columbia computer science phd acceptance rate

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Computer Science

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The Computer Science Department:  

Department website: http://www.cs.columbia.edu

Office location: 450 Mudd

Office contact: [email protected]

Director of Undergraduate Studies:  Dr. Jae Woo Lee, 715 CEPSR; 212-939-7066; [email protected]

Undergraduate Administrator:  CS Advising, [email protected]  

The Computer Science Major 

Students study a common core of fundamental topics, supplemented by a program of six electives that provides a high degree of flexibility. Three of the electives are chosen from a list of upper-level courses that represent area foundations within computer science. The remaining electives are selected from the complete list of upper-level computer science courses. Students are encouraged to work with their faculty advisor to create a plan tailored to fit their goals and interests. The department webpage provides several example programs for students interested in a variety of specific areas in computer science.

Our website is always the most current in terms of information and has many FAQs for students. Please view this here: cs.columbia.edu and contact [email protected] with any questions.

Student Advising  

Consulting advisers.

Undergraduate students will be assigned a CS Faculty Advisor from the list on the CS website - https://www.cs.columbia.edu/education/undergraduate/advisors/. Students will typically have the same advisor throughout their time in the program. However, students are encouraged to check this list at the start of every term to ensure their advisor remains the same. To reach out to your CS Faculty Advisor, please email first or visit during office hours. 

Enrolling in Classes  

Computer Science Department courses are needed by many student populations and are in high demand. To facilitate all COMS students getting the courses they need and distribute seats fairly, please refer to our policy - https://www.cs.columbia.edu/cs-course-registration-policy/ 

Preparing for Graduate Study

The department offers a number of options at the graduate level, including the MS Express. Please refer to our FAQs - https://www.cs.columbia.edu/education/admissions8/ - or email [email protected] with any questions.

Coursework Taken Outside of Columbia  

Advanced placement  .

The department grants 3 points for a score of 4 or 5 on the AP Computer Science A exam, along with an exemption from COMS W1004 Introduction to Computer Science and Programming in Java. However, we recommend that you take COMS W1004 before taking COMS W3134/W3137 Data Structures if you received a score of 4 or have not programmed in Java recently.

Barnard College Courses

Any course offered by the Computer Science @Barnard department can count towards degree requirements. Please refer to the major and minor program information pages for specific information.

Transfer Courses  

Up to four transfer courses are accepted toward the major. Up to two transfer courses are accepted toward the minor. Calculus, linear algebra, and probability/statistics courses can be transferred in addition to the four/two-course limits. Each course must be approved as equivalent by the faculty who teaches it at Columbia. Please refer to the guide here - https://www.cs.columbia.edu/education/undergraduate/#sec8

Study Abroad Courses

If you are considering studying abroad, please consult with the CS Advisor as soon as possible. Each course for potential incorporation into your CS major or minor must be approved as equivalent by the faculty who teaches it at Columbia.

Summer Courses  

Any Computer Science or approved cognate course offered during the summer session will count towards the degree, with the exception of online-only courses, which do not count towards degree requirements.

Undergraduate Research and Senior Thesis  

Undergraduate research in courses  .

COMS W3998 UNDERGRAD PROJECTS IN COMPUTER SCIENCE. 1.00-3.00 points .

Prerequisites: Approval by a faculty member who agrees to supervise the work.

Independent project involving laboratory work, computer programming, analytical investigation, or engineering design. May be repeated for credit. Consult the department for section assignment.

COMS W4901 Projects in Computer Science. 1-3 points .

A second-level independent project involving laboratory work, computer programming, analytical investigation, or engineering design. May be repeated for credit, but not for a total of more than 3 points of degree credit. Consult the department for section assignment.

Senior Thesis Coursework and Requirements  

A thesis is not a requirement for the major or minor.

COMS W3902 UNDERGRADUATE THESIS. 0.00-6.00 points.

Prerequisites: Agreement by a faculty member to serve as thesis adviser. An independent theoretical or experimental investigation by an undergraduate major of an appropriate problem in computer science carried out under the supervision of a faculty member. A formal written report is mandatory and an oral presentation may also be required. May be taken over more than one term, in which case the grade is deferred until all 6 points have been completed. Consult the department for section assignment

Undergraduate Research Outside of Courses

Laboratory Facilities

The department has well-equipped lab areas for research in computer graphics, computer-aided digital design, computer vision, databases and digital libraries, data mining and knowledge discovery, distributed systems, mobile and wearable computing, natural language processing, networking, operating systems, programming systems, robotics, user interfaces, and real-time multimedia.

Research labs contain several large Linux and Solaris clusters; Puma 500 and IBM robotic arms; a UTAH-MIT dexterous hand; an Adept-1 robot; three mobile research robots; a real-time defocus range sensor; interactive 3-D graphics workstations with 3-D position and orientation trackers; prototype wearable computers, wall-sized stereo projection systems; see-through head-mounted displays; a networking testbed with three Cisco 7500 backbone routers, traffic generators; an IDS testbed with secured LAN, Cisco routers, EMC storage, and Linux servers; and a simulation testbed with several Sun servers and Cisco Catalyst routers.  The department uses a SIP IP phone system. The protocol was developed in the department.

The department's computers are connected via a switched 1Gb/s Ethernet network, which has direct connectivity to the campus OC-3 Internet and internet 2 gateways. The campus has 802.11b/g wireless LAN coverage.

The research facility is supported by a full-time staff of professional system administrators and programmers.

Participating in Research Projects

Students can reach out to professors whose research areas are of interest to them. Professors will typically require that students have completed the relevant coursework covering the background knowledge and skills. 

Once a faculty member agrees to supervise the student’s research work, the student will register for the professor’s section of COMS W3998 or W4901.

Independent project involving laboratory work, computer programming, analytical investigation, or engineering design. May be repeated for credit. Consult the department for section assignment

Department Honors and Prizes  

Department honors.

The Computer Science Department does not award departmental honors.

Academic Prizes  

Jonathan L. Gross Award for Academic Excellence: This award was established in 2017 in honor of the much loved Professor Emeritus Jonathan Gross. Each year a cash gift is awarded to one graduating masters student and to one graduating senior from each of the four undergraduate schools served by the Department of Computer Science. 

Theodore R. Bashkow Award: Presented to a computer science senior who has excelled in independent projects. This is awarded in honor of Professor Theodore R. Bashkow, whose contributions as a researcher, teacher, and consultant have significantly advanced the state of the art of computer science.

Andrew P. Kosoresow Memorial Award for Excellence in Teaching and Service: Awarded for outstanding contributions to teaching in the Department of Computer Science and exemplary service to the Department and its mission.

Computer Science Scholarship Award : A cash prize awarded to two B.A. and two B.S. degree candidates for outstanding academic achievement in computer science.

Russell C. Mills Award : This annual award, established by the computer science department in 1992 in memory of Russell C. Mills, is a cash prize given to a computer science major who has exhibited excellence in the area of computer science.

Other Important Information  

See the Requirements section for the policies on double counting and D grades.

Peter N. Belhumeur Steven M. Bellovin Luca Carloni Xi Chen Steven K. Feiner Luis Gravano Julia B. Hirschberg Gail E. Kaiser John R. Kender Tal Malkin Kathleen R. McKeown Vishal Misra Shree Kumar Nayar Jason Nieh Christos Papadimitriou Itsik Pe'er Toniann Pitassi Kenneth A. Ross Tim Roughgarden Daniel S. Rubenstein Henning G. Schulzrinne Rocco A. Servedio Simha Sethumadhavan Salvatore J. Stolfo Bjarne Stroustrup Vladimir Vapnik Jeannette Wing Junfeng Yang Mihalis Yannakakis Richard Zemei

Associate Professors

Alexandr Andoni Elias Bareinboim Augustin Chaintreau Stephen A. Edwards Roxana Geambasu Daniel Hsu Suman Jana Martha Allen Kim Baishakhi Ray Carl Vondrick Eugene Wu Zhou Yu Changxi Zheng Xia Zhou

Assistant Professors

Josh Alman Lydia Chilton Ronghui Gu Kostis Kaffes David Knowles Brian Smith Henry Yuen

Senior Lecturer in Discipline

• Adam Cannon
• Jae Woo Lee

Lecturer in Discipline

Daniel Bauer Brian Borowski Tony Dear

Associated Faculty Joint

Andrew Blumberg Shih-Fu Chang Feniosky Peña-Mora Clifford Stein

Shipra Agrawal Mohammed AlQuraishi Elham Azizi Paolo Blikstein Asaf Cidon Matei Ciocarlie Rachel Cummings Noemie Elhadad Javad Ghaderi Gamze Gursoy Xiaofan Jiang Ethan Katz-Bassett Hod Lipson Smaranda Muresan Liam Paninski Brian Plancher Mark Santolucito Lisa Soros Barbara Tversky Venkat Venkatasubramanian Rebecca Wright Gil Zussman

Senior Research Scientists

Gaston Ormazabal Moti Yung

Alfred V. Aho Peter K. Allen Edward G. Coffman Jr. Zvi Galil Jonathan L. Gross Steven M. Nowick Stephen H. Unger Henryk Wozniakowski Yechiam Yemini

Guidance for Undergraduate Students in the Department  

Program planning for all students.

The following requirements are new as of the academic year 2023-2024. Students who declared a CS major in the academic year 2022-2023 or earlier have the option to follow the old requirements. The old requirements are noted on the Undergraduate Programs pages of the Computer Science Department website ( https://www.cs.columbia.edu/education/undergraduate/ ). 

Please note that the information on the department website is more up-to-date than the information in the archived Bulletins. Students with questions about which requirements to follow are advised to talk with [email protected].

Restrictions on overlapping courses

Students may receive credit for only one of the following two courses:

COMS W1004 Introduction to Computer Science and Programming in Java

COMS W1005 Introduction to Computer Science and Programming in MATLAB.

Students may receive credit for only one of the following three courses:

COMS W3134 Data Structures in Java

COMS W3136 ESSENTIAL DATA STRUCTURES

COMS W3137 HONORS DATA STRUCTURES ＆ ALGOL

COMS W1005 and COMS W3136 cannot be counted towards the Computer Science major, minor, and concentration.

No more than 6 points of project/thesis courses (COMS W3902, W3998, W4901) can count toward the major. COMS W3999 Fieldwork cannot be used as a CS Elective.

No more than one course from each set below may be applied towards the computer science major:

 IEOR E3658, STAT UN1201, MATH UN2015

 MATH UN2015, MATH UN2010, APAM E3101, COMS W3251

 COMS W4771, COMS W4721

Double Counting

Double-counting policies are to be construed within the larger double-counting policy of the student's home school. Double-counting policies are detailed on each School's Bulletin and/or Catalog.

The CS department allows the following courses in the CS Core and Mathematics requirement to be double-counted with another major, minor, or concentration. No other courses can be double-counted with another program.

Any calculus courses (including Honors Math A and B)

One Linear Algebra course

One Probability/Statistics course

A maximum of one course worth no more than 4 points passed with a grade of D may be counted toward the major or minor.

Course Numbering Structure

The first digit indicates the level of the course, as follows: 

0 Course that cannot be credited toward any degree

1 Undergraduate course

2 Undergraduate course, intermediate

3 Undergraduate course, advanced

4 Graduate course that is open to qualified undergraduates

6 Graduate course

8 Graduate course, advanced

9 Graduate research course or seminar

Guidance for First-Year Students  

Pre-introductory courses.

COMS W1004 is the first course in the Computer Science major curriculum, and it does not require any previous computing experience.  Before taking COMS W1004, however, students have an option to start with one of the pre-introductory courses: ENGI E1006 or COMS W1002.

ENGI E1006 Introduction to Computing for Engineers and Applied Scientists is a general introduction to computing for STEM students.  ENGI E1006 is in fact a required course for all engineering students.  COMS W1002 Computing in Context is a course primarily intended for humanities majors, but it also serves as a pre-introductory course for CS majors.  ENGI E1006 and COMS W1002 do not count towards Computer Science major.

Guidance for Transfer Students  

Up to four transfer courses are accepted toward the major. Up to two transfer courses are accepted toward the minor or concentration. Calculus, linear algebra, and probability/statistics courses can be transferred in addition to the four/two-course limits.

Undergraduate Programs of Study

Major in computer science.

