Eligibility Requirements for Georgia Institute of Technology Master of Science in Computer Science

Review the eligibility requirements outlined below for the georgia tech masters computer science program:





Eligibility
Requirement


GPA
Minimum: 3.0/4.0


Letter of Recommendation
Three LOR is required


GRE
153 in Verbal, 155 in Quantitative, and 3.0 in Analytical sections


English Language Proficiency
TOEFL: 100
IELTS: 7.5


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Test Scores Requirement

ExamRequirement
7.5
100

Program Eligibility URL:https://www.cc.gatech.edu/ms-computer-science-admission-requirements

Georgia Institute of Technology Master of Science in Computer Science Rankings

World University Ranking- QS 2021

97

About Georgia Institute of Technology

Georgia Institute of Technology is a public research university widely known for its highly qualified students from its campuses in Atlanta, France, China as well as online. It is highly recognized for its highly ranked engineering and computing colleges and, besides, boasts great programs in business, design, liberal arts, and science.


The annual research grants returning over a billion dollars, Georgia Tech is an engine of economic development. Dedicated to training leaders who use technological tools to the advancement of society the university's innovative culture has been the driving force that has been driving 137 years of solutions that have been illuminating the future.

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Georgia Institute of Technology Master of Science in Computer Science Program Description

The Georgia tech masters computer science program equips students for successful careers in industry through specialized training. Applicants with a bachelor's degree in computer science from an accredited institution are eligible to apply. Additionally, students without a bachelor's degree in computer science are welcome to apply, provided they understand they may need to complete remedial coursework tailored to their background, alongside fulfilling MSCS degree requirements.

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Program URL:https://www.cc.gatech.edu/degree-programs/master-science-computer-science

Georgia Institute of Technology Master of Science in Computer Science Program Curriculum

Students enrolled in M.S. Computer Science degree programs have the option to select from 11 specializations. For detailed information, please refer to the list of specializations and core courses provided:


Computational Perception and Robotics


Core Courses (6 hours)


Algorithms: Pick one (1) of:



  • Computability, Algorithms, and Complexity

  • Introduction to Graduate Algorithms 

  • Computational Complexity Theory

  • Design and Analysis of Algorithms

  • Approximation Algorithms

  • Randomized Algorithms

  • Computational Science and Engineering Algorithms


    • And pick one of:



  • Artificial Intelligence

  • Machine Learning


    • Electives (9 hours)


    Pick three (3) courses from Perception and Robotics, with at least one course from each.


    Perception



  • Computational Photography

  • Computer Vision

  • 3D Reconstruction

  • Computational Perception

  • Cyber Physical Design and Analysis

  • Machine Learning for Robotics

  • Natural Language


    • Robotics



  • Autonomous Robotics

  • Autonomous Multi-Robot Systems

  • Human-Robot Interaction

  • Artificial Intelligence Techniques for Robotics

  • Interactive Robot Learning

  • Robot Intelligence: Planning


    • Computer Graphics


    Core Courses (6 hours)


    Pick one (1) of:



  • Foundations of Computer Graphics

  • Video Game Design

  • Computer Animation


    • And pick one (1) of:



  • Computability, Algorithms, and Complexity

  • Introduction to Graduate Algorithms


    • Electives (9 hours)


    Pick three (3) from:



  • Video Game Design and Programming

  • Computational Photography

  • Computer Vision

  • Foundations of Computer Graphics

  • Shape Grammars

  • Data Visualization Principles

  • Information Visualization

  • Computer Animation


    • Computing Systems


    Core courses (9 hours):



  • Computability, Algorithms, and Complexity

  • Introduction to Graduate Algorithms


    • And, pick two (2) of:



  • Advanced Operating Systems

  • Compiler Design

  • Computer Networks

  • High-Performance Computer Architecture

  • Software Development Process OR Advanced Topics in Software Engineering

  • Programming Languages

  • Database Systems Concepts and Designs


    • Any Core Courses in excess of the 9 hour requirement may be used as Computing Systems Electives


    Electives (9 hours):


    Pick three (3) courses from:


