The MS program, through course-work and thesis options, lets students strengthen their foundational education, prepare for technical careers in industry, or prepare for advanced study at the doctoral level. The department's faculty work in the areas of algorithms, artificial intelligence, bioinformatics, computer and network security, database systems, data mining, distributed algorithms, distributed systems, information assurance, information retrieval, machine learning, natural language processing, networking, non-standard parallel computing, and parallel algorithms.
Applicants to the Master of Science program must have a Bachelor's degree in computer science, computer engineering, information systems, electrical engineering, or a closely related field from an accredited academic institution. Candidates must have taken undergraduate courses on programming, data structures, hardware, architecture, algorithms, and mathematics, such as discrete mathematics, calculus, linear algebra, probability, and statistics.
They must have a grade-point average of at least 3.0 in their undergraduate studies. Applicants must provide the following documents:
- graduate school application forms.
- statement of academic, professional, and personal goals.
- three letters of recommendation.
- official transcripts from all previous academic institutions.
- official GRE scores.
- official TOEFL or IELTS scores, if necessary (see Item 7 of the Graduate School's checklist for required minimum scores).
Applicants to the Master's program must apply online.
Please take note of the Graduate School's application procedures and requirements checklist, and our answers to frequently asked questions. All applicants regardless of their qualifications must submit official GRE scores. In addition to sending required official transcripts and test scores, we strongly encourage applicants to upload unofficial copies of transcripts and test scores with their application.
Prospective applicants who lack the requisite background in computer science and mathematics will be considered for admission only after they have successfully completed preparatory courses in computer science and mathematics. People in this category can apply to the department's post-baccalaureate certificate program in computer science.
We do provide merit-based scholarships for Master's students. These provide support for student tuition, typically ranging from one course to three courses. Different research and teaching assistantships may be available to Master's students, but we make decisions regarding assistantships only after students have been in the program for at least one semester.
Students elect to complete the requirements for the degree by taking ten courses (30 credits) or by taking eight courses (24 credits) and writing a thesis. All students must take Algorithms (COSC-540) and Architecture (COSC-520). Students pursuing the course-work option take a total of eight electives to complete the degree. A generic schedule for a full-time student pursuing the course-work option appears in Table 1, which leads to a number of possible courses of study.
Note: The information on this page is intended for prospective students. Current students should refer to the Graduate Program Handbook for requirements, procedures, and policies.
Table 1: Hypothetical Schedule for Full-time, Course-work Option.
Students may choose as an elective any course numbered 400 or higher, but at least five of these classes must be numbered 500 or higher. The department regularly offers introductory or advanced electives in the areas of artificial intelligence, computer and network security, cryptography, database systems, data mining, information assurance, information retrieval, and machine learning.
Students may also elect to substitute up to two courses from another department for similarly numbered electives, provided that the courses support the student's plan of study and have been approved by the student's faculty advisor. We have prepared a list of approved external electives, and students may petition for the use of other courses as external electives.
Students choosing to write a thesis complete similar requirements, but substitute Graduate Thesis Research (COSC-999) for two electives numbered 400 or higher. That is, such students complete the core requirements, and take six electives numbered 400 or higher, for a total of twenty-four credit hours. Students selecting the thesis option must be in good academic standing when beginning the thesis. A hypothetical schedule for a full-time student pursuing the thesis option appears in Table 2.
Table 2: Hypothetical Schedule for Full-time, Thesis Option.
Graduate Learning Goals, Outcomes, Assessments
- To develop, improve, and maintain the program's leadership position nationally and internationally, the program aims to:
be nationally competitive in attracting high-quality students (P).
offer a robust and current curriculum that includes the fundamental areas of algorithms, systems, and theory and allied areas that reflect the research of its faculty (P).
provide effective mentoring that encourages students to graduate in a timely manner (P).
place graduates in positions in industry and academia (P).
maintain a nationally recognized faculty (P).
- To prepare students to conduct research effectively in CS, the program aims to provide a variety of educational experiences that will develop the ability to:
develop current knowledge in core and allied areas of CS, including the ability to read and review literature in an area of study (S)
identify research questions and problems that are pertinent to the field (S)
gather, organize, analyze, and report data using a conceptual framework appropriate to the research question (S)
- To enable students to develop as successful professionals for highly competitive positions in industry, government, and academia, the program aims to provide a variety of experiences that help students to:
achieve high levels of expertise in our core and allied areas of CS (S)
present research to regional, national, and international audiences (S)
participate in professional development activities (P)
- The graduate program in computer science will be nationally competitive and will attract high-quality students.
