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Georgetown University


Detailed Course Information


Fall 2017
Nov 24, 2017
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SEST 704 - Intel Analytics
Intelligence Analytics is intended for students without a background in mathematics to learn and benefit from impressive state-of-the-art data analysis tools and methods. There are no prerequisites beyond an open mind and a desire to understand the truth, wherever the data may lead.
Key elements of this class philosophy are asking the right questions, gathering the best data, using proper methods, and ensuring the provided answer is the best answer to the question—all while ensuring results making sense in the proper context. Guiding principles are that more information is not necessarily better, not all information is created equal, and the easiest information to get is rarely the best to use. Also, tools and techniques are important, but the human element—both expertise and intuition—is essential.
In this spirit, this course will provide students a series of applied analytic tools of increasing complexity, starting with classical logic and leading to techniques for analyzing Big Data. Tools will include classic analytic methods for top-down deduction, bottom-up induction, and qualitative techniques for determining causality. We will identify limitations of Aristotelian logic, introduce basic principles of probabilistic reasoning, and demonstrate the power of model updating through Bayes’ theorem. There will be a strong laboratory component to try new analytic tools in depth and discuss approaches and results with instructors and peers.  Instructors will walk students through any tools or models requiring quantitative training.
The class emphasizes rigorous logic, but it is not a math class; no quantitative skill is necessary, and there are no prerequisites, but we highly recommend that students have earned a B+ or higher in SEST 521, The Theory and Practice of Intelligence. The class draws from the logic portions of SEST 624, Thinking Critically About Intelligence and Policy. No professional experience is required, but the instructors expect that Intelligence Concentrators will draw on their past course and/or professional work to enrich the class’ experience. 

3.000 Credit hours
3.000 Lecture hours

Levels: MN or MC Graduate
Schedule Types: Seminar

Security Studies Department

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