More than any other field, computer technology has shaped the modern world. Things considered common these days, like the Internet, Smart Phones, Cyber security systems, and more, wouldn’t have been possible without computing. A Master’s in Computer Science or Masters in CS from Columbia University can help develop an individual's skills and career prospects.

MSCS full formMaster of Science in Computer Science
Course Duration24 months
Course LevelPostgraduate
Average Tuition FeeINR 61,00,000 - 2,06,38,066 per year
Examination TypeSemester

The masters in computer science course at the Columbia University is usually offered for 24 months. Through MS in computer science course at Columbia University, the students will receive the knowledge and experience that will help them demonstrate domain expertise and the ability to either continue educational training at the doctoral level or immediately work in the key economic sectors, such as government, business, industry, health care, and education. MS in CS at Columbia University also brings several career opportunities after course completion.

The Master of Science in Computer Science Program at CU is distinguished by its forward-thinking curriculum, which includes credits, courses, and a research thesis requirement. It also involves physical resources for comprehensive learning like labs/equipment and the diversity of the program faculty.

Subjects under MS in Computer Science

The syllabus for the MS in Computer Science course will vary as per the specialisation, and university/college chosen by an international student. However, certain MS in Computer Science subjects are common for most courses. Apart from the final year projects and internship, the common MS in Computer Science subjects at Columbia University are as follows:

  • Basic Programming Laboratory
  • Programming Languages
  • Theory of Computation
  • Design and Analysis of Algorithms
  • Mathematical Logic
  • Discrete Mathematics
  • Distributed Systems
  • Computer Systems Verification
  • Complexity Theory
  • Operations Research
  • Data Mining and Machine Learning
  • Cryptography and Computer Security
  • Probability and Statistics

These MSCS Subjects list may vary based on the specialisations in each university or one might be given the option to pick and choose.

Scope of MS in Computer Science

After pursuing an MS in CS at Columbia University, a student can explore the given benefits and more:

  • Better career opportunities
  • Knowledge to elevate your tech stature
  • Possibility of tuition fee reimbursement
  • One step closer to doctorate
  • Avenues in the teaching field

Course Highlights

Official website

link to course page

Annual tuition fees annual tution fees info

$49,240 / year

Total tuition fees total tution fees info

$73,860 / 18 months

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5000+ Students

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3000+ Cr

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100% Free

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Application Fee


Application Deadline

January 15

  Fall priority 1

Fall priority 2

Feb 15, 2024

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Test Score Requirements test score requirement

Minimum english score required









Minimum aptitude score required


Waived off until further notice

Application Pre-requisites application pre requisite

  • Online Application
  • Application Fee
  • Official Transcript
  • Three Letters of Recommendation
  • Personal Statement
  • English Language Proficiency
  • Resume or CV
  • Video Interview

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Course Details

Core subject areas

  • Machine Learning for Functional Genomics
  • Machine Learning Theory
  • Topics in Database Systems
  •  Advanced Distributed Systems
  •  Hardware Security


