Georgia Institute of TechnologyWebsiteAcademic Catalog
College of ComputingDepartment Website
BS Degree in Computer Sciencesource 1source 2
CS Courses
- orComputing, Society, and ProfessionalismorCS 4001 (3)impactCS 4001: Computing, Society, and Professionalism
Examines the role and impact of information and communication technology in society, with emphasis on ethical, professional, and public policy issues. Credit not allowed for both CS 4001 and 4002.
Robots and SocietyororCS 4002 (3)impactCS 4002: Robots and SocietyExamines the role and impact of robotics, distributed sensing and actuation, ubiquitous computing and related technology in society, emphasizing ethical, professional and public policy issues. Credit not allowed for both CS 4001 and 4002.
- ororProject DesignCS 3311 (1)capstoneCS 3311: Project Design
Part 1 of a 2 semester project design and implementation sequence conjoined with Technical Communications. Prepare requirements, design and project plans. Develop a basic prototype of the desired system. Project is completed in CS 3312-Project Implementation. Credit will not be awared for CS 3311 and CS 4911.
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Math/Stat Courses
- Introduction to Multivariable CalculusMATH 2550 (2)mathMATH 2550: Introduction to Multivariable Calculus
Vectors in three dimensions, curves in space, functions of several variables, partial derivatives, optimization, integration of functions of several variables. Vector Calculus not covered. Credit will not be awarded for both MATH 2550 and MATH 2605 or MATH 2401 or MATH 2551 or MATH 1555.
- orProbability and Statistics with ApplicationsororMATH 3670 (3)mathMATH 3670: Probability and Statistics with Applications
Introduction to probability, probability distributions, point estimation, confidence intervals, hypothesis testing, linear regression and analysis of variance. Students cannot receive credit for both MATH 3670 and MATH 3770 or ISYE 3770 or CEE 3770.
Probability with ApplicationsISYE 2027 (3)mathISYE 2027: Probability with ApplicationsTopics include conditional probability, density and distribution functions from engineering, expectation, conditional expectation, laws of large numbers, central limit theorem, and introduction to Poisson Processes.