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Georgetown University


Detailed Course Information


Spring 2019
May 27, 2020
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Information Select the desired Level or Schedule Type to find available classes for the course.

STIA 475 - Financial Markets & Int'l Sec
This course examines information technology’s role in transforming the global financial system and in providing new tools to help policymakers understand and anticipate the still unfolding international security implications of this transformation. The IT revolution that led to financial innovation, deregulation, and the proliferation of financial markets around the world continuously spawns capabilities for sense-making. New (and new combinations of) methods, models and tools for the collection, aggregation, processing and sense-making of financial market data emerge. One can analyze tens of millions of financial instruments with millions of price updates per second, streaming topic-coded news in foreign languages, supply chains, private equity and venture capital investments, social media, and projects offers and tenders from around the world. Economic indicators, which have long been staples for policy makers are losing ground to near real time financial indicators of systemic risk, nation state stability, disease spread, agricultural prospects and other critical topics. Through combining social science, quantitative finance, data science and IT, teams are increasingly able to harness financial data to keep policy makers informed and better able to anticipate national security events and crises. 

The objective of this course is to introduce students to empirical methods, models, and tools from the peer reviewed literature and the financial and security domains by demonstrating their utility using real data against real world use cases. This course will acclimate students to challenges and opportunities inherent in trying to provide situational and option awareness to policymakers in a decision-theoretic framework. Professors will introduce students to ontologies, typologies, taxonomies, data models, and industry classification schema, and demonstrate how they are used with IT and financial data to answer real world questions. Causation, correlation and various approaches to anticipatory analysis will be explored and demonstrated. A guest lecturer will provide a succinct and practical lecture on “big data”. Professors will demonstrate Bayesian, machine learning and other approaches for sense-making and anticipatory analysis as well as financial industry analytics and visualization tied to real world use cases. Lectures and demonstrations will be followed by student exercises and problem solving employing the types of methods, models, tools and data demonstrated in class.

3.000 Credit hours
3.000 Lecture hours
0.000 Lab hours

Levels: MN or MC Graduate, Undergraduate
Schedule Types: Seminar

Science, Tech, & Int'l Affairs Department

Course Attributes:
SFS/IPOL Foreign Policy

Must be enrolled in one of the following Classifications:     

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