    Introduction to Information Security


    Graduate Introduction to Operating Systems
    Big Data Systems and Analytics



  • Real Time Systems

  • Secure Computer Systems

  • Applied Cryptography

  • Network Security

  • Intro to Cyber Physical Systems Security

  • Embedded Software Optimization

  • Software Architecture and Design

  • Software Analysis and Testing

  • Introduction to Enterprise Computing

  • Database System Implementation

  • Design and Analysis of Algorithms

  • Advanced Internet Computing Systems and Applications

  • Distributed Computing

  • Internetworking Architectures and Protocols

  • Networked Applications and Services

  • Network Science

  • Advanced Topics in Microarchitecture

  • Reliability and Security in Computer Architecture

  • Theory of Cryptography

  • FPL Special Topics: Foundations of Programming Languages

  • High Performance Computing


    • Any Special Topics course that is being taught by a School of Computer Science faculty member may also count as a Computing Systems elective.  The definition of "School of Computer Science faculty member" is a faculty member who appears on the School of Computer Science website.


    High Performance Computing


    Core Courses (6 hours):



  • Computational Science and Engineering Algorithms

  • High Performance Computing


    • Electives (9 hours):


    Pick three (3) from:



  • Multicore Computing: Concurrency and Parallelism on the Desktop

  • High-Performance Parallel Computing: Tools and Applications

  • Compiler Design

  • High-Performance Computer Architecture

  • Special Topics: Parallel Numerical Algorithms

  • Parallel and Distributed Simulation

  • Special Topics: Hot Topics in Parallel Computing


    • Human Centered Computing


    (This specialization is only for PhD students in HCC who want to earn an MSCS degree)


    Core Courses (9 hours):


    CS 6451 Intro to HCC
    AND


    CS 6452 Prototyping Interactive Systems
    AND


    CS 7455 Issues in HCC
    Electives (6 hours):


    Pick two (2) from:


    CS 6455 User Interface Design and Evaluation
    CS 6456 User Interface Software
    CS 6460 Educational Technology: Conceptual Foundations
    CS 6465 Computational Journalism
    CS 6470 Design of Online Communities
    CS 6471 Computational Social Science
    CS 6474 Social Computing
    CS 6476 Computer Vision
    CS 6601 Artificial Intelligence
    CS 6730 Data Visualization: Principles & Applications
    CS 6750 Human-Computer Interaction
    CS 6795 Introduction to Cognitive Science
    CS 7450 Information Visualization
    CS 7451 Human-Centered Data Analysis
    CS 7460 Collaborative Computing
    CS 7461 Machine Learning
    CS 7470 Mobile and Ubiquitous Computing
    CS 7476 Advanced Computer Vision
    CS 7610 Modeling and Design
    CS 7632 Game AI
    CS 7633 Human Robot Interaction
    CS 7637 Knowledge-Based AI
    CS 7620 Case-based Reasoning
    CS 7650 Natural Language
    CS 7695 Philosophy of Cognition
    CS 7697 Cognitive Models of Science and Technology
    CS 7790 Cognitive Modeling
    CS 8803 Computational Creativity
    CS 8803 Expressive AI
    CS 8803 Computers, Communications & International Development


    Human-Computer Interaction 


    Core courses (6 hours):


    Principles of User Interface Software OR CS 7470 Mobile and Ubiquitous Computing
    Human-Computer Interaction


    Electives (9 hours):


    Pick three (3) courses from the two sub-areas below, including at least one from each sub-area:


    Sub-area: Design and evaluation concepts



  • Principles of Design

  • Software Requirements Analysis and Specification

  • User Interface Design and Evaluation

  • Video Game Design

  • Educational Technology: Conceptual Foundations

  • Computational Journalism

  • Design of Online Communities

  • Introduction to Cognitive Science

  • Educational Technology: Design and Evaluation

  • Computer-Supported Collaborative Learning

  • Cognitive Modeling


    • Sub-area: Interactive technology



  • Introduction to Health Informatics

  • Data Visualization: Principles & Applications

  • Design of Design Environments

  • Mixed Reality Experience Design

  • Information Visualization

  • Human-Centered Data Analysis

  • Collaborative Computing

  • Mobile and Ubiquitous Computing


    • Interactive Intelligence


    Core courses (9 hours):


    Take one (1) course from:


    Algorithms and Design



  • Software Development Process

  • Advanced Topics in Software Engineering

  • Computability, Algorithms, and Complexity

  • Introduction to Graduate Algorithms

  • Computational Science and Engineering Algorithms


    • And, two (2) courses from:​



  • Artificial Intelligence

  • Knowledge-Based AI

  • Machine Learning


    • Electives (6 hours): 