All majors should confer with their program adviser each term to plan their programs of study. Students considering a major in computer science are encouraged to talk to a program adviser during their first or second year. The Computer Science major is composed of four basic components: The Mathematics Requirement, the Computer Science Core, the Area Foundation Courses, and the Computer Science Electives.

Mathematics Requirement (6-11 points)

  Pre-intro course (Optional, 3-4 points)

Computer Science Core (20-21 points):

Area Foundation Courses (9 to 12 points):

Select three from the following list:

Computer Science Electives (9 to 12 points)

Any three COMS courses or jointly offered computer science courses such as CSXX or XXCS course that are worth at least 3 points and are at the 3000 level or above. This includes 3000-level courses offered by Barnard CS.

Restrictions

Major in computational biology.

For a description of the joint major in computer science—Biology, see the Biological Sciences section in this bulletin.

Major in Computer Science - Mathematics

For a description of the joint major in computer science—mathematics, see the Mathematics section in this bulletin.

Major in Information Science

The major in information science requires a minimum of 33 points, including a core requirement of five courses. Adjustments were made to the course lists below in March 2022.

The elective courses must be chosen with a faculty adviser to focus on the modeling and use of information within the context of a disciplinary theme. After discussing potential selections, students prepare a proposal of study that must be approved by the faculty adviser. In all cases, the six courses must be at the 3000 level or above, with at least three courses chosen from computer science. Following are some example programs. For more examples or templates for the program proposal, see a faculty adviser.

Note: In most cases, additional courses will be necessary as prerequisites in order to take some of the elective courses. This will depend on the student's proposed program of study.

Core Requirement

Following are some suggested programs of instruction:

Information Science and Contemporary Society

Students may focus on how humans use technology and how technology has changed society.

The requirements include:

Information Science and the Economy

Students may focus on understanding information modeling together with existing and emerging needs in economics and finance as well as algorithms and systems to address those needs.

Information Science and Health Sciences

Students may focus on understanding information modeling together with existing and emerging needs in health sciences, as well as algorithms and systems to address those needs.

Major in Data Science

In response to the ever-growing importance of "big data" in scientific and policy endeavors, the last few years have seen explosive growth in theory, methods, and applications at the interface between computer science and statistics. The statistics and computer science departments have responded with a joint major that emphasizes the interface between the disciplines.

Minor in Computer Science 

Students who pass the Computer Science Advanced Placement Exam A with a 4 or 5 will receive 3 points and an exemption from COMS W1004.

The Computer Science Minor consists of 6 courses as follows:

1. COMS W1004: Intro to computer science and programming in Java (3) or COMS W1007: Honors intro to comp sci (3)

2. COMS W3134: Data structures in Java (3) or COMS W3137: Honors data structures and algorithms (4)

3. COMS W3203: Discrete mathematics (4)

4. One course of the following:

COMS W3157: Advanced programming (4)

COMS W3261: Comp science theory (3)

CSEE W3827: Fundamentals of computer systems (3)

5. Any 3000-level or 4000-level COMS/CSXX/XXCS course of at least 3 points

6. Any 3000-level or 4000-level COMS/CSXX/XXCS course of at least 3 points OR one linear algebra or probability/statistics course from the following : APMA E3101, APMA E2101, MATH UN2010, MATH UN2015, IEOR E3658, STAT UN1201, STAT GU4001 or STAT GU4203 .

For students who entered Columbia in or before the 2023-24 academic year  

Concentration in computer science.

The concentration in computer science requires a minimum of 22-24 points, as follows:

COMS W1001 Introduction to Information Science. 3 points .

Basic introduction to concepts and skills in Information Sciences: human-computer interfaces, representing information digitally, organizing and searching information on the internet, principles of algorithmic problem solving, introduction to database concepts, and introduction to programming in Python.

COMS W1002 COMPUTING IN CONTEXT. 4.00 points .

CC/GS: Partial Fulfillment of Science Requirement

Introduction to elementary computing concepts and Python programming with domain-specific applications. Shared CS concepts and Python programming lectures with track-specific sections. Track themes will vary but may include computing for the social sciences, computing for economics and finance, digital humanities, and more. Intended for nonmajors. Students may only receive credit for one of ENGI E1006 or COMS W1002

COMS W1003 INTRO-COMPUT SCI/PROGRAM IN C. 3.00 points .

COMS W1004 Introduction to Computer Science and Programming in Java. 3 points .

A general introduction to computer science for science and engineering students interested in majoring in computer science or engineering. Covers fundamental concepts of computer science, algorithmic problem-solving capabilities, and introductory Java programming skills. Assumes no prior programming background. Columbia University students may receive credit for only one of the following two courses: 1004  or  1005 .

COMS W1005 Introduction to Computer Science and Programming in MATLAB. 3 points .

A general introduction to computer science concepts, algorithmic problem-solving capabilities, and programming skills in MATLAB. Assumes no prior programming background. Columbia University students may receive credit for only one of the following two courses: W1004  or  W1005 .

COMS W1011 INTERMED COMPUTER PROGRAMMING. 3.00 points .

COMS W1012 COMPUTING IN CONTEXT REC. 0.00 points .

COMS W1103 HONORS INTRO COMPUTER SCIENCE. 3.00 points .

COMS W1404 EMERGING SCHOLARS PROG SEMINAR. 1.00 point .

Pass/Fail only.

Prerequisites: the instructor's permission. Corequisites: COMS W1002 or COMS W1004 or COMS W1007 Corequisites: COMS W1004 ,COMS W1007, COMS W1002 Peer-led weekly seminar intended for first and second year undergraduates considering a major in Computer Science. Pass/fail only. May not be used towards satisfying the major or SEAS credit requirements

COMS W2702 AI in Context. 3.00 points .

An interdisciplinary introduction to the history, development and modern application of artificial intelligence in a variety of contexts. Context subjects and teaching staff will vary by semester

COMS W3011 INTERMED COMPUTER PROGRAMMING. 3.00 points .

COMS W3101 PROGRAMMING LANGUAGES. 1.00 point .

Prerequisites: Fluency in at least one programming language. Introduction to a programming language. Each section is devoted to a specific language. Intended only for those who are already fluent in at least one programming language. Sections may meet for one hour per week for the whole term, for three hours per week for the first third of the term, or for two hours per week for the first six weeks. May be repeated for credit if different languages are involved

COMS W3102 DEVELOPMENT TECHNOLOGY. 1.00-2.00 points .

Lect: 2. Lab: 0-2.

Prerequisites: Fluency in at least one programming language. Introduction to software development tools and environments. Each section devoted to a specific tool or environment. One-point sections meet for two hours each week for half a semester, and two point sections include an additional two-hour lab

COMS W3107 Clean Object-Oriented Design. 3.00 points .

Prerequisites: Intro to Computer Science/Programming in Java (COMS W1004) or instructor’s permission. May not take for credit if already received credit for COMS W1007.

Prerequisites: see notes re: points A course in designing, documenting, coding, and testing robust computer software, according to object-oriented design patterns and clean coding practices. Taught in Java.Object-oriented design principles include: use cases; CRC; UML; javadoc; patterns (adapter, builder, command, composite, decorator, facade, factory, iterator, lazy evaluation, observer, singleton, strategy, template, visitor); design by contract; loop invariants; interfaces and inheritance hierarchies; anonymous classes and null objects; graphical widgets; events and listeners; Java's Object class; generic types; reflection; timers, threads, and locks

COMS W3123 ASSEMBLY LANG AND COMPUT LOGIC. 3.00 points .

COMS W3132 Intermediate Computing in Python. 4.00 points .

Essential data structures and algorithms in Python with practical software development skills, applications in a variety of areas including biology, natural language processing, data science and others

COMS W3134 Data Structures in Java. 3 points .

Prerequisites: ( COMS W1004 ) or knowledge of Java.

Data types and structures: arrays, stacks, singly and doubly linked lists, queues, trees, sets, and graphs. Programming techniques for processing such structures: sorting and searching, hashing, garbage collection. Storage management. Rudiments of the analysis of algorithms. Taught in Java. Note: Due to significant overlap, students may receive credit for only one of the following three courses: COMS W3134 , COMS W3136 , COMS W3137 .

COMS W3136 ESSENTIAL DATA STRUCTURES. 4.00 points .

Prerequisites: ( COMS W1004 ) or ( COMS W1005 ) or (COMS W1007) or ( ENGI E1006 ) A second programming course intended for nonmajors with at least one semester of introductory programming experience. Basic elements of programming in C and C , arraybased data structures, heaps, linked lists, C programming in UNIX environment, object-oriented programming in C , trees, graphs, generic programming, hash tables. Due to significant overlap, students may only receive credit for either COMS W3134 , W3136 , or W3137

COMS W3137 HONORS DATA STRUCTURES ＆ ALGOL. 4.00 points .

Prerequisites: ( COMS W1004 ) or (COMS W1007) Corequisites: COMS W3203 An honors introduction to data types and structures: arrays, stacks, singly and doubly linked lists, queues, trees, sets, and graphs. Programming techniques for processing such structures: sorting and searching, hashing, garbage collection. Storage management. Design and analysis of algorithms. Taught in Java. Note: Due to significant overlap, students may receive credit for only one of the following three courses: COMS W3134 , W3136 , or W3137

COMS W3157 ADVANCED PROGRAMMING. 4.00 points .

Prerequisites: ( COMS W3134 ) or ( COMS W3137 ) C programming language and Unix systems programming. Also covers Git, Make, TCP/IP networking basics, C fundamentals

COMS W3202 FINITE MATHEMATICS. 3.00 points .

COMS W3203 DISCRETE MATHEMATICS. 4.00 points .

Prerequisites: Any introductory course in computer programming. Logic and formal proofs, sequences and summation, mathematical induction, binomial coefficients, elements of finite probability, recurrence relations, equivalence relations and partial orderings, and topics in graph theory (including isomorphism, traversability, planarity, and colorings)

COMS W3210 Scientific Computation. 3 points .

Prerequisites: two terms of calculus.

Introduction to computation on digital computers. Design and analysis of numerical algorithms. Numerical solution of equations, integration, recurrences, chaos, differential equations. Introduction to Monte Carlo methods. Properties of floating point arithmetic. Applications to weather prediction, computational finance, computational science, and computational engineering.

COMS W3251 COMPUTATIONAL LINEAR ALGEBRA. 4.00 points .

COMS W3261 COMPUTER SCIENCE THEORY. 3.00 points .

Prerequisites: ( COMS W3203 ) Corequisites: COMS W3134 , COMS W3136 , COMS W3137 Regular languages: deterministic and non-deterministic finite automata, regular expressions. Context-free languages: context-free grammars, push-down automata. Turing machines, the Chomsky hierarchy, and the Church-Turing thesis. Introduction to Complexity Theory and NP-Completeness

COMS W3410 COMPUTERS AND SOCIETY. 3.00 points .

Broader impact of computers. Social networks and privacy. Employment, intellectual property, and the media. Science and engineering ethics. Suitable for nonmajors

COMS E3899 Research Training. 0.00 points .

Research training course. Recommended in preparation for laboratory related research

COMS W3902 UNDERGRADUATE THESIS. 0.00-6.00 points .

COMS W3995 Special Topics in Computer Science. 3 points .

Prerequisites: the instructor's permission.

Consult the department for section assignment. Special topics arranged as the need and availability arise. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit.

Prerequisites: Approval by a faculty member who agrees to supervise the work. Independent project involving laboratory work, computer programming, analytical investigation, or engineering design. May be repeated for credit. Consult the department for section assignment

COMS W3999 FIELDWORK. 1.00-2.00 points .

May be repeated for credit, but no more than 3 total points may be used toward the 128-credit degree requirement. Only for SEAS computer science undergraduate students who include relevant off campus work experience as part of their approved program of study. Final report and letter of evaluation may be required. May not be used as a technical or nontechnical elective or as a GTE (general technical elective). May not be taken for pass/fail credit or audited

COMS E3999 Fieldwork. 1 point .

Prerequisites: Obtained internship and approval from faculty advisor.

May be repeated for credit, but no more than 3 total points may be used toward the 128-credit degree requirement. Only for SEAS computer science undergraduate students who include relevant off-campus work experience as part of their approved program of study. Final report and letter of evaluation required. May not be used as a technical or non-technical elective. May not be taken for pass/fail credit or audited.

COMS W4111 INTRODUCTION TO DATABASES. 3.00 points .

CC/GS: Partial Fulfillment of Science Requirement Prerequisites: COMS W3134, COMS W3136, or COMS W3137; or the instructor's permission.

