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Subject name: Computing and Data Science |
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Author : Topic content : |
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This course consists of three parts. The first part, programming with Python, covers branching and iteration; string manipulation; abstractions, functions; tuples, lists, dictionaries; recursion, testing, debugging, exceptions. The second part, problem modeling and experiment techniques, covers combinatorial optimization and modeling, graph-theoretic models, random walks, Monte Carlo simulation, confidence intervals, understanding experimental data. Finally, it introduces elements of machine learning (clustering and classification) and its application.
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