    Pick two (2) courses from:


    Interaction



  • Introduction to Health Informatics

  • Educational Technology: Conceptual Foundations

  • Computational Journalism

  • Computational Social Science

  • AI, Ethics, and Society

  • Human-Computer Interaction 


    • AI Methods



  • Computer Vision

  • Multi-Robot Systems

  • Game AI

  • Human-Robot Interaction

  • AI Storytelling in Virtual Worlds

  • Deep Learning

  • Machine Learning with Limited Supervision

  • Natural Language

  • Special Topics: Advanced Game AI


    • Cognition



  • Introduction to Cognitive Science

  • Modeling and Design

  • Human and Machine Learning

  • Special Topics: Computational Creativity


    • Machine Learning 


    Core courses (6 hours):


    Algorithms: Pick one (1) of:



  • Computability, Algorithms, and Complexity

  • Introduction to Graduate Algorithms

  • Computational Complexity Theory

  • Design and Analysis of Algorithms

  • Graph Algorithms

  • Approximation Algorithms

  • Randomized Algorithms

  • Computational Science and Engineering Algorithms


    • And, pick one (1) of:



  • Machine Learning

  • Computational Data Analysis: Learning, Mining, and Computation


    • Electives (9 hours): 


    Elective ML courses must have at least 1/3 of their graded content based on Machine Learning.


    Pick three (3) of:



  • Big Data Systems & Analysis

  • Computer Vision 

  • AI, Ethics, and Society

  • Network Science

  • Markov Chain Monte Carlo

  • Spectral Algorithms

  • Machine Learning Theory

  • Pattern Recognition

  • Behavioral Imaging 

  • Reinforcement Learning and Decision Making

  • Deep Learning 

  • Machine Learning for Robotics

  • Machine Learning for Trading

  • Natural Language

  • Special Topics: Probabilistic Graph Models

  • Web Search and Text Mining

  • Data and Visual Analytics

  • Big Data for Health

  • Computational Statistics

  • Bayesian Methods

  • Stochastic Optimization

  • Approved Substitutions


    • Modeling and Simulations


    Core courses (6 hours):



  • Modeling and Simulation: Foundations and Implementation


    • And pick one (1) of



  • High Performance Computing

  • Simulation

  • Introduction to Numerical Methods for Partial Differential Equations


    • Electives (9 hours):


    Pick three (3) of:



  • High Performance Computing 

  • Parallel and Distributed Simulation

  • Special Topics: Quantum Information, Computation, and Simulation

  • Network Science

  • Modeling, Simulation and Military Gaming

  • Simulation

  • Introduction to Numerical Methods for Partial Differential Equations


    • Scientific Computing 


    Core courses (6 hours):



  • Numerical Linear Algebra


    • Pick one (1) of:



  • Iterative Methods for Systems of Equations

  • Introduction to Numerical Methods for Partial Differential Equations


    • Electives (9 hours):


    Pick three (3) of:



  • High-Performance Parallel Computing: Tools and Applications

  • Special Topics: Parallel Numerical Algorithms

  • Computational Science and Engineering Algorithms

  • High Performance Computing

  • Iterative Methods for Systems of Equations

  • Special Topics: Algorithms for Medical Imaging and Inverse Problems

  • Computational Chemistry

  • Introduction to Numerical Methods for Partial Differential Equations


    • Social Computing


    Core courses (6 hours):


    Pick two (2) of:



  • Design of Online Communities

  • Social Computing

  • Computational Social Science


    • Electives (9 hours):


    Pick three (3) more classes including additional classes from the above and:



  • Secure Computer Systems

  • Computer Networks

  • Principles of User Interface Software

  • Computational Journalism

  • Computability, Algorithms, and Complexity

  • Introduction to Graduate Algorithms 

  • Advanced Internet Computing Systems and Applications

  • Data Visualization: Principles & Applications

  • Human-Computer Interaction

  • Distributed Computing

  • Networked Applications and Services

  • Network Science

  • Information Visualization

  • Human-Centered Data Analysis

  • Computer-Supported Collaborative Learning

  • Natural Language


    • Visual Analytics


    Core courses (9 hours):