Prerequisites: ( COMS W3134 ) or ( COMS W3136 ) or ( COMS W3137 ) or The fundamentals of database design and application development using databases: entity-relationship modeling, logical design of relational databases, relational data definition and manipulation languages, SQL, XML, query processing, physical database tuning, transaction processing, security. Programming projects are required

COMS W4112 DATABASE SYSTEM IMPLEMENTATION. 3.00 points .

Prerequisites: ( COMS W4111 ) and fluency in Java or C++. CSEE W3827 is recommended. The principles and practice of building large-scale database management systems. Storage methods and indexing, query processing and optimization, materialized views, transaction processing and recovery, object-relational databases, parallel and distributed databases, performance considerations. Programming projects are required

COMS W4113 FUND-LARGE-SCALE DIST SYSTEMS. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) and ( COMS W3157 or COMS W4118 or CSEE W4119 ) Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) and ( COMS W3157 or COMS W4118 or CSEE W4119 ) Design and implementation of large-scale distributed and cloud systems. Teaches abstractions, design and implementation techniques that enable the building of fast, scalable, fault-tolerant distributed systems. Topics include distributed communication models (e.g. sockets, remote procedure calls, distributed shared memory), distributed synchronization (clock synchronization, logical clocks, distributed mutex), distributed file systems, replication, consistency models, fault tolerance, distributed transactions, agreement and commitment, Paxos-based consensus, MapReduce infrastructures, scalable distributed databases. Combines concepts and algorithms with descriptions of real-world implementations at Google, Facebook, Yahoo, Microsoft, LinkedIn, etc

COMS E4115 PROGRAMMING LANG ＆ TRANSL. 3.00 points .

COMS W4115 PROGRAMMING LANG ＆ TRANSLATORS. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) and ( COMS W3261 ) and ( CSEE W3827 ) or equivalent, or the instructor's permission. Modern programming languages and compiler design. Imperative, object-oriented, declarative, functional, and scripting languages. Language syntax, control structures, data types, procedures and parameters, binding, scope, run-time organization, and exception handling. Implementation of language translation tools including compilers and interpreters. Lexical, syntactic and semantic analysis; code generation; introduction to code optimization. Teams implement a language and its compiler

COMS W4118 OPERATING SYSTEMS I. 3.00 points .

Prerequisites: ( CSEE W3827 ) and knowledge of C and programming tools as covered in COMS W3136 , W3157 , or W3101, or the instructor's permission. Design and implementation of operating systems. Topics include process management, process synchronization and interprocess communication, memory management, virtual memory, interrupt handling, processor scheduling, device management, I/O, and file systems. Case study of the UNIX operating system. A programming project is required

COMS W4119 COMPUTER NETWORKS. 3.00 points .

Introduction to computer networks and the technical foundations of the internet, including applications, protocols, local area networks, algorithms for routing and congestion control, security, elementary performance evaluation. Several written and programming assignments required

COMS W4121 COMPUTER SYSTEMS FOR DATA SCIENCE. 3.00 points .

Prerequisites: background in Computer System Organization and good working knowledge of C/C++ Corequisites: CSOR W4246 , STAT GU4203 An introduction to computer architecture and distributed systems with an emphasis on warehouse scale computing systems. Topics will include fundamental tradeoffs in computer systems, hardware and software techniques for exploiting instruction-level parallelism, data-level parallelism and task level parallelism, scheduling, caching, prefetching, network and memory architecture, latency and throughput optimizations, specialization, and an introduction to programming data center computers

COMS W4137 From Algorithmic Thinking to Development. 3.00 points .

Algorithmic problem-solving and coding skills needed to devise solutions to interview questions for software engineering positions. Solutions are implemented in Python, Java, C, and C . Approaches include brute-force, hashing, sorting, transform-and-conquer, greedy, and dynamic programming. Focus on experimentation and team work

COMS W4152 Engineering Software-as-a-Service. 3.00 points .

Modern software engineering concepts and practices including topics such as Software-as-a-Service, Service-oriented Architecture, Agile Development, Behavior-driven Development, Ruby on Rails, and Dev/ops

COMS W4153 Cloud Computing. 3.00 points .

Software engineering skills necessary for developing cloud computing and software-as-a-service applications, covering topics such as service-oriented architectures, message-driven applications, and platform integration. Includes theoretical study, practical application, and collaborative project work

COMS W4156 ADVANCED SOFTWARE ENGINEERING. 3.00 points .

Prerequisites: ( COMS W3157 ) or equivalent. Software lifecycle using frameworks, libraries and services. Major emphasis on software testing. Centers on a team project

COMS W4160 COMPUTER GRAPHICS. 3.00 points .

Prerequisites: ( COMS W3134 ) or ( COMS W3136 ) or ( COMS W3137 ) COMS W4156 is recommended. Strong programming background and some mathematical familiarity including linear algebra is required. Introduction to computer graphics. Topics include 3D viewing and projections, geometric modeling using spline curves, graphics systems such as OpenGL, lighting and shading, and global illumination. Significant implementation is required: the final project involves writing an interactive 3D video game in OpenGL

COMS W4162 Advanced Computer Graphics. 3 points .

Prerequisites: ( COMS W4160 ) or equivalent, or the instructor's permission.

A second course in computer graphics covering more advanced topics including image and signal processing, geometric modeling with meshes, advanced image synthesis including ray tracing and global illumination, and other topics as time permits. Emphasis will be placed both on implementation of systems and important mathematical and geometric concepts such as Fourier analysis, mesh algorithms and subdivision, and Monte Carlo sampling for rendering. Note: Course will be taught every two years.

COMS W4165 COMPUT TECHNIQUES-PIXEL PROCSS. 3.00 points .

An intensive introduction to image processing - digital filtering theory, image enhancement, image reconstruction, antialiasing, warping, and the state of the art in special effects. Topics from the basis of high-quality rendering in computer graphics and of low-level processing for computer vision, remote sensing, and medical imaging. Emphasizes computational techniques for implementing useful image-processing functions

COMS W4167 COMPUTER ANIMATION. 3.00 points .

Prerequisites: Multivariable calculus, linear algebra, C++ programming proficiency. COMS W4156 recommended.

Theory and practice of physics-based animation algorithms, including animated clothing, hair, smoke, water, collisions, impact, and kitchen sinks. Topics covered: Integration of ordinary differential equations, formulation of physical models, treatment of discontinuities including collisions/contact, animation control, constrained Lagrangian Mechanics, friction/dissipation, continuum mechanics, finite elements, rigid bodies, thin shells, discretization of Navier-Stokes equations. General education requirement: quantitative and deductive reasoning (QUA). 

COMS W4170 USER INTERFACE DESIGN. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) Introduction to the theory and practice of computer user interface design, emphasizing the software design of graphical user interfaces. Topics include basic interaction devices and techniques, human factors, interaction styles, dialogue design, and software infrastructure. Design and programming projects are required

COMS W4172 3D UI AND AUGMENTED REALITY. 3.00 points .

Prerequisites: ( COMS W4160 ) or ( COMS W4170 ) or the instructor's permission. Design, development, and evaluation of 3D user interfaces. Interaction techniques and metaphors, from desktop to immersive. Selection and manipulation. Travel and navigation. Symbolic, menu, gestural, and multimodal interaction. Dialogue design. 3D software support. 3D interaction devices and displays. Virtual and augmented reality. Tangible user interfaces. Review of relevant 3D math

COMS W4181 SECURITY I. 3.00 points .

Not offered during 2023-2024 academic year.

Prerequisites: COMS W3157 or equivalent. Introduction to security. Threat models. Operating system security features. Vulnerabilities and tools. Firewalls, virtual private networks, viruses. Mobile and app security. Usable security. Note: May not earn credit for both W4181 and W4180 or W4187

COMS W4182 SECURITY II. 3.00 points .

Prerequisites: COMS W4181 , COMS W4118 , COMS W4119 Advanced security. Centralized, distributed, and cloud system security. Cryptographic protocol design choices. Hardware and software security techniques. Security testing and fuzzing. Blockchain. Human security issues. Note: May not earn credit for both W4182 and W4180 or W4187

COMS W4186 MALWARE ANALYSIS＆REVERSE ENGINEERING. 3.00 points .

Prerequisites: COMS W3157 or equivalent. COMS W3827 Hands-on analysis of malware. How hackers package and hide malware and viruses to evade analysis. Disassemblers, debuggers, and other tools for reverse engineering. Deep study of Windows Internals and x86 assembly

COMS W4203 Graph Theory. 3 points .

Prerequisites: ( COMS W3203 )

General introduction to graph theory. Isomorphism testing, algebraic specification, symmetries, spanning trees, traversability, planarity, drawings on higher-order surfaces, colorings, extremal graphs, random graphs, graphical measurement, directed graphs, Burnside-Polya counting, voltage graph theory.

COMS W4205 Combinatorial Theory. 3 points .

Lect: 3. Not offered during 2023-2024 academic year.

Prerequisites: ( COMS W3203 ) and course in calculus.

Sequences and recursions, calculus of finite differences and sums, elementary number theory, permutation group structures, binomial coefficients, Stilling numbers, harmonic numbers, generating functions. 

COMS W4223 Networks, Crowds, and the Web. 3.00 points .

Introduces fundamental ideas and algorithms on networks of information collected by online services. It covers properties pervasive in large networks, dynamics of individuals that lead to large collective phenomena, mechanisms underlying the web economy, and results and tools informing societal impact of algorithms on privacy, polarization and discrimination

COMS W4231 ANALYSIS OF ALGORITHMS I. 3.00 points .

COMS W4232 Advanced Algorithms. 3.00 points .

Prerequisite: Analysis of Algorithms (COMS W4231).

Prerequisites: see notes re: points Introduces classic and modern algorithmic ideas that are central to many areas of Computer Science. The focus is on most powerful paradigms and techniques of how to design algorithms, and how to measure their efficiency. The intent is to be broad, covering a diversity of algorithmic techniques, rather than be deep. The covered topics have all been implemented and are widely used in industry. Topics include: hashing, sketching/streaming, nearest neighbor search, graph algorithms, spectral graph theory, linear programming, models for large-scale computation, and other related topics

COMS W4236 INTRO-COMPUTATIONAL COMPLEXITY. 3.00 points .

Prerequisites: ( COMS W3261 ) Develops a quantitative theory of the computational difficulty of problems in terms of the resources (e.g. time, space) needed to solve them. Classification of problems into complexity classes, reductions, and completeness. Power and limitations of different modes of computation such as nondeterminism, randomization, interaction, and parallelism

COMS W4241 Numerical Algorithms and Complexity. 3 points .

Prerequisites: Knowledge of a programming language. Some knowledge of scientific computation is desirable.

Modern theory and practice of computation on digital computers. Introduction to concepts of computational complexity. Design and analysis of numerical algorithms. Applications to computational finance, computational science, and computational engineering.

COMS W4242 NUMRCL ALGORTHMS-COMPLEXITY II. 3.00 points .

COMS W4252 INTRO-COMPUTATIONAL LEARN THRY. 3.00 points .

Prerequisites: ( CSOR W4231 ) or ( COMS W4236 ) or COMS W3203 and the instructor's permission, or COMS W3261 and the instructor's permission.

Possibilities and limitations of performing learning by computational agents. Topics include computational models of learning, polynomial time learnability, learning from examples and learning from queries to oracles. Computational and statistical limitations of learning. Applications to Boolean functions, geometric functions, automata.

COMS W4261 INTRO TO CRYPTOGRAPHY. 3.00 points .

Prerequisites: Comfort with basic discrete math and probability. Recommended: COMS W3261 or CSOR W4231 . An introduction to modern cryptography, focusing on the complexity-theoretic foundations of secure computation and communication in adversarial environments; a rigorous approach, based on precise definitions and provably secure protocols. Topics include private and public key encryption schemes, digital signatures, authentication, pseudorandom generators and functions, one-way functions, trapdoor functions, number theory and computational hardness, identification and zero knowledge protocols

COMS W4281 INTRO TO QUANTUM COMPUTING. 3.00 points .

Prerequisites: Knowledge of linear algebra. Prior knowledge of quantum mechanics is not required although helpful.

Introduction to quantum computing. Shor's factoring algorithm, Grover's database search algorithm, the quantum summation algorithm. Relationship between classical and quantum computing. Potential power of quantum computers.

