This is a graduate-level course on theoretical aspects of Big Data. We will examine algorithms and data structures for dealing with massive data sets. We will discuss such topics as streaming algorithms, sublinear algorithms, dynamic graph algorithms, dimensionality reduction, metric embeddings, sketching, and parallel algorithms. In this course, students will read and present papers on the cutting-edge research in the area of Big Data.

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2 months

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January 15, 2024

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This course provides a survey of computer algorithms, examines fundamental techniques in algorithm design and analysis, and develops problem-solving skills required in all programs of study involving data science. Topics include advanced data structures for data science (tree structures, disjoint set data structures), algorithm analysis and computational complexity (recurrence relations, big-O notation, introduction to complexity classes (P, NP and NP-completeness)), data transformations (FFTs, principal component analysis), design paradigms (divide and conquer, greedy heuristic, dynamic programming), and graph algorithms (depth-first and breadth-first search, ordered and unordered trees). Advanced topics are selected from among the following: approximation algorithms, computational geometry, data preprocessing methods, data analysis, linear programming, multi-threaded algorithms, matrix operations, and statistical learning methods. The course will draw on applications from Data Science.

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Collins Chapusha
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