Assessment. The following data will be collected to assess applicant quality, recruitment success, and retention:
- undergraduate and Master's GPA and GRE scores of applicants and recruits.
- feeder schools of applicants and recruits.
- the number of students applying for fellowships (e.g., NSF).
- faculty funding rates (grants, contracts).
- faculty research productivity.
- national rankings of graduate programs in CS.
Implementation. The DGS will collect data annually for review by the Graduate Committee.
- The program will offer a robust and current curriculum that includes the fundamental areas of algorithms, systems, and theory and the areas of concentration that reflect the research of its faculty.
Assessment. The curriculum will be evaluated by reviewing the type and number of courses offered at the graduate-level and the undergraduate/graduate-level to assess availability and rigor of courses in the fundamental and elective areas. Courses will be evaluated by review of syllabi for key elements such as review of current literature and emerging problems and through university-developed student evaluations. Faculty members will maintain portfolios that include samples of student work (low, middle, and high scoring products) for review.
Implementation. The DGS will collect data on type and availability of courses on an annual basis for review by the Graduate Committee. Syllabi and student evaluations of courses in the fundamental areas will be collected by the DGS and reviewed by the Graduate Committee annually, and for the elective courses every three years. Portfolios of student work will be collected by the DGS for review by the Graduate Committee every two years.
- The program will provide effective mentoring that encourages students to graduate in a timely manner.
Assessment. Student progress to the degree will be tracked using data on enrollment patterns in core courses, time to qualifying exams, time to thesis proposal defense, and time to the dissertation defense. Faculty members will develop and maintain timelines with individual students.
Implementation. The DGS will maintain information about timelines in each student's file, updating it on an annual basis. Timelines will be reported to the Graduate Committee on an annual basis.
- The program will place graduates in positions in industry, government, and academia.
Assessment. The program will maintain data on student internships, job interviews, and job placement of graduates through the exit and alumni/ae surveys.
Implementation. The DGS will conduct an annual survey of internships and job interviews as well as an exit survey for each graduating student. The DGS will conduct alumni/ae surveys every 5 years. The Graduate Committee will review all data annually.
- The program will recruit and maintain a nationally recognized faculty.
Assessment. Faculty recruitment will be evaluated by review of applicant and recruitment data from faculty searches. Faculty retention will be evaluated by reviewing tenure and promotion data and longevity of faculty in the department.
Implementation. The Chair will collect and report faculty recruitment and retention data as relevant to the Graduate Committee via departmental meetings.
- The program will develop and support student participation in professional development activities.
Assessment. Student participation in professional development programs will include activities that focus on teaching, industrial internships, and mentoring. The number and breadth of experiences students undertake will be collected. Student evaluations of their experience, as well as evaluations of the student's performance (if available) will be collected.
Implementation. The DGS will survey students regarding their participation in professional development activities annually. For those students who have participated in these activities, the DGS will implement a student survey. The DGS will collect evaluation data on the student's performance as it is available. These data will be reviewed by the Graduate Committee annually.
- Students will have current knowledge of computer science in the fundamental areas of algorithms, systems, and theory.
Assessment. Student achievement will be evaluated in core courses based on grades and student work as well as success rates on the core component of the qualifying exam.
Implementation. The DGS will compile course evaluations, passage rates, and grade distributions of core courses and of the core component of the qualifying exam for review by the Graduate Committee annually. Portfolios of student work will be reviewed by the Committee every two years.
- Students will have knowledge of current research problems and practice in two elective areas of computer science.
Assessment. The DGS will compile student performance and evaluation data in courses and seminars in their chosen field of research and their performance on the area components of the qualifying exam.
Implementation. Each year, the Graduate Committee will examine course evaluations, passage rates, and grade distributions of elective courses and of the area components of the qualifying exam.
- Students will have oral, written, and mathematical communication skills sufficient for expressing concepts in computer science.