COMS W4419 INTERNET TECHNOLOGY,ECONOMICS,AND POLICY. 3.00 points .

Technology, economic and policy aspects of the Internet. Summarizes how the Internet works technically, including protocols, standards, radio spectrum, global infrastructure and interconnection. Micro-economics with a focus on media and telecommunication economic concerns, including competition and monopolies, platforms, and behavioral economics. US constitution, freedom of speech, administrative procedures act and regulatory process, universal service, role of FCC. Not a substitute for CSEE4119. Suitable for non-majors. May not be used as a track elective for the computer science major.

COMS W4444 PROGRAMMING ＆ PROBLEM SOLVING. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) and ( CSEE W3827 ) Hands-on introduction to solving open-ended computational problems. Emphasis on creativity, cooperation, and collaboration. Projects spanning a variety of areas within computer science, typically requiring the development of computer programs. Generalization of solutions to broader problems, and specialization of complex problems to make them manageable. Team-oriented projects, student presentations, and in-class participation required

COMS W4460 PRIN-INNOVATN/ENTREPRENEURSHIP. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) or the instructor's permission. Team project centered course focused on principles of planning, creating, and growing a technology venture. Topics include: identifying and analyzing opportunities created by technology paradigm shifts, designing innovative products, protecting intellectual property, engineering innovative business models

COMS W4701 ARTIFICIAL INTELLIGENCE. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) and any course on probability. Prior knowledge of Python is recommended. Prior knowledge of Python is recommended. Provides a broad understanding of the basic techniques for building intelligent computer systems. Topics include state-space problem representations, problem reduction and and-or graphs, game playing and heuristic search, predicate calculus, and resolution theorem proving, AI systems and languages for knowledge representation, machine learning and concept formation and other topics such as natural language processing may be included as time permits

COMS W4705 NATURAL LANGUAGE PROCESSING. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) or the instructor's permission. Computational approaches to natural language generation and understanding. Recommended preparation: some previous or concurrent exposure to AI or Machine Learning. Topics include information extraction, summarization, machine translation, dialogue systems, and emotional speech. Particular attention is given to robust techniques that can handle understanding and generation for the large amounts of text on the Web or in other large corpora. Programming exercises in several of these areas

COMS W4706 Spoken Language Processing. 3 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) or the instructor's permission.

Computational approaches to speech generation and understanding. Topics include speech recognition and understanding, speech analysis for computational linguistics research, and speech synthesis. Speech applications including dialogue systems, data mining, summarization, and translation. Exercises involve data analysis and building a small text-to-speech system.

COMS W4721 MACHINE LEARNING FOR DATA SCI. 3.00 points .

COMS W4725 Knowledge representation and reasoning. 3 points .

Prerequisites: ( COMS W4701 )

General aspects of knowledge representation (KR). The two fundamental paradigms (semantic networks and frames) and illustrative systems. Topics include hybrid systems, time, action/plans, defaults, abduction, and case-based reasoning. Throughout the course particular attention is paid to design trade-offs between language expressiveness and reasoning complexity, and issues relating to the use of KR systems in larger applications. 

COMS W4731 Computer Vision I: First Principles. 3.00 points .

Prerequisites: Fundamentals of calculus, linear algebra, and C programming. Students without any of these prerequisites are advised to contact the instructor prior to taking the course. Introductory course in computer vision. Topics include image formation and optics, image sensing, binary images, image processing and filtering, edge extraction and boundary detection, region growing and segmentation, pattern classification methods, brightness and reflectance, shape from shading and photometric stereo, texture, binocular stereo, optical flow and motion, 2D and 3D object representation, object recognition, vision systems and applications

COMS W4732 Computer Vision II: Learning. 3.00 points .

Advanced course in computer vision. Topics include convolutional networks and back-propagation, object and action recognition, self-supervised and few-shot learning, image synthesis and generative models, object tracking, vision and language, vision and audio, 3D representations, interpretability, and bias, ethics, and media deception

COMS W4733 COMPUTATIONAL ASPECTS OF ROBOTICS. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136COMS W3137) Introduction to fundamental problems and algorithms in robotics. Topics include configuration spaces, motion and sensor models, search and sampling-based planning, state estimation, localization and mapping, perception, and learning

COMS W4735 VISUAL INTERFACES TO COMPUTERS. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) Visual input as data and for control of computer systems. Survey and analysis of architecture, algorithms, and underlying assumptions of commercial and research systems that recognize and interpret human gestures, analyze imagery such as fingerprint or iris patterns, generate natural language descriptions of medical or map imagery. Explores foundations in human psychophysics, cognitive science, and artificial intelligence

COMS W4737 Biometrics. 3 points .

Prerequisites: a background at the sophomore level in computer science, engineering, or like discipline.

In this course. we will explore the latest advances in biometrics as well as the machine learning techniques behind them. Students will learn how these technologies work and how they are sometimes defeated. Grading will be based on homework assignments and a final project. There will be no midterm or final exam. This course shares lectures with COMS E6737 . Students taking COMS E6737 are required to complete additional homework problems and undertake a more rigorous final project. Students will only be allowed to earn credit for COMS W4737 or COMS E6737 and not both.

COMS W4762 Machine Learning for Functional Genomics. 3 points .

Prerequisites: Proficiency in a high-level programming language (Python/R/Julia). An introductory machine learning class (such as COMS 4771 Machine Learning) will be helpful but is not required.

Prerequisites: see notes re: points

This course will introduce modern probabilistic machine learning methods using applications in data analysis tasks from functional genomics, where massively-parallel sequencing is used to measure the state of cells: e.g. what genes are being expressed, what regions of DNA (“chromatin”) are active (“open”) or bound by specific proteins.

COMS E4762 Machine Learning for Functional Genomics. 3.00 points .

This course will introduce modern probabilistic machine learning methods using applications in data analysis tasks from functional genomics, where massively-parallel sequencing is used to measure the state of cells: e.g. what genes are being expressed, what regions of DNA (“chromatin”) are active (“open”) or bound by specific proteins

COMS W4771 MACHINE LEARNING. 3.00 points .

Prerequisites: Any introductory course in linear algebra and any introductory course in statistics are both required. Highly recommended: COMS W4701 or knowledge of Artificial Intelligence. Topics from generative and discriminative machine learning including least squares methods, support vector machines, kernel methods, neural networks, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models and hidden Markov models. Algorithms implemented in MATLAB

COMS W4772 ADVANCED MACHINE LEARNING. 3.00 points .

Prerequisites: ( COMS W4771 ) or instructor's permission; knowledge of linear algebra & introductory probability or statistics is required. An exploration of advanced machine learning tools for perception and behavior learning. How can machines perceive, learn from, and classify human activity computationally? Topics include appearance-based models, principal and independent components analysis, dimensionality reduction, kernel methods, manifold learning, latent models, regression, classification, Bayesian methods, maximum entropy methods, real-time tracking, extended Kalman filters, time series prediction, hidden Markov models, factorial HMMS, input-output HMMs, Markov random fields, variational methods, dynamic Bayesian networks, and Gaussian/Dirichlet processes. Links to cognitive science

COMS W4773 Machine Learning Theory. 3 points .

Prerequisites: Machine Learning (COMS W4771). Background in probability and statistics, linear algebra, and multivariate calculus. Ability to program in a high-level language, and familiarity with basic algorithm design and coding principles.

Core topics from unsupervised learning such as clustering, dimensionality reduction and density estimation will be studied in detail. Topics in clustering: k-means clustering, hierarchical clustering, spectral clustering, clustering with various forms of feedback, good initialization techniques and convergence analysis of various clustering procedures. Topics in dimensionality reduction: linear techniques such as PCA, ICA, Factor Analysis, Random Projections, non-linear techniques such as LLE, IsoMap, Laplacian Eigenmaps, tSNE, and study of embeddings of general metric spaces, what sorts of theoretical guarantees can one provide about such techniques. Miscellaneous topics: design and analysis of data structures for fast Nearest Neighbor search such as Cover Trees and LSH. Algorithms will be implemented in either Matlab or Python.

COMS E4773 Machine Learning Theory. 3.00 points .

Theoretical study of algorithms for machine learning and high-dimensional data analysis. Topics include high-dimensional probability, theory of generalization and statistical learning, online learning and optimization, spectral analysis

COMS W4774 Unsupervised Learning. 3.00 points .

Prerequisites: Solid background in multivariate calculus, linear algebra, basic probability, and algorithms.

Prerequisites: see notes re: points Core topics from unsupervised learning such as clustering, dimensionality reduction and density estimation will be studied in detail. Topics in clustering: k-means clustering, hierarchical clustering, spectral clustering, clustering with various forms of feedback, good initialization techniques and convergence analysis of various clustering procedures. Topics in dimensionality reduction: linear techniques such as PCA, ICA, Factor Analysis, Random Projections, non-linear techniques such as LLE, IsoMap, Laplacian Eigenmaps, tSNE, and study of embeddings of general metric spaces, what sorts of theoretical guarantees can one provide about such techniques. Miscellaneous topics: design and analysis of datastructures for fast Nearest Neighbor search such as Cover Trees and LSH. Algorithms will be implemented in either Matlab or Python

COMS W4775 Causal Inference. 3.00 points .

Prerequisites: Discrete Math, Calculus, Statistics (basic probability, modeling, experimental design), some programming experience.

Prerequisites: see notes re: points Causal Inference theory and applications. The theoretical topics include the 3-layer causal hierarchy, causal bayesian networks, structural learning, the identification problem and the do-calculus, linear identifiability, bounding, and counterfactual analysis. The applied part includes intersection with statistics, the empirical-data sciences (social and health), and AI and ML

COMS E4775 Causal Inference. 3 points .

Prerequisites: (COMS4711W) and Discrete Math, Calculus, Statistics (basic probability, modeling, experimental design), Some programming experience

Causal Inference theory and applications. The theoretical topics include the 3-layer causal hierarchy,  causal bayesian networks, structural learning, the identification problem and the do-calculus, linear identifiability, bounding, and counterfactual analysis. The applied part includes intersection with statistics, the empirical-data sciences (social and health), and AI and ML.

COMS W4776 Machine Learning for Data Science. 3 points .

Prerequisites: ( STAT GU4001 or IEOR E4150 ) and linear algebra.

Introduction to machine learning, emphasis on data science. Topics include least square methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines kernel methods. Emphasizes methods and problems relevant to big data. Students may not receive credit for both COMS W4771 and W4776.

COMS W4824 COMPUTER ARCHITECTURE. 3.00 points .

COMS W4835 COMPUTER ORGANIZATION II. 3.00 points .

COMS E4899 Research Training. 0.00 points .

COMS W4910 CURRICULAR PRACTICAL TRAINING. 1.00 point .

COMS E4995 COMPUTER ARTS/VIDEO GAMES. 3.00 points .

Special topics arranged as the need and availability arises. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit. Consult the department for section assignment

COMS W4995 TOPICS IN COMPUTER SCIENCE. 3.00 points .

Prerequisites: Instructor's permission. Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section

COMS W4996 Special topics in computer science, II. 3 points .

Prerequisites: Instructor's permission.

A continuation of COMS W4995 when the special topic extends over two terms.

Computer Science - Electrical Engineering

CSEE W3826 FUNDAMENTALS OF COMPUTER ORG. 3.00 points .

CSEE W3827 FUNDAMENTALS OF COMPUTER SYSTS. 3.00 points .

Prerequisites: an introductory programming course. Fundamentals of computer organization and digital logic. Boolean algebra, Karnaugh maps, basic gates and components, flipflops and latches, counters and state machines, basics of combinational and sequential digital design. Assembly language, instruction sets, ALU’s, single-cycle and multi-cycle processor design, introduction to pipelined processors, caches, and virtual memory

CSEE W4119 COMPUTER NETWORKS. 3.00 points .

Introduction to computer networks and the technical foundations of the Internet, including applications, protocols, local area networks, algorithms for routing and congestion control, security, elementary performance evaluation. Several written and programming assignments required

CSEE W4121 COMPUTER SYSTEMS FOR DATA SCIENCE. 3.00 points .

Prerequisites: Background in Computer System Organization and good working knowledge of C/C++. Corequisites: CSOR W4246 Algorithms for Data Science, STAT W4203 Probability Theory, or equivalent as approved by faculty advisor. An introduction to computer architecture and distributed systems with an emphasis on warehouse scale computing systems. Topics will include fundamental tradeoffs in computer systems, hardware and software techniques for exploiting instruction-level parallelism, data-level parallelism and task level parallelism, scheduling, caching, prefetching, network and memory architecture, latency and throughput optimizations, specialization, and an introduction to programming data center computers

CSEE W4140 NETWORKING LABORATORY. 3.00 points .