  • Data Visualization: Principles & Applications

  • Information Visualization

  • Data and Visual Analytics


    • Electives (6 hours): 


    Pick two (2) from:



  • Principles of User Interface Software

  • Computational Journalism

  • Computer Graphics

  • Human-Computer Interaction

  • Introduction to Cognitive Science

  • Human-Centered Data Analysis

  • Machine Learning

  • Computational Data Analysis
     

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    Specialization

    Students enrolled in M.S. Computer Science degree programs have the option to select from 11 specializations. For detailed information, please refer to the list of specializations and core courses provided:

    Computational Perception and Robotics

    Core Courses (6 hours)

    Algorithms: Pick one (1) of:

    • Computability, Algorithms, and Complexity
    • Introduction to Graduate Algorithms 
    • Computational Complexity Theory
    • Design and Analysis of Algorithms
    • Approximation Algorithms
    • Randomized Algorithms
    • Computational Science and Engineering Algorithms

    And pick one of:

    • Artificial Intelligence
    • Machine Learning

    Electives (9 hours)

    Pick three (3) courses from Perception and Robotics, with at least one course from each.

    Perception

    • Computational Photography
    • Computer Vision
    • 3D Reconstruction
    • Computational Perception
    • Cyber Physical Design and Analysis
    • Machine Learning for Robotics
    • Natural Language

    Robotics

    • Autonomous Robotics
    • Autonomous Multi-Robot Systems
    • Human-Robot Interaction
    • Artificial Intelligence Techniques for Robotics
    • Interactive Robot Learning
    • Robot Intelligence: Planning

    Computer Graphics

    Core Courses (6 hours)

    Pick one (1) of:

    • Foundations of Computer Graphics
    • Video Game Design
    • Computer Animation

    And pick one (1) of:

    • Computability, Algorithms, and Complexity
    • Introduction to Graduate Algorithms

    Electives (9 hours)

    Pick three (3) from:

    • Video Game Design and Programming
    • Computational Photography
    • Computer Vision
    • Foundations of Computer Graphics
    • Shape Grammars
    • Data Visualization Principles
    • Information Visualization
    • Computer Animation

    Computing Systems

    Core courses (9 hours):

    • Computability, Algorithms, and Complexity
    • Introduction to Graduate Algorithms

    And, pick two (2) of:

    • Advanced Operating Systems
    • Compiler Design
    • Computer Networks
    • High-Performance Computer Architecture
    • Software Development Process OR Advanced Topics in Software Engineering
    • Programming Languages
    • Database Systems Concepts and Designs

    Any Core Courses in excess of the 9 hour requirement may be used as Computing Systems Electives

    Electives (9 hours):

    Pick three (3) courses from:

    Introduction to Information Security

    Graduate Introduction to Operating Systems
    Big Data Systems and Analytics

    • Real Time Systems
    • Secure Computer Systems
    • Applied Cryptography
    • Network Security
    • Intro to Cyber Physical Systems Security
    • Embedded Software Optimization
    • Software Architecture and Design
    • Software Analysis and Testing
    • Introduction to Enterprise Computing
    • Database System Implementation
    • Design and Analysis of Algorithms
    • Advanced Internet Computing Systems and Applications
    • Distributed Computing
    • Internetworking Architectures and Protocols
    • Networked Applications and Services
    • Network Science
    • Advanced Topics in Microarchitecture
    • Reliability and Security in Computer Architecture
    • Theory of Cryptography
    • FPL Special Topics: Foundations of Programming Languages
    • High Performance Computing

    Any Special Topics course that is being taught by a School of Computer Science faculty member may also count as a Computing Systems elective.  The definition of "School of Computer Science faculty member" is a faculty member who appears on the School of Computer Science website.