Assessment. For thesis proposals, proposal presentations, dissertations, dissertation defenses, and projects in courses, faculty members will evaluate each student's oral, written, and mathematical communication skills as appropriate. For each student, evaluators will complete a form indicating whether each student's oral, written, and mathematical communication skills are unacceptable, acceptable, or need improvement. In addition, assessment forms that include items that focus on communication skills will be distributed at public oral presentations.
Implementation. The DGS will compile evaluations of student communication skills from courses, proposals, dissertations, and presentations for review by the Graduate Committee annually.
- Students will have the ability to conduct independent research, including the ability to identify research questions and problems pertinent to their chosen field, and to gather, organize, analyze, and report data using a conceptual framework appropriate to the research question.
Assessment. For research projects in courses, faculty members will evaluate a student's ability to conduct independent research using an assessment form that includes items such as the identification of pertinent questions and problems, ability to gather and analyze data, and ability to report data and results. For thesis proposals, proposal presentations, dissertations, and dissertation defenses, the thesis advisor and committee members will use an assessment instrument that evaluates the student's ability to conduct independent research at the level of a dissertation. In addition, assessment forms that include items that focus on research skills will be distributed at public oral presentations. Data will be collected on quality and number of student research awards and on student publications in journals and conference proceedings.
Implementation. The DGS will collect course assessment materials for review by the Graduate Committee annually. Evaluations of thesis/dissertation writings will be collected as implemented and reviewed annually. The DGS will also present publication and research award data annually. Data collected from public oral presentations will be compiled by the DGS following the presentation and reviewed at the following Graduate Committee meeting. The results of these assessments will be communicated to the thesis advisor by the Chair within two weeks of the Committee meeting.
- Students will have a demonstrated ability to communicate their academic research including the ability to plan and write research papers and to design and deliver oral research presentations.
Assessment. For thesis proposals, dissertations, and project papers in courses, faculty members will evaluate each student's ability to plan and communicate about their academic research in written form using an assessment form. Data on quality and number of student conference and journal publications will be collected. For oral presentations in courses, thesis proposal presentations, and dissertation defenses, faculty members will evaluate the design and delivery of the presentations using an assessment form. In addition, assessment forms that include items that focus on presentation design and delivery will be distributed to audience members at public oral presentations.
Implementation. The DGS will collect assessments on course papers and presentations, as well as evaluations of thesis/dissertation written work for review by the Graduate Committee annually. The DGS will also present publication and research award data annually. Data collected from public oral presentations will be compiled by the DGS following the presentation and reviewed at the following Graduate Committee meeting. The results of these assessments will be communicated to the thesis advisor by the Chair within two weeks of the Committee meeting.
External Electives for MS
Students may substitute up to two courses from another department for similarly numbered electives, provided that the courses support the student's plan of study and have been approved by the student's faculty advisor. A list of approved external electives appears below. Students may petition for the use of other courses as external electives, but in addition to satisfying the previous criteria, such courses must be approved by the Department's curriculum committee. Students must obtain all necessary approvals before enrolling in the class.
List of Approved External Graduate Electives
Communication, Culture, and Technology
- CCTP-674 E-Government 2.0
- CCTP-728 Networks & Creative Process
Biostatistics and Epidemiology
- BIST-510 Probability and Sampling
- BIST-511 Statistical Inference
- BIST-530 Biostatistics for Bioinformatics
- BIST-531 Pattern Recognition
- LING-420 Statistical Natural Language Processing
- MATH-501 Probability Theory and Applications
- MATH-502 Deterministic Mathematical Models
- MATH-503 Mathematical Statistics
- MATH-504 Numerical Methods
- MATH-605 Introduction to Financial Mathematics
- MATH-656 Data Exploration and Data Mining
- PHYS-503 Computational Techniques
- PHYS-504 Numerical Simulation Techniques
- PHIL-402 Epistemology: An Historical Survey
- PHIL-438 Philosophy of Mind
- PHIL-491 Philosophy and Cognitive Science
- PHIL-624 Philosophical Logic
- PHIL-682 Mathematical Logic
- PHIL-725 Philosophy of Language
Science, Technology, and International Affairs
- STIA-402 Technology and Social Justice
- SEST-552 Information Technology and Security
- SEST-562 Emerging Technologies and Security
This school offers programs in:
Last updated December 18, 2017