Prerequisites: ( CSEE W4119 ) or equivalent. In this course, students will learn how to put principles into practice, in a hands-on-networking lab course. The course will cover the technologies and protocols of the Internet using equipment currently available to large internet service providers such as CISCO routers and end systems. A set of laboratory experiments will provide hands-on experience with engineering wide-area networks and will familiarize students with the Internet Protocol (IP), Address Resolution Protocol (ARP), Internet Control Message Protocol (ICMP), User Datagram Protocol (UDP) and Transmission Control Protocol (TCP), the Domain Name System (DNS), routing protocols (RIP, OSPF, BGP), network management protocols (SNMP, and application-level protocols (FTP, TELNET, SMTP)

CSEE W4823 Advanced Logic Design. 3 points .

Prerequisites: ( CSEE W3827 ) or a half semester introduction to digital logic, or the equivalent.

An introduction to modern digital system design. Advanced topics in digital logic: controller synthesis (Mealy and Moore machines); adders and multipliers; structured logic blocks (PLDs, PALs, ROMs); iterative circuits. Modern design methodology: register transfer level modelling (RTL); algorithmic state machines (ASMs); introduction to hardware description languages (VHDL or Verilog); system-level modelling and simulation; design examples.

CSEE W4824 COMPUTER ARCHITECTURE. 3.00 points .

Prerequisites: ( CSEE W3827 ) or equivalent. Focuses on advanced topics in computer architecture, illustrated by case studies from classic and modern processors. Fundamentals of quantitative analysis. Pipelining. Memory hierarchy design. Instruction-level and thread-level parallelism. Data-level parallelism and graphics processing units. Multiprocessors. Cache coherence. Interconnection networks. Multi-core processors and systems-on-chip. Platform architectures for embedded, mobile, and cloud computing

CSEE W4840 EMBEDDED SYSTEMS. 3.00 points .

Prerequisites: ( CSEE W4823 ) Embedded system design and implementation combining hardware and software. I/O, interfacing, and peripherals. Weekly laboratory sessions and term project on design of a microprocessor-based embedded system including at least one custom peripheral. Knowledge of C programming and digital logic required

CSEE W4868 SYSTEM-ON-CHIP PLATFORMS. 3.00 points .

Prerequisites: ( COMS W3157 ) and ( CSEE W3827 ) Design and programming of System-on-Chip (SoC) platforms. Topics include: overview of technology and economic trends, methodologies and supporting CAD tools for system-level design, models of computation, the SystemC language, transaction-level modeling, software simulation and virtual platforms, hardware-software partitioning, high-level synthesis, system programming and device drivers, on-chip communication, memory organization, power management and optimization, integration of programmable processor cores and specialized accelerators. Case studies of modern SoC platforms for various classes of applications

Computer Science - Biomedical Engineering

CBMF W4761 COMPUTATIONAL GENOMICS. 3.00 points .

Prerequisites: Working knowledge of at least one programming language, and some background in probability and statistics. Computational techniques for analyzing genomic data including DNA, RNA, protein and gene expression data. Basic concepts in molecular biology relevant to these analyses. Emphasis on techniques from artificial intelligence and machine learning. String-matching algorithms, dynamic programming, hidden Markov models, expectation-maximization, neural networks, clustering algorithms, support vector machines. Students with life sciences backgrounds who satisfy the prerequisites are encouraged to enroll

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What are the acceptance rates at various CS masters degree programs? [closed]

I'm an undergraduate student at a top CS school who is looking to apply for my Master's soon (in about a year or so). I am applying to my school's 5 year masters program as a backup, but I would prefer to switch schools just to get a different experience.

My first question has to do with the acceptance rates of various CS Masters degree programs. I found that it was very difficult to search for the acceptance rates for various schools that I am interested in. Some of those schools include Yale, Berkeley, Stanford, and CalTech. However, I was unable to find acceptance rates for any of these schools. Could someone point me to a resource that I could use, or just give me the acceptance rates? Also, are there any "underrated" schools that I should be aware of (and by underrated, I mean high acceptance rates and good departments)?

• graduate-admissions
• computer-science

• I'd recommend looking at the U.S. & World Report graduate school rankings (you have to pay for access). At top schools there is probably not too much difference in the qualifications required for prospective MS or PhD students when choosing among applications. –  Gabriel Southern Commented Nov 25, 2014 at 0:26
• Professional masters or research masters? –  JeffE Commented Dec 25, 2014 at 5:29

2 Answers 2

I suspect that most programs do not post their acceptance rates at the master's level. That's in part because they could be subject to wild fluctuations from year to year, as the number of applications change.

In particular, one other thing to consider: most graduate programs have a target of incoming students they want to enroll. They will generally admit only as many students as they feel they need to reach the goal. If students are accepting offers at higher-than-expected rates, they may lower the admit rate in subsequent years to compensate. Similarly, if they are under their target, they may admit a larger than expected pool.

Considering top-tier schools, my suspicion (although I don't have the numerical evidence) is that they get enough qualified applications that admissions now are partly a "lottery": if you meet the qualifications of a typical student, you may or may not get in, depending on the needs of the department in that particular year. So just do your best.

• 1 do you have any source for any of this? I've also heard masters programs described as cash cows where schools try to stuff them full, which runs opposite this answer. –  user18072 Commented Dec 24, 2014 at 21:58
• Depends on the school. Some milk the undergrads, some the grad students, some make each level pay its own way. –  keshlam Commented Dec 25, 2014 at 5:09

I found this link on Quora. It looks like top 30 programs all have nearly same acceptance rates hovering from 6% to 10%.

When you mean underrated, I understand it as underrated by public and does not trigger 'wow' to public which includes Big Ten schools like Purdue, Penn State, UMichigan, UMaryland, UMadison, and other schools like UCIrvine, UCSanDiego, UTAustin, Georgia Tech etc.

Overrated are Ivy Leagues for sure including Harvard, Columbia and revenue generator masters programs in USC, UPenn, Brown, Johns Hopkins etc. (for CS/IT)

Level (neither underrated nor overrated) are CMU, Stanford, UIUC, MIT, Caltech, Princeton etc.

Not the answer you're looking for? Browse other questions tagged graduate-admissions masters computer-science .

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Gre prep online guides and tips, graduate school acceptance rates: can you get in.

Even the most qualified and confident applicants worry about getting into grad school. But don’t panic! Graduate school acceptance rates, which give the percentage of applicants that were admitted to a particular school or program in an academic year, can help you determine how likely you are to get into a given program.  But where can you find grad school admissions statistics?

In this article, we’ll first investigate the trends and factors associated with graduate school acceptance rates. Then, we’ll take a look at some of the current acceptance rates and give you expert tips on how to find acceptance rates for your programs. Finally, we’ll show you how to determine your odds of getting into grad school.

Graduate School Acceptance Rates: Factors and Trends

Grad school acceptance rates are the same as any other acceptance rate: the lower the acceptance rate, the more selective the school or program is. Similarly, the higher the acceptance rate, the less selective the school or program is. As with undergrad acceptance rates, grad school acceptance rates vary widely, from extraordinarily selective (less than 5 percent) to incredibly lenient (nearly 100 percent).

Unlike undergrad rates, though, grad school acceptance rates are usually calculated for specific programs or departments and  not for entire universities. This is because with grad school, you are essentially applying to an individual program rather than an overall institution (as you did for undergrad).

Now that we’ve covered all of the basics, let’s look at a few key trends. Our research indicates there are three major factors that help determine grad school acceptance rates:

• School or program prestige
• Degree type
• Amount of funding

Let’s look at how each of these factors influences grad school acceptance rates.

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#1: School or Program Prestige

How prestigious a particular grad school or program is can affect its overall competitiveness and selectivity. In general, the more prestigious a program is, the more competitive it’ll be and thus the lower acceptance rate it’ll have.

An easy way to determine school or program prestige is to consult official rankings, such as those listed on  U.S. News . (Grad schools are typically ranked by field or program and   not by overall institution.)

For example, a 2017  U.S. News  list of the best political science grad programs  ranked Duke’s political science program at #7 and Northwestern’s at #23. Because both of the programs have fairly high rankings, it’s safe to assume they’re probably quite selective.

And this is true: in 2016,  Duke  reported a mere 10 percent acceptance rate to its political science doctoral program, while  Northwestern  reported a 12 percent acceptance rate.

#2: Degree Type

Another major factor is degree type. Generally,  doctoral programs tend to be more selective than master’s programs (though this isn’t always the case as I’ll explain in a moment). This trend is likely due to the fact that doctoral programs often look for higher-quality applicants with proven academic track records and more relevant experience in their fields.

For example, in 2016  University of Michigan’s math doctoral program  had a 17.2 percent acceptance rate, whereas its master’s program  had a much higher 31.8 percent rate. In this case, the doctoral program is clearly tougher to get into than the master’s program.

Still,   master’s programs can have lower acceptance rates than doctoral programs. If we were to take the University of Michigan’s grad programs in computer science and engineering, we’d find that the doctoral program has  a 15 percent acceptance rate  and the master’s  an even lower 8 percent acceptance rate .

Additionally, M.F.A. programs are particularly cutthroat. In 2015, the creative writing M.F.A. program at UT Austin’s James A. Michener Center for Writers only admitted 12 out of 678 applicants — that’s a mere 1.8 percent acceptance rate !

#3: Amount of Funding

Funding, too, plays a big role in how selective a grad program is.

Well-funded  programs typically receive more applications than those offering little to no aid, thereby raising their selectivity. Competition is especially fierce for fully funded programs — possibly because fewer people are willing to go into debt for grad school.

Compared to fully funded doctoral programs, fully funded master’s programs are somewhat rare and thus pretty competitive. UT Austin’s Creative Writing M.F.A. program, for instance, is not only a prestigious program but also one of the most well-funded Creative Writing M.F.A. programs in the country: it  offers full tuition remission and a $27,500 stipend per academic year . It’s no wonder, then, that its acceptance rate is below 2 percent!

What Are the Current Graduate School Acceptance Rates?

For this section, we’ve scoured the internet to bring you a robust assortment of acceptance rates for popular U.S. grad schools.

Before we dive in, note that not all institutions calculate grad school acceptance rates using the same methodologies. Some offer only a single acceptance rate for all of their grad schools put together, while others offer individual rates by school, field, or program.

Now, let’s see how selective these schools really are!

*Statistics for NYU are based on the number of enrolled students and not the number of admitted students. Therefore, expect actual acceptance rates to be slightly higher.

How to Find Graduate School Acceptance Rates: 4 Methods

Unfortunately, grad school admissions statistics tend to be more difficult to find than undergrad acceptance rates.  But there are ways to search for them — you just have to do a lot of digging and possibly a little reaching out.

Below are our top four methods for finding grad school acceptance rates for the programs you’re applying to.

#1: Consult School Websites

By far the most reliable resources for grad school admissions statistics are  school websites.

Start your search by consulting program and departmental pages, particularly admissions and FAQ pages. Look out for any statistics-related keywords or phrases, such as “admission(s) rates,” “acceptance rates,” “enrollment,” “facts and figures,” etc. Use ctrl+F to move swiftly through large chunks of text.

Not all schools publish grad admissions information online, and those that do don’t always report it in the same way as others. For example, Princeton offers a handy PDF  containing acceptance rates for all academic fields of study. On the other hand,  Notre Dame  gives separate admissions charts for each of its grad programs (which you can access by selecting a program and then clicking “Admissions Statistics”).

Additionally, many schools release admissions statistics without explicitly publishing acceptance rates.  In this case, it’s your job to take the statistics provided and use them to calculate an acceptance rate. To find the acceptance rate of a school or program, you’ll need the following information:

• The total number of applicants in a year
• The total number of applicants granted admission  that year

The acceptance rate equals the total number of applicants offered admission divided by the total number of applicants and then multiplied by 100, or:

$$\acceptance \rate = {\number \of \applicants \offered \admission}/{\total \number \of \applicants}100$$

Be sure to  avoid conflating the number of students who were  offered admission   with the number of students who accepted their offers of admission. These two concepts sound alike but are actually different. What you’re looking for is the first statistic — that is, the number of admitted students (regardless of whether they decided to enroll).

