Philippe Cote

FIN 440 - Commodities Analytics and Trading

This course reflects the aspects of a trader development program in industry with a strong trading analytics base consistent with today's marketplace requirements. You are expected to learn analytical concepts using the R language and become proficient in your ability to implement them with real world data. The skills set is transferable to any analytically based job, such as risk management, trading analytics, and/or quantitative trading including fundamentals. Prerequisites: FIN 312 and FIN 450.

FIN 450 - FinTech II: Applied Data Science in Finance

This course provides data science skills that are needed to implement financial concepts and theories. Topics covered include data wrangling, visualization, web scraping, machine learning, and natural language processing. Students gain an ability to draw informed insights from data for identifying business's needs, and to articulate solutions with effective visualization supporting business communication and discussions. Prerequisite: FIN 312 (Credit in FIN 449 is recommended).

FIN 451 - FinTech III: Enterprise-level Data Science

Data science expertise is increasingly sought after in the Finance industry, requiring a vast range of skills spanning many disciplines. The core objectives of this class are to: build Data Science tools necessary for operating in Finance at Enterprise level (Business Concepts and Theories / Teamwork), become able to confidently navigate all entrepreneurial activities from identifying market needs to creating business solutions (Entrepreneurial Thinking / Business Concepts and Theories / Teamwork) and learn how to communicate complex ideas with modern visualization techniques (Business Communication / Teamwork). Prerequisite: FIN 450.

FIN 488 - Selected Topics in Finance

Normally restricted to third- and fourth-year Business students. Prerequisites: FIN 301 or consent of Department. Additional prerequisites may be required.

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