    High Performance Computing

    Core Courses (6 hours):

    • Computational Science and Engineering Algorithms
    • High Performance Computing

    Electives (9 hours):

    Pick three (3) from:

    • Multicore Computing: Concurrency and Parallelism on the Desktop
    • High-Performance Parallel Computing: Tools and Applications
    • Compiler Design
    • High-Performance Computer Architecture
    • Special Topics: Parallel Numerical Algorithms
    • Parallel and Distributed Simulation
    • Special Topics: Hot Topics in Parallel Computing

    Human Centered Computing

    (This specialization is only for PhD students in HCC who want to earn an MSCS degree)

    Core Courses (9 hours):

    CS 6451 Intro to HCC
    AND

    CS 6452 Prototyping Interactive Systems
    AND

    CS 7455 Issues in HCC
    Electives (6 hours):

    Pick two (2) from:

    CS 6455 User Interface Design and Evaluation
    CS 6456 User Interface Software
    CS 6460 Educational Technology: Conceptual Foundations
    CS 6465 Computational Journalism
    CS 6470 Design of Online Communities
    CS 6471 Computational Social Science
    CS 6474 Social Computing
    CS 6476 Computer Vision
    CS 6601 Artificial Intelligence
    CS 6730 Data Visualization: Principles & Applications
    CS 6750 Human-Computer Interaction
    CS 6795 Introduction to Cognitive Science
    CS 7450 Information Visualization
    CS 7451 Human-Centered Data Analysis
    CS 7460 Collaborative Computing
    CS 7461 Machine Learning
    CS 7470 Mobile and Ubiquitous Computing
    CS 7476 Advanced Computer Vision
    CS 7610 Modeling and Design
    CS 7632 Game AI
    CS 7633 Human Robot Interaction
    CS 7637 Knowledge-Based AI
    CS 7620 Case-based Reasoning
    CS 7650 Natural Language
    CS 7695 Philosophy of Cognition
    CS 7697 Cognitive Models of Science and Technology
    CS 7790 Cognitive Modeling
    CS 8803 Computational Creativity
    CS 8803 Expressive AI
    CS 8803 Computers, Communications & International Development

    Human-Computer Interaction 

    Core courses (6 hours):

    Principles of User Interface Software OR CS 7470 Mobile and Ubiquitous Computing
    Human-Computer Interaction

    Electives (9 hours):

    Pick three (3) courses from the two sub-areas below, including at least one from each sub-area:

    Sub-area: Design and evaluation concepts

    • Principles of Design
    • Software Requirements Analysis and Specification
    • User Interface Design and Evaluation
    • Video Game Design
    • Educational Technology: Conceptual Foundations
    • Computational Journalism
    • Design of Online Communities
    • Introduction to Cognitive Science
    • Educational Technology: Design and Evaluation
    • Computer-Supported Collaborative Learning
    • Cognitive Modeling

    Sub-area: Interactive technology

    • Introduction to Health Informatics
    • Data Visualization: Principles & Applications
    • Design of Design Environments
    • Mixed Reality Experience Design
    • Information Visualization
    • Human-Centered Data Analysis
    • Collaborative Computing
    • Mobile and Ubiquitous Computing

    Interactive Intelligence

    Core courses (9 hours):

    Take one (1) course from:

    Algorithms and Design

    • Software Development Process
    • Advanced Topics in Software Engineering
    • Computability, Algorithms, and Complexity
    • Introduction to Graduate Algorithms
    • Computational Science and Engineering Algorithms

    And, two (2) courses from:​

    • Artificial Intelligence
    • Knowledge-Based AI
    • Machine Learning

    Electives (6 hours): 

    Pick two (2) courses from:

    Interaction

    • Introduction to Health Informatics
    • Educational Technology: Conceptual Foundations
    • Computational Journalism
    • Computational Social Science
    • AI, Ethics, and Society
    • Human-Computer Interaction 

    AI Methods

    • Computer Vision
    • Multi-Robot Systems
    • Game AI
    • Human-Robot Interaction
    • AI Storytelling in Virtual Worlds
    • Deep Learning
    • Machine Learning with Limited Supervision
    • Natural Language
    • Special Topics: Advanced Game AI

    Cognition

    • Introduction to Cognitive Science
    • Modeling and Design
    • Human and Machine Learning
    • Special Topics: Computational Creativity

    Machine Learning 

    Core courses (6 hours):

    Algorithms: Pick one (1) of:

    • Computability, Algorithms, and Complexity
    • Introduction to Graduate Algorithms
    • Computational Complexity Theory
    • Design and Analysis of Algorithms
    • Graph Algorithms
    • Approximation Algorithms
    • Randomized Algorithms
    • Computational Science and Engineering Algorithms

    And, pick one (1) of:

    • Machine Learning
    • Computational Data Analysis: Learning, Mining, and Computation

    Electives (9 hours): 

    Elective ML courses must have at least 1/3 of their graded content based on Machine Learning.