If you’re having trouble finding admissions statistics by browsing school websites, search on Google for “[Your School] graduate acceptance rate” and see if any relevant school pages appear. While searching for acceptance rates to use in the table above, I consistently swapped “acceptance rate” with similar phrases, such as “admission(s) rate,” “facts and figures,” “student statistics,” “admittance rates,” and “admission(s) statistics.”

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Don’t be afraid to get creative! You can also use phrases like “Ph.D. admissions statistics” or “master’s admissions statistics” to narrow your search even further. Try to think outside the box as you do your research. What are other ways people talk about acceptance rates?

#2: Check  U.S. News

If your school or program doesn’t offer any admissions statistics on its website, go to  U.S. News . This website offers official rankings of grad programs as well as lists of the most (and least) selective programs in various fields.

For example, I found a 2016 list of the most competitive online M.B.A. programs  and a 2015 list of the most competitive online graduate engineering programs .

If U.S. News doesn’t offer any relevant lists for you to use, try skimming the current grad school rankings to gauge how competitive your program is compared with others in the same field.

#3: Search Other Websites

One less reliable method for looking up grad school admissions statistics is to  look for (unofficial) websites discussing acceptance rates for your school or program.

The Grad Cafe’s  admissions results  section is a solid place to start. Here, applicants post whether they’ve been accepted, rejected, or waitlisted for grad programs.

Search for your program to get a rough feel for how many acceptances and rejections go out each year. You might notice that certain types of applicants are more active than others. Creative Writing M.F.A. applicants, for example, are prolific posters in winter and spring (during admissions season).

Occasionally, Google itself will provide you with grad school acceptance rates, but this only appears to work consistently for well-known law schools, medical schools, and business schools.

Additionally, while using Google, don’t assume that any acceptance rates that pop up are directly connected to your search terms. For example, when I searched “stanford graduate acceptance rate,” Google gave me this result:

This 4.8 percent acceptance rate is  not  the acceptance rate for Stanford’s grad programs (what I searched for) but rather the acceptance rate for undergrads. So always cross-check any statistics Google gives you.

You can also consult grad school data websites such as  Peterson’s and StartClass . Take their grad school acceptance rates with a grain of salt, though — their data isn’t always verifiable online. If possible, try to compare any data you find on these types of websites with the school websites themselves or U.S. News .

#4: Contact Schools

If the internet isn’t giving you the help you need, call or email your schools. Be polite but upfront: ask whether the school calculates acceptance rates for grad programs and where you can find this information online (if available).

If a school refuses to divulge admissions statistics or simply doesn’t report acceptance rates, see if they can give you estimates for how many applications they receive each year, or for how many acceptances they usually extend to applicants in your program.

Graduate School Acceptance: What Are Your Odds?

By this point, you might be wondering how likely it is you’ll actually get into the grad program you wish to attend. After all, acceptance rates are pretty broad — they tell you what everyone’s odds are but not your odds specifically.

Below are three easy steps for determining your odds of getting into grad school, including advice on when it’s better to go for it or choose another program.

Step 1: Check Program Requirements

First, go to your program’s website and pinpoint the admissions requirements page. Now, ask yourself:  do you meet all of the program’s basic requirements? If not, you’ll likely wind up with a rejection (and might not even be able to apply).

However, if you’re still interested in applying, contact the program and ask if they’ll make an exception for you. Your chance of getting accepted is still low, but you’ll at least have your application considered.

If your program strongly recommends qualities you lack, don’t interpret this as an automatic rejection. Sometimes, applicants can make up for deficiencies in other ways. For example, if your undergrad GPA is 3.1 and your program recommends applicants have a minimum 3.2, don’t write off the program — you might still have a shot at getting in as long as the rest of your application is solid.

On the other hand, even if you meet all of a program’s requirements, you’re not necessarily a shoo-in. Remember, all other applicants have met these requirements, too, so you’ll need to find a unique way to make your application stand out.

Step 2: Find Average GRE Scores and GPAs

Your next step is to look up your program’s average GRE scores and GPA  to see how your own scores and GPA compare with those of previously admitted applicants.

You can usually find GRE score information on admissions requirements or FAQ pages. You can also search on Google for “[Your School] [Your Program] average GRE scores.” For step-by-step instructions on how to find average GRE scores, check out  my article on average GRE scores by school .

For GPAs, you can use the same basic methodology. Check admissions requirements and FAQ pages and use ctrl+F to search for “GPA.” If GPA information is available, you’ll most likely come across minimum GPAs or average GPAs (or both). For more tips on how to find GPA information for your grad schools, read our guide .

Now, compare your own GRE scores and GPA with the averages you’ve found. Below are all possible scenarios and what they mean for you and your odds of getting into the program:

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• Your GRE scores and GPA are both  higher than your program’s averages:  Congratulations! You have an excellent chance of getting accepted, especially if the rest of your application is equally impressive. Keep up the great work!
• Your GRE scores and GPA are both  about the same as your program’s averages:  You’re doing pretty well! You are just the type of applicant your program is looking for. The only drawback is that you probably won’t stand out as much from other applicants who have similar GRE scores and GPAs. So take time to make your application sparkle (I’m looking at you, statement of purpose).
• Your GRE scores and GPA are both lower than your program’s averages (or just one of the two is lower):  It ain’t over ’til it’s over! You can still make up for your deficiencies in other ways. While you can’t change your GPA, you can retake the GRE . If your GPA is low, a great strategy for combating this is to discuss it in your statement of purpose, taking care to highlight any external factors that contributed to the low GPA as well as any attributes of yours that prove you’re indeed ready for grad school.

Step 3: Decide Whether to Apply

Now, we get to the final question: do you apply to the program or not?  This is a vague question that’s difficult to answer as is. The real questions you should be asking yourself are as follows:

• Do I meet all of the program’s basic requirements?
• Do I meet most or all of the program’s expectations of applicants (in terms of GRE scores, GPA, etc.)?
• Is the program’s acceptance rate extremely low?
• Do I really like this program?

Although acceptance rates and GRE/GPA comparisons are helpful, don’t base your decision to apply solely on how difficult the program is to get into. We can’t know for sure what kind of applicant a grad program is looking for or who they’re willing to make an exception for.

Take a moment to think deeply about how interested you are in this particular program. Be realistic about your chances of getting in — but don’t cross the line into pessimism. If you don’t meet most or all of a program’s expectations and you’re not super invested in it, consider applying elsewhere.

But if you meet some, most, or all of a program’s expectations and you’re extremely interested in enrolling, give the application a go. Remember, it’s totally normal (and even encouraged) to have a few reach schools. Plus, you’ll never get in if you don’t apply!

Key Takeaways: Graduate School Acceptance Rates

Grad school acceptance rates quantify for us the selectivity of grad schools and programs. More specifically, acceptance rates tell us  what percentage of applicants were offered admission to a particular grad school or program. 

With grad school, acceptance rates are often reported for individual schools or programs,  not  entire universities. Acceptance rates can vary widely depending on program prestige, the type of degree you’re seeking, and how much (or how little) funding a program offers.

Unlike undergrad acceptance rates, grad school acceptance rates are somewhat difficult to locate online. You can look for them using any of the following four methods:

• Peruse school websites
• Check grad school facts and lists on  U.S. News
• Browse other websites and forums such as The Grad Cafe
• Call or email your schools

When trying to determine your  odds of getting into a program, look at your program’s requirements as well as the average GPA and GRE scores of previously admitted applicants to your program. If your GRE scores and GPA are comparable to those of your program, you have a decent shot at getting accepted. If one or both are lower than your program’s averages, however, you can always try to  raise your GRE score  with a retake or address your GPA in your statement of purpose.

At the end of the day, what ultimately matters isn’t that you get accepted to a highly competitive grad program but that you make the right decision for you and you alone!

What’s Next?

Need help with your grad school application?  Learn about the most common grad school requirements  and get tips on how to write a grad school CV or resume !

Is your GPA good enough for grad school ?  Read our in-depth guide to learn how you can make up for a less-than-stellar GPA and ultimately raise your chances of getting into the school of your dreams.

Do you have to take the GRE for grad school ? When are grad school deadlines ?  Check out our guides for answers to these questions and more.

Ready to improve your GRE score by 7 points?

Author: Hannah Muniz

Hannah graduated summa cum laude from the University of Southern California with a bachelor’s degree in English and East Asian languages and cultures. After graduation, she taught English in Japan for two years via the JET Program. She is passionate about education, writing, and travel. View all posts by Hannah Muniz

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Any info on Penn/Columbia CS PhD Acceptance Rates?

Does anyone know the accept rate for Penn and for Columbia’s CS PhD programs for recent years (particularly for the past cycle)?

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Breaking Down Columbia University’s Graduate School Acceptance Rates

Getting accepted into an Ivy League graduate program like Columbia is highly competitive, but not impossible with proper planning. If you’re short on time, here’s a quick answer to your question: Columbia’s overall graduate school acceptance rate is around 6%, but rates vary by program from 3% to 14% .

In this comprehensive guide, we will dig into the acceptance rates for all of Columbia’s graduate and professional degree programs. You’ll find data on acceptance rates over time, factors that impact admission success, and tips to strengthen your Columbia application.

Columbia University Graduate Programs Overview

List of all graduate schools and programs.

Columbia University offers a wide range of graduate programs across various disciplines. Some of the notable graduate schools at Columbia University include the School of Engineering and Applied Science, the Mailman School of Public Health, the Graduate School of Arts and Sciences, and the Columbia Business School.

These schools offer programs in fields such as engineering, public health, arts and sciences, business, law, and many more. The university prides itself on its diverse and comprehensive selection of graduate programs, catering to the interests and career aspirations of students from different academic backgrounds.

            View this post on Instagram                         A post shared by Columbia University (@columbia)

Key application requirements and deadlines

When applying to Columbia University’s graduate programs, there are certain requirements and deadlines that prospective students should be aware of. While specific requirements and deadlines may vary depending on the program, most applications typically require a completed online application form, letters of recommendation, a statement of purpose, official transcripts, and standardized test scores (such as the GRE or GMAT).

It is important for applicants to carefully review the requirements for their desired program and ensure that all materials are submitted by the specified deadlines. Missing a deadline can significantly impact the chances of acceptance.

Average GPA and test scores of accepted students

As one of the most prestigious universities in the world, Columbia University attracts highly competitive applicants to its graduate programs . While there is no specific cutoff for GPA or test scores, the average GPA and test scores of accepted students provide insight into the level of academic achievement expected by the university.

For example, the average undergraduate GPA of admitted students to Columbia Business School is around 3.6 , and the average GMAT score is 729 , but it ranges from 550 to 780. However, it’s important to note that these averages can vary depending on the specific program and the applicant pool for a given year.

It is worth mentioning that the admissions process at Columbia University takes into account various factors beyond just GPA and test scores. The university also considers an applicant’s work experience, extracurricular involvement, letters of recommendation, and personal statement.

Admissions committees aim to create a diverse and well-rounded cohort of students, with a range of backgrounds and experiences.

For more detailed information on specific graduate programs and their acceptance rates, it is advisable to visit the official websites of the respective schools or programs at Columbia University. These websites provide comprehensive information on the application process, program requirements, and contact information for further inquiries.

Columbia Graduate School Acceptance Rates by Program

Lowest acceptance rates – medicine, law, business.

Columbia University is renowned for its rigorous and highly competitive graduate programs. Among the most competitive programs are those in medicine, law, and business. These programs have the lowest acceptance rates due to their high demand and limited number of available spots.

In the field of medicine, Columbia University’s College of Physicians and Surgeons receives thousands of applications each year, but only a small percentage of applicants are accepted . The program’s selectivity ensures that the best and brightest students are admitted, as they will go on to become future leaders in the medical profession.

The law school at Columbia is also highly selective, accepting only a fraction of applicants. With its prestigious faculty and comprehensive curriculum, the Columbia Law School attracts top candidates from around the world.

The competitive nature of the program ensures that students receive a world-class legal education.

Similarly, the Columbia Business School is highly competitive, accepting only a small number of applicants. With its emphasis on leadership and innovation, the business school attracts talented individuals who are looking to excel in the corporate world.

Moderate acceptance rates – arts, sciences, engineering

Columbia University’s graduate programs in arts, sciences, and engineering have moderate acceptance rates compared to the highly competitive programs mentioned above. These programs still attract a large number of applicants, but the acceptance rates are relatively higher due to a larger number of available spots.

