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dc.contributor.authorKelly, Van
dc.identifier.citationKelly, Van. (2021, Spring), Syllabus, COM 1310: Mathematics for Computer Science, Yeshiva College, Yeshiva University.en_US
dc.descriptionCourse syllabus / YU onlyen_US
dc.description.abstractDescription The course will introduce students to a variety of topics in discrete mathematics that are essential for a Computer Science career. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; asymptotic notation and growth of functions; permutations and combinations, and counting principles. The Python programming language will be introduced during the first few weeks and assignments will require programming in Python. Course Outcomes Students will:  acquire a familiarity with the Python language and will use various Python software packages to solve problems in discrete mathematics.  be able to understand mathematical reasoning in order to read, comprehend, and construct mathematical arguments  understand the relationship between mathematical reasoning and the logical operations of Python.  be able to construct logically correct proofs, especially those based on induction and well ordering.  understand how inductive arguments are mirrored in recursive designs for computer programs.  understand how sets, relations, and graphs are used for modeling in Computer Science and how Python data structures to represent them.  be able to perform combinatorial analysis to estimate complexity  analyze the time and space complexity of simple algorithms using asymptotic analysis techniques.en_US
dc.relation.ispartofseriesYeshiva College Syllabi;Spring 2021
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.subjectdiscrete mathematicsen_US
dc.subjectcomputer scienceen_US
dc.subjectcourse syllabusen_US
dc.titleCOm 1310: Mathematics for Computer Scienceen_US
dc.typeLearning Objecten_US

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States