    Pick three (3) of:

    • Big Data Systems & Analysis
    • Computer Vision 
    • AI, Ethics, and Society
    • Network Science
    • Markov Chain Monte Carlo
    • Spectral Algorithms
    • Machine Learning Theory
    • Pattern Recognition
    • Behavioral Imaging 
    • Reinforcement Learning and Decision Making
    • Deep Learning 
    • Machine Learning for Robotics
    • Machine Learning for Trading
    • Natural Language
    • Special Topics: Probabilistic Graph Models
    • Web Search and Text Mining
    • Data and Visual Analytics
    • Big Data for Health
    • Computational Statistics
    • Bayesian Methods
    • Stochastic Optimization
    • Approved Substitutions

    Modeling and Simulations

    Core courses (6 hours):

    • Modeling and Simulation: Foundations and Implementation

    And pick one (1) of

    • High Performance Computing
    • Simulation
    • Introduction to Numerical Methods for Partial Differential Equations

    Electives (9 hours):

    Pick three (3) of:

    • High Performance Computing 
    • Parallel and Distributed Simulation
    • Special Topics: Quantum Information, Computation, and Simulation
    • Network Science
    • Modeling, Simulation and Military Gaming
    • Simulation
    • Introduction to Numerical Methods for Partial Differential Equations

    Scientific Computing 

    Core courses (6 hours):

    • Numerical Linear Algebra

    Pick one (1) of:

    • Iterative Methods for Systems of Equations
    • Introduction to Numerical Methods for Partial Differential Equations

    Electives (9 hours):

    Pick three (3) of:

    • High-Performance Parallel Computing: Tools and Applications
    • Special Topics: Parallel Numerical Algorithms
    • Computational Science and Engineering Algorithms
    • High Performance Computing
    • Iterative Methods for Systems of Equations
    • Special Topics: Algorithms for Medical Imaging and Inverse Problems
    • Computational Chemistry
    • Introduction to Numerical Methods for Partial Differential Equations

    Social Computing

    Core courses (6 hours):

    Pick two (2) of:

    • Design of Online Communities
    • Social Computing
    • Computational Social Science

    Electives (9 hours):

    Pick three (3) more classes including additional classes from the above and:

    • Secure Computer Systems
    • Computer Networks
    • Principles of User Interface Software
    • Computational Journalism
    • Computability, Algorithms, and Complexity
    • Introduction to Graduate Algorithms 
    • Advanced Internet Computing Systems and Applications
    • Data Visualization: Principles & Applications
    • Human-Computer Interaction
    • Distributed Computing
    • Networked Applications and Services
    • Network Science
    • Information Visualization
    • Human-Centered Data Analysis
    • Computer-Supported Collaborative Learning
    • Natural Language

    Visual Analytics

    Core courses (9 hours):

    • Data Visualization: Principles & Applications
    • Information Visualization
    • Data and Visual Analytics

    Electives (6 hours): 

    Pick two (2) from:

    • Principles of User Interface Software
    • Computational Journalism
    • Computer Graphics
    • Human-Computer Interaction
    • Introduction to Cognitive Science
    • Human-Centered Data Analysis
    • Machine Learning
    • Computational Data Analysis
       

    The campus life at Georgia Tech is really lively, students can join numerous clubs and organizations based on their personal interests. Media, service, leadership, whatever the path, there is an avenue to explore and expand for all to grow outside the classroom.
    Here's a glimpse into the vibrant world of clubs and organizations at Georgia Tech:

    Sound Off:

    An option that students can use to channel themselves through various forms of the media such as radio, podcasts et cetera.

    Let Loose:

    Organize student events meant to offer a break in the academic stress and promote interactivity, relaxation, and, most importantly, enjoyment on campus.

    Join In:

    Helps students to connect with different clubs and organizations in the directory list, guaranteeing everyone will find their appropriate community.

    Go Greek:

    Students wishing to join fraternity and sorority life can have avenues to belong, develop leadership, or serve in philanthropic works.

    Volunteer:

    We promotes the students to help the community through numerous service projects and initiatives that promote giving, thus, building a culture of altruism.

    Lead:

    Provides forums for students to take up leadership roles and have a good effect on campus through partaking in student government and leadership programs.