The graduate program in arts at Columbia University offers a wide range of disciplines, including visual arts, performing arts, and creative writing. Although the program is competitive, it provides aspiring artists with a nurturing and stimulating environment to develop their skills and creativity.

In the field of sciences , Columbia University offers graduate programs in various disciplines such as biology, chemistry, physics, and computer science . These programs attract talented individuals who are passionate about advancing scientific knowledge and making groundbreaking discoveries.

Similarly, the engineering programs at Columbia University are highly regarded and attract students from around the world. The Department of Mechanical Engineering , for example, offers cutting-edge research opportunities and prepares students for careers in industries such as aerospace, automotive, and energy.

Highest acceptance rates – education, public health, social work

Columbia University ‘s graduate programs in education, public health, and social work have the highest acceptance rates among its graduate programs. These programs aim to train professionals who can make a positive impact on society and address the pressing needs of communities.

The graduate program in education at Columbia University prepares future educators, administrators, and policy makers to excel in the field of education. With a focus on research-based teaching practices and innovative approaches to learning, the program equips students with the knowledge and skills needed to shape the future of education.

The Mailman School of Public Health at Columbia University offers a range of graduate programs that address critical issues in public health. These programs attract individuals who are passionate about improving population health and reducing health disparities.

Columbia University’s School of Social Work is dedicated to training professionals who can address social and economic challenges faced by individuals, families, and communities. The program emphasizes social justice and provides students with the skills to advocate for vulnerable populations.

Interpreting and Improving Your Chances

When applying to graduate school, understanding the acceptance rates can be crucial in determining your chances of admission. In the case of Columbia University’s Graduate School, it is important to interpret these rates accurately and find ways to improve your odds.

By considering factors such as selectivity rates, test scores, GPA, personal statement, and experience, you can take proactive steps towards increasing your chances of acceptance.

How selectivity rates have changed over time

Over the years, Columbia University’s Graduate School has become increasingly competitive. It is essential to understand the changes in selectivity rates to gauge the level of competition you will be facing.

By analyzing historical data, you can gain insights into the trends and make informed decisions. Websites like collegefactual.com provide comprehensive data on acceptance rates for various programs at Columbia and other universities, allowing you to compare and contrast the selectivity rates over time.

Understanding your odds based on test scores, GPA

While selectivity rates provide a general overview, it is important to assess your individual circumstances. Your test scores, such as GRE or GMAT, and GPA play a significant role in the application process.

By researching the average scores of admitted students in your desired program, you can gauge where you stand and identify areas for improvement. Admission committees often consider these scores as a measure of your academic potential , so it is crucial to aim for scores that align with or exceed the averages.

Standing out through your personal statement and experience

Aside from academic achievements, your personal statement and experience can make a significant impact on your application. Use your personal statement to showcase your unique qualities, experiences, and aspirations that make you a standout candidate.

Emphasize how your background aligns with the program and highlight any relevant research or projects you have undertaken. Additionally, your professional experience, internships, and extracurricular activities can demonstrate your dedication, leadership skills, and passion for your chosen field.

Remember, Columbia University’s Graduate School seeks well-rounded individuals who can contribute to their academic community . By understanding the changing selectivity rates, evaluating your test scores and GPA, and highlighting your personal statement and experience, you can increase your chances of being accepted into your desired program.

Stay focused, work hard, and don’t be discouraged by the competitive nature of the admissions process. With the right preparation and determination, you can maximize your chances of success!

Application Tips to Get Into Columbia

Applying to graduate school can be a daunting process, but with the right strategies and preparation, you can increase your chances of getting accepted into Columbia University. Here are some application tips to help you stand out from the competition:

Choosing recommenders strategically

When selecting recommenders, it’s important to choose individuals who can speak to your strengths and abilities. Consider professors, mentors, or employers who have a good understanding of your academic or professional achievements.

It’s also helpful to choose recommenders who can provide specific examples of your work or contributions . This will give the admissions committee a better understanding of your potential and suitability for the program.

It’s also a good idea to meet with your recommenders beforehand to discuss your goals and aspirations . This will help them write a more personalized and impactful letter of recommendation. Providing them with your resume, transcript, and any relevant work samples can also help them craft a stronger recommendation.

Crafting a compelling and cohesive narrative

Your application materials, including your statement of purpose and resume, should tell a compelling story about your academic and professional journey. It’s important to highlight your unique experiences, skills, and achievements that align with the program you are applying to.

This will help the admissions committee understand why you are a good fit for Columbia University.

Take the time to reflect on your experiences and think about how they have shaped your goals and aspirations. Be sure to clearly articulate your motivations for pursuing graduate study and how Columbia University can help you achieve those goals.

Additionally, ensure that your narrative is cohesive and flows logically from one point to another. This will make it easier for the admissions committee to follow your story and understand your potential.

Avoiding common essay pitfalls and errors

When writing your essays, it’s important to avoid common pitfalls and errors that can weaken your application. One common mistake is not answering the prompt directly or failing to address all aspects of the question.

It’s crucial to carefully read and understand the essay prompt and ensure that your response is on point.

Another common pitfall is not proofreading your essays thoroughly. Spelling and grammar errors can detract from the overall quality of your application. Take the time to review and edit your essays multiple times, or consider asking someone else to proofread them for you.

This will help you catch any mistakes or areas that need improvement.

Additionally, be cautious about using clichés or generic language in your essays . The admissions committee wants to see your unique perspective and voice, so try to avoid using overly common phrases or ideas. Be authentic and let your personality shine through in your writing.

By strategically choosing recommenders, crafting a compelling narrative, and avoiding common essay pitfalls, you can increase your chances of getting accepted into Columbia University’s Graduate School .

Remember to start early, give yourself enough time to prepare your application materials, and showcase your unique qualities and experiences. Good luck!

Getting into an Ivy League graduate program is highly competitive, but taking the time to understand Columbia’s admissions by program can help you craft a stronger application. Focus on showing a match between Columbia’s offerings and your academic and professional goals and highlight the unique experiences you’ll bring to campus.

Maria Sanchez is the founder of the Save Our Schools March blog. As a former teacher and parent, she is passionate about equitable access to quality public education. Maria created the blog to build awareness around education issues and solutions after organizing a local march for public schools.

With a Master's in Education, Maria taught high school English before leaving her career to raise a family. As a parent, she became concerned about underfunded schools and over-testing. These experiences drove Maria to become an education advocate.

On the blog, Maria provides resources and policy insights from the dual perspective of an informed parent and former teacher. She aims to inspire others to join the movement for quality, equitable public education. Maria lives with her family in [city, state].

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MASTER OF SCIENCE PROGRAM

The Master of Science (MS) program is intended for people who wish to broaden and deepen their understanding of Computer Science. Columbia University and the New York City environment provide excellent career opportunities in multiple industries.

The program provides a unique opportunity to develop leading-edge in-depth knowledge of specific computer science disciplines. The department currently offers concentration tracks covering eight such disciplines. MS students are encouraged to participate in state-of-the-art research with our research groups and labs.

REQUIREMENTS

• Complete a total of 30 points (Courses must be at the 4000 level or above)
• Maintain at least a 2.7 overall GPA. (No more than 1 D is permitted). The full Academic Standing Policy can be found here .
• Complete the Columbia Engineering Professional Development & Leadership (PDL) requirement (Not applicable to CVN students)
• Satisfy breadth requirements
• Take at least 6 points of technical courses at the 6000 level
• At most, up to 3 points of your degree can be Non-CS/Non-track If they are deemed relevant to your track and sufficiently technical in nature. Please submit the course syllabus to your CS Faculty Advisor for review, and then forward the approval confirmation email to [email protected]

TRACK OPTIONS

Choose one of the tracks below, view each track webpage for details on requirements.

Columbia Video Network (CVN) students should also choose from one of the above tracks. For faculty advisement, please contact the assigned track advisors .

Cs ms faculty track advisors.

CS Faculty Advisors will be assigned after you select a track in Mice. If you do not yet have a Mice account but are a CS MS student, please contact [email protected] . Contact your Track Advisor to get special permission for any course not specifically approved on your CS track websites .

DEGREE PROGRESS CHECKLIST

Students should keep an updated copy of their Degree Progress Checklist on hand for any academic progress reviews with their Faculty and/or Admin advisor. This form will also be requested a few weeks before graduation to verify your program requirements are met.

HOW TO FILL OUT YOUR CHECKLIST:

• List all coursework that should be used to meet your CS MS program requirements.
• For all topics courses (COMS 4995 & COMS 6998 courses), remember to include the actual course title (for example, use “COMS 4995 Hacking for Defense” NOT “COMS 4995”).
• For all Projects courses (COMS 6901), include the name of the instructor and how many points the course was worth.
• For all Thesis courses (COMS 6902), include the name of the instructor and how many points the course was worth.
• For all Personalized Track students, include the name of your track advisor on your checklist.
• For Advanced standing – mark which courses are approved by SEAS Academics.
• For waived Required Track courses, list which course you are using to satisfy the points requirement for the waived course. 

TOPICS COURSES

If you are interested in applying a specialized Topics in Computer Science courses (COMS 4995 or COMS 6998) to your Track electives, please view Topics Courses by Track Approval . 

Students may take multiple sections of COMS 4995 and/or COMS 6998, as each topic title will vary by content each semester. If you aren’t sure if a course is the same, please email your MS Faculty Track Advisor.

No approval is required for the course to count as a General Elective.

A list of current and recent Topics Course Descriptions can be found here .

MS IN COMPUTER ENGINEERING

In addition to the Computer Science MS Program, we offer the Computer Engineering MS Program jointly with the Electrical Engineering Department. More information about the program can be found in the Computer Engineering section of the SEAS bulletin and on the Computer Engineering website .

DUAL MS IN JOURNALISM AND COMPUTER SCIENCE

Admitted students will enroll for a total of four semesters. In addition to taking classes already offered at the Journalism and Engineering schools, students will attend a seminar and workshop designed specifically for the dual degree program. The seminar will teach students about the impact of digital techniques on journalism; the emerging role of citizens in the news process; the influence of social media; and the changing business models that will support news gathering. In the workshop, students will use a hands-on approach to delve deeply into information design, focusing on how to build a site, section, or application from concept to development, ensuring the editorial goals are kept uppermost in mind. For more information, please visit the program website .

IMPORTANT AND USEFUL LINKS

• MS TRACK ADVISORS
• MS PROGRAM FAQ
• FIELDWORK/CPT FAQ
• COLUMBIA ENGINEERING RESEARCH OPPORTUNITIES
• COLUMBIA ENGINEERING PROFESSIONAL DEVELOPMENT & LEADERSHIP (PDL) PROGRAM
• COMPUTER SCIENCE ACADEMIC HONESTY POLICY

ADMISSIONS INFORMATION

• SEAS Admissions contact: [email protected]
• CS Admissions contact: [email protected]

Updated 09/04/2024

Find open faculty positions here .

Computer Science at Columbia University

Upcoming events, fall 2024 research fair.

Thursday 12:00 pm

Friday 12:00 pm

Careers Walk-In Hours

Friday 3:00 pm

, CS Careers

Hybrid Employer Info Session: FTI Delta

Tuesday 1:00 pm

In the News

Press mentions, dean boyce's statement on amicus brief filed by president bollinger.

President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”

This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.

I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.

Mary C. Boyce Dean of Engineering Morris A. and Alma Schapiro Professor

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UW-Milwaukee Academic Catalog

Computer science.

Computer Science, PhD

The Doctor of Philosophy, the highest degree offered by the University, is conferred in recognition of marked scholarship in a broad field of knowledge as well as distinguished critical or creative achievement within a special area of the general field (the special area being the subject of the doctoral dissertation). The Doctor of Philosophy (PhD) in Computer Science program in the College of Engineering and Applied Science (CEAS) is designed to meet the traditional high standards for such programs. The PhD in Computer Science is administered by the division of Computer Science in the department of Electrical Engineering and Computer Science. Some aspects of the program are delegated to the CEAS Graduate Office.

The program is flexible, allowing the student to develop a plan of studies tailored to meet individual needs. Evaluation of the study plan is based on its appropriateness as a computer science program, the availability within the University of appropriate course offerings, and the availability within the division of Computer Science of a faculty member who is qualified to serve as the student’s major professor.

The PhD degree requires a minimum of 66 credits beyond the baccalaureate, including a dissertation. The student must also satisfy a residence requirement.

