Abstract: 

This course focuses on learning the essentials of Python coding in the context of Computational Finance. Workshop attendees will learn about Python methods for streaming data inputs, creating data structures, transforming mathematical equations into computational models, coding machine learning algorithms, time series forecasting, data visualizations, and report generation. All of this will be hands-on and will use Finance use cases to illustrate the Python techniques. Computational Finance examples will include derivatives, options, algorithmic trading, and financial network analysis. As a bonus, attendees will learn the basics of agent-based modeling of dynamic systems using Python.

Bio: 

Fatena El-Masri, PhD is a Senior Financial Analyst at FDIC, with many years of experience in financial engineering, applied statistics, and quantitative risk modeling and management (including market, credit and operational risk). For her Computational Science and Informatics PhD dissertation research at GMU, she used Python and agent-based models to study the stability of the banking network and to identify instability conditions for failing banks.

While Fatena was working for an assignment with the Royal Australian Navy, she managed a Proof of Concept (POC) Artificial Intelligence (AI) project for the Australian Maritime Warfare Center, using IBM Watson, Watson Explorer, and Blue Prism for Natural Language Processing, Robotic Process Automation (RPA), & Video Analytics. She coded Python & TensorFlow for video analytics, and provided oversight management of the RPA contract to automate processes.