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Topics in Applicable Analysis
MATH 277
Spring 2014
Section: 01  
Crosslisting: COMP 261

This course is aimed at students with no previous experience or with modest experience in programming. The course will be structured in two parts that will run parallel. One part will be dedicated to learning the basics of programming, such as data types, statements and syntax, data containers, control structures, functions, object oriented programming, and file input/output. Teaching will be based on the Python language, which is a free and widely adopted high level language with applications in scientific computing, finance industry, and the web. In parallel to this formal computer science introduction, the course will introduce students to the basics of scientific computing, teaching aspects such as data interpolation, data fitting, solution of linear systems, meaning of eigenvectors and eigenvalues in computing, filtering and convolution, least squares problems, and a brief! introduction to Ordinary Differential Equations and Partial Differential Equations. Students will work on 2 projects, a midterm and a final project, which will be developed over the course of 5 weeks each. The projects will be modularized in weekly subprojects which will serve as weekly assignments. The midterm project will be oriented towards creating a computer vision application; the final project will be oriented to developing an application for simulating and visualizing heat transport.
Credit: 1 Gen Ed Area Dept: NSM MATH
Course Format: Lecture / DiscussionGrading Mode: Graded
Level: UGRD Prerequisites: [MATH222 AND MATH221] OR [MATH222 AND MATH223]
Fulfills a Major Requirement for: (MATH)(NS&B)
Past Enrollment Probability: Not Available

Last Updated on JUN-25-2022
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