Many of the courses leading toward graduate degrees in CEAS are offered in the late afternoon or evening. So, students can complete much of their coursework on a part-time basis.

Admission Requirements

Credits and courses, additional requirements, application deadlines.

Application deadlines vary by program, please review the application deadline chart for specific programs. Other important dates and deadlines can be found by using the One Stop calendars .

An applicant must meet  Graduate School requirements  plus these program requirements to be considered for admission to the program:

• Applicants holding a MS degree in computer science will generally be admitted without deficiencies. Applicants holding a BS degree in computer science may be admitted only if they are exceptionally strong, such as with a record including successful completion of courses normally taken at the graduate level in computer science.
• Applicants holding MS degrees from domains outside of computer science may be admitted with specific program-defined course deficiencies, provided that the deficiencies amount to no more than two courses. The student is expected to satisfy deficiency requirements within three enrolled semesters. The deficiencies are monitored by the Graduate School and the division of Computer Science. No course credits earned in making up deficiencies may be counted as program credits required for the degree. The mathematics preparation must generally include mathematics equivalent to MATH 231 . Otherwise, the made-up deficiencies must be sufficient to assure that the applicant is able to proceed with advanced work directed toward the doctoral degree.
• A minimum grade point average of 3.0 on the basis of 4.0, in the highest degree granted. An applicant with a master’s degree in engineering or computer science having a GPA of less than 3.0, but at least equal to 2.75, may be admitted if substantial evidence can be submitted demonstrating that the applicant has the capacity to perform satisfactory doctoral work.
• All applicants are required to submit a brief (1 or 2 page) statement describing their professional goals and at least two letters of reference.
• The Graduate Record Examination (GRE) is required for all international and domestic applicants.
• International students require proof of English language proficiency. Complete information is available at the  UWM Center for International Education .
• Applicants with a relevant master’s degree who intend to complete an additional master’s in Computer Science at UWM should announce their plans at the time of admission, and not later than the start of their second year into the PhD program.

Reapplication

A student who receives a master’s degree at UWM must formally apply for admission to the Graduate School as a doctoral student before continuing studies that will be credited toward the Doctor of Philosophy in Computer Science.

The minimum degree requirement is 66 graduate credits beyond the bachelor’s degree. The minimum credit  distribution of coursework to be undertaken must be as follows depending on the option selected.

The 6-credit requirement in mathematics and/or quantitative methods may be met by satisfactorily completing certain courses specified by the Department or by taking the minor in mathematics. When such courses also count for either the major or the minor area, the remaining credits may be taken as approved electives.

The student must achieve a 3.0 GPA separately in each of the following areas: the major area, the minor area, and the quantitative methods area.

The minor is normally in another area offered in the College or in the physical sciences or mathematics or in management sciences. Consideration of any other area as a minor requires the prior approval of the Department.

A minimum of 26 credits, excluding doctoral thesis, must be at the 700 level or higher.

Major Professor as Advisor

The Graduate School requires that the student must have a major professor to advise, supervise, and approve the program of study before registering for courses. The incoming student will be assigned to an initial Program Advisor at the time of admission. Prior to the completion of 12 credits (9 credits for part-time students), the student must select a major professor who will be the student’s thesis advisor. The student, in consultation with the major professor, develops a proposed program of studies which is submitted for approval. For subsequent changes, the student must file a revised program of study for approval.

Foreign Language

There is no foreign language requirement for the degree.

The program residence requirement is satisfied either by completing 8 or more graduate credits in two consecutive semesters, exclusive of summer sessions, or by completing 6 or more graduate credits in each of three consecutive semesters, exclusive of summer sessions.

Qualifying Examination

Each student in the program must take and pass a Qualifying Examination to demonstrate that the student is qualified for doctoral-level work. The Qualifying Examination is a written exam and is structured in two parts: Part 1 and Part 2. The examination is offered twice a year during the regular academic year. 

Students entering with only a bachelor’s degree or with a master’s degree in an area unrelated to their major may take the Qualifying Examination for the first time after earning 12 credits of graduate work at UWM and must successfully pass the exam before earning 30 credits of graduate work at UWM.

Students admitted after completing an appropriate master’s degree must take this examination no later than the semester immediately after 18 credits of graduate work have been earned at UWM.

A student may take the Qualifying Examination twice. On the first attempt, the student must attempt both Part 1 and Part 2 of the examination.

• If the student passes both parts, then the student has passed the entire examination and will be permitted to proceed toward the Doctor of Philosophy degree.
• If the student fails both parts, then the student must take the entire exam again at its next offering.
• If a student passes only one of the two parts, then the student must take the examination again at its next offering, but may choose to take only the part of the examination that was not passed on the first attempt.
• If a passing grade is not obtained on the second attempt of the Qualifying Examination, the student will not be permitted to proceed toward the Doctor of Philosophy degree.

A student who fails the qualifying exam twice is subject to dismissal from the PhD in Computer Science program. A student may appeal the failure and dismissal within 30 days of being notified of the failure. If the student does not appeal or the appeal is not granted, the College will recommend to the Graduate School that the student be dismissed. A student who is dismissed from the PhD in Computer Science program because of failing the qualifying exam may not be enrolled in the PhD in Computer Science program for a complete calendar year. This does not preclude the student from being enrolled in any other degree program offered by the University. A student who wishes to re-enroll in the program after a calendar year has passed must apply as any other student would, including payment of fees. A student readmitted after having failed the qualifying exam twice must take the qualifying exam in the first semester of matriculation and this will count as the student’s first attempt at the exam. The student may appeal this requirement prior to the first scheduled day of classes. If the student fails the qualifying exam on this first attempt, the student is permitted the customary second attempt as described above. All appeals must be in writing and directed to the CEAS Associate Dean for Academic Affairs.

Doctoral Program Committee

The Doctoral Program Committee is proposed by the major professor in consultation with the student and the department. The Committee must include at least five graduate faculty (three from major area, one from minor area, and one from any area, including the major and minor areas). The last member may be a person from outside the University (such as another university, a research laboratory, or a relevant industrial partner), provided that person meets Graduate School requirements. The Committee may have more than five members, provided that the majority of the Committee members are from the student’s major field.

Doctoral Preliminary Examination

A student is admitted to candidacy only after successful completion of the doctoral preliminary examination conducted by the Doctoral Program Committee. This examination, which normally is oral, must be taken before the completion of 48 credits of graduate work toward the Doctor of Philosophy degree in Computer Science and should be taken within the first seven years in the program. Prior to the examination, the student must present a proposal for a doctoral dissertation project. The examination may cover both graduate course material and items related to the proposed dissertation project.

Dissertation and Dissertator Status

The student must carry out a creative effort in the major area under the supervision of the major professor and report the results in an acceptable dissertation. The effort of the student and the major professor to produce the dissertation is reflected in the PhD in Computer Science program requirement that the student complete at least 18 credits of doctoral thesis. 

After the student has successfully completed all degree requirements except the dissertation, the student may enter Dissertator Status. Achieving Dissertator Status requires successful completion of the Doctoral Preliminary Examination and prior approval of the student’s advisor, the Doctoral Program Committee, and the Computer Science GPR of a dissertation proposal that outlines the scope of the project, the research method, and the goals to be achieved. Any proposal that may involve a financial commitment by the University also must be approved by the Office of the Dean. After having achieved Dissertator Status, the student must continue to register for 3 credits of doctoral thesis per semester during the academic year until the dissertation is completed.

Dissertation Defense

The final examination, which is oral, consists of a defense of the dissertation project. The doctoral defense examination may only be taken after all coursework and other requirements have been completed. The student must have Dissertator Status at the time of the defense.

All degree requirements must be completed within ten years from the date of initial enrollment in the doctoral program.

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Admission Steps

Computer science - phd, admission requirements.

Terms and Deadlines

Degree and GPA Requirements

Prerequisites

Additional standards for non-native english speakers, additional standards for international applicants.

For the 2025-2026 academic year

See 2024-2025 requirements instead

Fall 2025 quarter (beginning in September)

Priority deadline: February 14, 2025

Final submission deadline: June 16, 2025

International submission deadline: May 5, 2025

Winter 2026 quarter (beginning in January)

Final submission deadline: November 4, 2025

International submission deadline: September 8, 2025

Spring 2026 quarter (beginning in March)

Final submission deadline: February 3, 2026

International submission deadline: December 8, 2025

Summer 2026 quarter (beginning in June)

Final submission deadline: May 4, 2026

International submission deadline: February 23, 2026

Priority deadline: Applications will be considered after the Priority deadline provided space is available.

Final submission deadline: Applicants cannot submit applications after the final submission deadline.

Degrees and GPA Requirements

Bachelors degree: All graduate applicants must hold an earned baccalaureate from a regionally accredited college or university or the recognized equivalent from an international institution.

University GPA requirement: The minimum grade point average for admission consideration for graduate study at the University of Denver must meet one of the following criteria:

A cumulative 2.5 on a 4.0 scale for the baccalaureate degree.

A cumulative 2.5 on a 4.0 scale for the last 60 semester credits or 90 quarter credits (approximately two years of work) for the baccalaureate degree.

An earned master’s degree or higher from a regionally accredited institution or the recognized equivalent from an international institution supersedes the minimum GPA requirement for the baccalaureate.

A cumulative GPA of 3.0 on a 4.0 scale for all graduate coursework completed for applicants who have not earned a master’s degree or higher.

Prerequisite courses for the PhD include: COMP 1671 Introduction to Computer Science I, COMP 1672 Introduction to Computer Science II, COMP 2673 Introduction to Computer Science III, COMP 2300 Discrete Structures in Computer Science, COMP 2370 Introduction to Algorithms & Data Structures, and COMP 2691 Introduction to Computer Organization (or equivalent).

Official scores from the Test of English as a Foreign Language (TOEFL), International English Language Testing System (IELTS), C1 Advanced or Duolingo English Test are required of all graduate applicants, regardless of citizenship status, whose native language is not English or who have been educated in countries where English is not the native language. Your TOEFL/IELTS/C1 Advanced/Duolingo English Test scores are valid for two years from the test date.

The minimum TOEFL/IELTS/C1 Advanced/Duolingo English Test score requirements for this degree program are:

Minimum TOEFL Score (Internet-based test): 80

Minimum IELTS Score: 6.5

Minimum C1 Advanced Score: 176

Minimum Duolingo English Test Score: 115

Additional Information:

Read the English Language Proficiency policy for more details.

Read the Required Tests for GTA Eligibility policy for more details.

Per Student & Exchange Visitor Program (SEVP) regulation, international applicants must meet all standards for admission before an I-20 or DS-2019 is issued, [per U.S. Federal Register: 8 CFR § 214.3(k)] or is academically eligible for admission and is admitted [per 22 C.F.R. §62]. Read the Additional Standards For International Applicants policy for more details.

Application Materials

Transcripts, letters of recommendation.

Required Essays and Statements

We require a scanned copy of your transcripts from every college or university you have attended. Scanned copies must be clearly legible and sized to print on standard 8½-by-11-inch paper. Transcripts that do not show degrees awarded must also be accompanied by a scanned copy of the diploma or degree certificate. If your academic transcripts were issued in a language other than English, both the original documents and certified English translations are required.

Transcripts and proof of degree documents for postsecondary degrees earned from institutions outside of the United States will be released to a third-party international credential evaluator to assess U.S. education system equivalencies. Beginning July 2023, a non-refundable fee for this service will be required before the application is processed.

Upon admission to the University of Denver, official transcripts will be required from each institution attended.

Three (3) letters of recommendation are required.  Letters should be submitted by recommenders through the online application.

Essays and Statements

Personal statement instructions.

A personal statement of at least 300 words is required. Your statement should include information concerning your life, education, experiences, interests and reason for applying to DU.

Résumé Instructions

The résumé (or C.V.) should include work experience, research, and/or volunteer work.

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Financial Aid Information

Start your application.

Your submitted materials will be reviewed once all materials and application fees have been received.

Our program can only consider your application for admission if our Office of Graduate Education has received all your online materials and supplemental materials by our application deadline.

Application Fee: $65.00 Application Fee

International Degree Evaluation Fee: $50.00 Evaluation Fee for degrees (bachelor's or higher) earned from institutions outside the United States.

Applicants should complete their Free Application for Federal Student Aid (FAFSA) by February 15. Visit the Office of Financial Aid for additional information.