Data Warehouse for Algorithmic Trading Stocks-Price Forecasting on Digital Research Infrastructure using Machine Learning Modelling
📊 Data Warehouse for Algorithmic Trading – Student Presentation
Ever wondered how financial data is organized and used to support stock market analysis?
Join this presentation to explore how a Data Warehouse system is built to manage and process large-scale stock market data. The project demonstrates how raw financial data is transformed into structured formats that can be efficiently queried and used for predictive modelling.
🔍 You’ll learn:
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How Data Warehouses store and organize large datasets
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Star schema design in a real system
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How data pipelines automate transformation of financial data
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How machine learning models use prepared datasets for predictions
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How large datasets are processed efficiently
💡 This presentation shows how data systems connect directly to real-world applications in finance and computing.
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- 1000 K. L. O. Rd
- Kelowna, British Columbia
- Canada V1Y 4X8
- Building: 1000 K. L. O. Rd
- Room Number: HS107
- Click here for Map
- Starts 17 April 2026 07:00 AM UTC
- Ends 21 April 2026 05:00 PM UTC
- No Admission Charge
Speakers
Biography:
Emilio Iturbide Gonzalez is a graduating Computer Science student at Okanagan College with interests in data systems, software engineering, and artificial intelligence.
He has worked on building a Data Warehouse system designed to manage and process large volumes of stock market data. His work focuses on creating efficient data pipelines, organizing complex datasets, and supporting data-driven applications.
He is interested in building practical systems that connect software engineering, data processing, and real-world applications in technology.
Address:British Columbia, Canada