    Georgia Institute of Technology Master of Science in Computer Science Acceptance Rate

    Acceptance Rate for Georgia tech masters computer science is 16%. 

    Application Documents for Georgia Institute of Technology Master of Science in Computer Science

    Mandatory Application Documents

    • College Transcript

    • IELTS/TOEFL/DTE/PTE

    • GRE

      Want to Score 8+ Band in IELTS?

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      Letter of Recommendation for Georgia Institute of Technology Master of Science in Computer Science

      3 LOR Freeformat

      Statement of Purpose for Georgia Institute of Technology Master of Science in Computer Science

      Here are some tips for writing a compelling SOP for Georgia tech masters computer science program:


      Research the Program:


      Familiarize yourself with the program's curriculum, faculty, research areas, and any unique features. Prepare your SOP to show why Georgia Tech's program specifically aligns with your academic and career goals.


      Showcase Your Passion:


      Clearly describe your passion for computer science and your reasons for pursuing graduate studies in this field. Discuss specific experiences or projects that ignited your interest and demonstrate your commitment to advancing your knowledge and skills.


      Highlight Relevant Experience:


      Highlight any relevant academic coursework, research projects, internships, or work experience that showcases your readiness for graduate-level study in computer science. Discuss how these experiences have prepared you for success in Georgia Tech's program.


      Connect Your Goals:


      Clearly outline your short-term and long-term career goals and explain how obtaining a Master's in Computer Science from Georgia Tech will help you achieve them. Show how the program's resources, opportunities, and network will support your aspirations.


      Be Authentic and Personal:


      Use your SOP as an opportunity to share your unique story, experiences, and perspective. Avoid generic statements and clichés, and instead, be genuine and reflective about your journey and motivations.

      Read more

      Georgia Institute of Technology Master of Science in Computer Science Scholarships available for International Students

      The chances of receiving financial help from the government or state programs as an international student at Georgia Tech could be low and so are the chances of receiving institutional scholarships administered by the OSFA office. However, there are alternative options available to assist you in managing your finances:


      Private Loans:


      In your search, think about checking with some private lending programs. Many banks offer those products however often with a credit-worthy co-signer, who is a U.S. citizen or permanent resident with a permanent U.S. address as a requirement. OSFA office will offer its services in terms of coordinating the private loan if it gets approved.


      Emergency Tuition Loan:


      If an unusual financial situation occurs, you may be allowed to apply and receive an Emergency Tuition Loan managed by the OSFA. Such loans, are made on a first-come, first-served basis. 


      Payment Plans:


      Taking into account that there are some situations when payments have a small delay or inter-national wire transfers take more than expected, you can work directly with the Bursar and Treasury Services office. They give schedule of payments to help you manage your payments in a way that is suitable for you.

      Read more

      What makes Georgia Institute of Technology Master of Science in Computer Science unique?

      Georgia Institute of Technology (Georgia Tech) have over 135 years of history dedicated to innovation and progress.
      What Makes Georgia Tech Unique:



    • Renowned globally for its premier STEM-focused education.

    • Offers diverse opportunities across six colleges and 100+ Ph.D. and master's programs.

    • Highlight interdisciplinary learning to prepare students for diverse career paths.

    • Collaborative environment where students work closely with award-winning faculty.

    • State-of-the-art facilities and resources support student passions and goals.

    • Promotes an innovative culture that encourages creativity and exploration.

    • Instills a sense of purpose and commitment to making a positive impact on society.

    • Provide graduates with the skills and mindset needed for success in any field.

      • Read more

      Conclusion: Should you apply to Georgia Institute of Technology Master of Science in Computer Science?

      In conclusion, if you are seeking a high-quality graduate program in computer science with ample opportunities for growth, networking, and academic excellence, applying to the Georgia tech masters computer science program could be a beneficial choice. However, it's essential to carefully consider all factors and assess whether the program aligns with your goals and circumstances before making your decision.

      Read more
      ‌

      Ask a Question

      Have Queries about Georgia Institute of Technology?

      Get Answers from Alums

      Hello. My name is Om Aherrao. I am doing bachelor of computer applications. I am thinking to pursue my master degree in computer science in USA. So I want to know that can I do MS in CS after BCA?

      Computer Science aka an MS or M.Sc. (depending on the country) is both a principle or research-based problem or a combination of both, depending on the university you enroll in. 


