Best Graduate Programs in Computer and Information Sciences - Updated 2020

The Computer Science accepts both beginning and advanced graduate students for study and research leading to the degree of Master of Science in Engineering (M.S.E.) and Doctor of Philosophy (Ph.D.). These degree programs are sufficiently flexible to adapt to individual plans of study and research. The doctoral program in computer science combines course work and participation in original research. Most students enter the program with an degree in computer science, mathematics, or a related discipline.

Through the degree program in software engineering you develop an understanding of current technologies, such as databases, communication protocols, cloud computing, and web frameworks and their application, Gain the ability to specify, design, implement, analyze, and improve software solutions from a wide variety of domains and learn how to adapt to new and emerging technologies.

The Master of Science (MS) program is intended for students planning to pursue a professional career directly after finishing the MS program, rather than continuing on in a PhD program. The MS program is also suitable for students interested in undertaking a research project but who are not sure yet whether they want to make the multi-year commitment to a PhD program.

The Masters Program in Computer Science (MPCS) offers a comprehensive and professionally-oriented computer science education that combines the foundations of computer science with the applied skills necessary for today's careers in technology. The MPCS is specially well suited for students interested in careers in software engineering, mobile computing, data analytics, and high-performance computing.

The Online Master of Science in computer science curriculum at Columbia places equal emphasis on theoretical computer science and mathematics and on experimental computer technology. A broad range of upper-level courses is available in such areas as artificial intelligence, computational complexity and the analysis of algorithms, combinatorial methods, computer architecture, computer-aided digital design, computer communications, databases, mathematical models for computation, optimization, and software systems.

Stanford University’s Electrical Engineering and Computer Science departments offer several areas of specialization. Their offer includes artificial intelligence, biocomputation, computer and network security, human-computer interaction, information management and analytics, mobile and internet computing, real-world computing, software theory, systems, and theoretical computer science. The program is also geared towards developing students’ professional abilities by allowing them to interact with industry and academic researchers and professionals.

The programming fundamentals in computer science online course program will introduce students to a powerful problem-solving process—the Seven Steps—which they can use to solve any programming problem. In this course, students will learn how to develop an algorithm, then progress to reading code and understanding how programming concepts relate to algorithms

The online Master of Computer and Information Technology (MCIT Online) degree is a first-of-its-kind graduate-level degree program in Computer Science tailored for non-Computer Science majors. The program requires the completion of 10 courses made up of six core courses and four electives. All courses are fully online and there are no required real-time sessions.

Our doctoral program is a full-time program: Admitted doctoral students have only the Ph.D. as their degree objective, and they have guaranteed funding for up to five years. All applicants should upload a scanned copy of their transcripts. Applicants should not send an official transcript to the Graduate Office unless they are admitted and accept our offer of Admission. Like many graduate programs, we base our admissions decisions primarily on the information requested above.

Biography: Greg Vorsanger is a Ph.D. candidate in Computer Science at Johns Hopkins University. Biography: Keith Levin is a Ph.D. candidate in Computer Science at Johns Hopkins University, where he works on graph inference, with applications to speech processing and neuroscience. Biography: Sureerat Reaungamornrat is a PhD candidate in Computer Science at Johns Hopkins University working under the supervision of Prof. Despite decades of research in the fields of robotics and computer vision, current approaches are inadequate for modeling complex activities outside of constrained environments or without using heavily instrumented sensor suites. Biography: Tuo Zhao is a Ph.D. Candidate in Computer Science at Johns Hopkins University working with Prof. Biography: Ali Uneri is a Ph.D. candidate in Computer Science at Johns Hopkins University. Biography: Nishikant is a Ph.D. candidate at The Johns Hopkins University, Computer Science and is being advised by Drs. Nishikant’s interest is in developing high‐performance systems with an application to medical imaging and computer integrated surgery. Biography: Samuel Carliles is a graduate student in the Computer Science. He has a BS and an MS in Computer Science from Johns Hopkins, and currently works as a Data Scientist at AppNexus, Inc. Biography: Michael Rushanan is a Ph.D. candidate in Computer Science at Johns Hopkins University.