functions, pivot tables, financial modelling, and VBA automation for professional-grade finance reporting.

ExcelVBAModelling

Power BI & DAX for Finance

End-to-end financial dashboards, KPI design, DAX measures, and Power Query ETL for management reporting.

Statistics & Time-Series

Statistical foundations, time-series forecasting (ARIMA, Prophet), and econometric modelling for financial data.

StatsmodelsARIMAEDA

Learning System: Every valuable insight from 100+ podcast episodes—across AI, career strategy, finance, and data science—gets clipped, tagged, and connected in Notion. No passive consumption. Pure applied learning that compounds over time. Explore the clips →

Let's Connect

If you'd like to discuss how I can help with your financial analytics, reporting, or data strategy, I'd love to hear from you.

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