In addition to graduate study, our students have gone on to a variety of careers either within the computer industry or elsewhere. Generally, the majority of our graduates have found positions at established computer/software companies (e.g. Microsoft, Google), research labs (e.g. IBM), or Wall Street firms (e.g. Morgan Stanley and Goldman Sachs). Other graduates have found positions at smaller companies or startups (e.g. foursquare). A few students have gone on to work or study outside of the field of computer science, applying their knowledge of the discipline to another field such as business, medicine, or law. Artificial Intelligence Artificial Intelligence (AI) is concerned with the development of systems that exhibit behavior typically associated with human cognition, such as perceiving, learning, communicating, reasoning, making decisions, and acting in a physical and social environment. AI research at Columbia CS focuses on machine learning, natural language and speech processing, computer vision, robotics, and security. AI researchers collaborate widely within the university and beyond, contributing to applications in medicine, public safety, law, journalism, and other areas. Some AI faculty are cross-listed with the Statistics department, Electrical Engineering department, and the Data Science Institute. Machine Learning The group does research on foundational aspects of machine learning — including causal inference, probabilistic modeling, and sequential decision making — as well as on applications in computational biology, computer vision, natural language and spoken language processing, and robotics. It is part of a broader machine learning community at Columbia that spans multiple departments, schools, and institutes. Activities include seminars on statistical machine learning, several student-led reading groups and social hours, and participation in local events such as the New York Academy of Sciences Machine Learning Symposium. Vision & Robotics The group studies the computational foundations for visual and robotic intelligence. They investigate machines that are able to perceive their surroundings and interact with them too. The group of ten faculty often collaborate across the sciences and the arts. They leverage insights from both nature and math to advance the fundamentals of perception and interaction, and they also transfer their research to tackle practical challenges across disciplines. Several of the faculty are cross-listed with the Data Science Institute. Networking The Networking group focuses on both the design and performance evaluation of communication systems and data networks of all kinds, including data center networks, wireless/cellular, optical, ultra-low power, Internet of Things, and the Internet; as well as policy and economics research related to the Internet. Computer Engineering Computer Engineering is where the study of hardware and software interfaces come together. It draws on techniques from Electrical Engineering and Computer Science to imagine and realize the next generation of devices and chips. The group researches the platforms that power all forms of computation – from IoT to mobile, data centers to supercomputers. Students learn how to design, create, and test software, hardware, and system designs for applications in business, industry, and government. The various research groups collaborate in diverse areas including computer architecture, hardware security, networks, and distributed embedded systems. Software Systems The Software Systems group pursues fundamental research in all aspects of the design, implementation, analysis, verification, and evaluation of software systems. The group conducts research with systems at all scales, from handheld devices to cloud computing data centers. They take an experimental systems approach in building real systems to investigate new research ideas, create tools to enable developers to quickly and correctly build complex systems, and teach students how to do it. Computational Biology The Computational Biology Group brings together interdisciplinary and cross-disciplinary individuals and skillsets to tackle problems in high throughput genomics, systems biology, and genetics. The group develops computational methods to analyze high throughput data on genetic variants within species, primarily human SNP, and sequencing data. The student and postdoc body in the group is very diverse in terms of undergraduate background as well as current PhD program or postdoctoral affiliation. The group meets weekly giving an opportunity for students to present their research internally and feedback on one another’s work. Weekly meetings occasionally give host to guest speakers, include a journal club, or extracurricular activities. Security & Privacy The security group works on a diverse set of security and privacy issues arising across the software/hardware stack. The key research goal of the group is to design, develop, and deploy principled solutions for improving the security and privacy aspects of computer systems. The group has a broad set of expertise ranging from systems security to designing privacy policies. It is a highly collaborative group and most of their students are co-advised by multiple faculty members. NLP & Speech The Speech and Natural Language Processing groups do fundamental work in language understanding and generation with applications to a wide variety of topics, including summarization, argumentation, persuasion, sentiment, detecting deceptive, emotional and charismatic speech, text-to-speech synthesis, analysis of social media to detect mental illness, abusive language, and radicalization. The groups collaborate closely on many research projects with each other, with language faculty in other universities, and with Columbia faculty in other disciplines. They also mentor a very large number of master’s and undergraduate research project students who participate in their research each semester. They have regular talks for faculty, students, and the larger New York area community. Theory The group does research on the fundamental capabilities and limitations of efficient computation. In addition, they use computation as a lens to gain deeper insights into problems from the natural, social, and engineering sciences. The group is highly collaborative, both within Columbia and among peer institutions. They hold a weekly Theory Lunch and a bi-weekly Student Seminar. Most graduate students have (at least) two advisors and collaborate with several professors and other students. Some of the faculty are cross-listed with the IEOR department and the Data Science Institute.