      Yes, you can do MS in Computer Science or MS in IT after BCA from precise universities. But, some reputed universities in the US ask for four years of bachelors as their minimum criteria. And for Germany, it is challenging to get admission to Public universities as they do not cost tuition fees. So, it is very hard to get admission to German Universities. They ask for 4 years of Bachelors and some work experience.


      Some of the basic requirements to be eligible to practice for MS abroad include:



      • An exact rating in IELTS or comparable English language talent tests. IELTS band score of a minimum 6 is required.

      • A precise GRE score. A rating of 310 – 320 is preferable.

      • An excellent academic profile. You want to have proper tutorial scores in your undergraduate degree.

      • Apart from these, you will require an incisive SOP/LOR.

      • Recommendation letters and other monetary archives are a must. Note that some universities, especially the ones in the U.S., ask for either 15 years or 16 years of undergraduate research to practice for a master’s.


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        Can i do masters in CS ( Computer Science ) in USA after doing B.Sc Computer Science?

        An MS in computer science in the USA takes 1-2 years and is available at many educational universities. To secure admission in MS in Computer Science program, international students should have a four-year undergraduate degree in a relevant discipline with good scores in the GRE (Graduate Record Examinations) and one of the
        English language proficiency tests.  
        To pursue MS in computer science in the USA for Indian students means complying with the requisite criteria. Here’s taking a look at the same: 



         Candidates should have Bachelor’s or undergraduate degrees equivalent to Bachelor’s program in Computer Science in the USA. They may also have a degree in any technical discipline which is relevant. Aggregates of 3.0 GPA are a must out of 4.0 GPA. It means a minimum B grade with 83-86% of marks. 
         Another MS in computer science in USA eligibility criteria pertains to the GRE score. Students need to score anywhere between 292-328 to boost their chances. Most universities accept GRE cut-offs in this segment. For example, Stanford University takes GRE 328, while GRE 292 is good enough for Harvard University. Carnegie Mellon University accepts scores of 316, while the University of Washington takes 300. 
         Good scores are necessary for English language proficiency tests like TOEFL or IELTS. The minimum IELTS cut-off is usually 7, although institutions like the University of Southern California may accept 6.5 scores while some like Carnegie Mellon University insist on IELTS 7.5.

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        I just completed my bachelor's degree in BCA (computer science). Now I am planning for MBA. Can you suggest some MBA specializations related to Computer Science?

        The route of MBA in Computer Science is moreover recognized as MBA in Computer Management. MBA in Computer Science has been one of the most chosen MBA specializations, particularly for B. Tech graduates. It is a postgraduate direction that is designed totally for deeper and centred study in Computer Science and Computer Engineering. After finishing a program centred around computer science at the undergraduate level, many university college students figure out an MBA in Computer Science in order to reap greater knowledge about the state of affairs before making a profession that caters especially to this field.


        The MBA in Computer Science program imparts sufficient capabilities and expertise to college students that help them in planning, designing, and imposing complex purposes and software program systems. It is plain that the desire for computers, technology, and software program systems is of utmost value throughout all industries in the world today. Hence, college students who earn an MBA in Computer Science acquire various career opportunities. Designing and imposing purposes and making sure its easy functioning is integral in all sectors. Students who specialize in Computer Science in the course of Master in Business Administration provide for the wishes and desires in the technical issue of all establishments and companies.


        Management gurus who specialize in computer science have a higher hand on all those university students who genuinely have a commencement degree in laptop science or a master's diploma which is solely associated with the science or technical stream. They have an acceptable understanding of the details and fundamentals of laptop science, as properly as the managerial components of it. MBA in computer science is completed within a tenure of two or three years, in accordance with the mode of study.


        The MBA in pc science course makes the expert direction of college students, especially various and full of options. Several lucrative jobs and professions are presented to administration graduates specialising in laptop science. The need for folks with such a heritage is constant throughout all sectors. Hence, good revenue programs are offered to the employees who take up roles that come beneath the field of administration in the context of laptop science. Some widely widespread job roles undertaken through the capacity of graduates who complete an MBA in pc science are supplied below.\


         



        1. Chief Information Officer

        2. Consultant

        3. Software Publisher

        4. Commercial and Industrial Designer

        5. Computer Scientist


          6. So these are your options but feel free to check out more.

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