We start Unit 3 by continuing our discussion of data clustering from Unit 2. We introduce graphs as a set of nodes and edges, and learn how these can help solve degrees-of-separation problems and find a shortest path. We will practice using pseudocode as preparation for writing code, and learn about dynamic programming as we attempt to write optimally efficient programs.
In order to become better statistical thinkers, we will learn to spot and avoid several common logical and statistical fallacies, such as bias, data enhancement, causal fallacies, and the Texas sharpshooter fallacy. We introduce queuing network simulations, and compare the most common queue disciplines. The last session presents different possible careers in computer science, and its application across diverse fields and industries.
Unit 3 concludes with a Final Exam covering all material (lectures, recitations, and problem sets) from the beginning of the course through Queuing Network Models.
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