Ohio University Graduate Catalog 20152017 [Archived Catalog]
Mathematics  MS


Return to: Programs Degree Title: Master of Science
Program Name and Numbers: Mathematics  MS3101, MS3111
Department/Unit: Department of Mathematics
Delivery Mode: Athens Campus
Program Mission: To train students to apply and disseminate mathematical knowledge and understanding.
Program Learning Objectives:
 Graduates will be able to apply a range of mathematical tools to problems within mathematics and in other disciplines.
 Graduates will be able to effectively disseminate mathematical knowledge and understanding orally, in writing, or by other means.
Program Overview:
A principal feature of the master’s program in mathematics is the possibility of designing a study plan to meet a student’s individual needs and interests. A master’s degree in mathematics can be used to fulfill several different goals, and the program meets this diversity of expectations in its several tracks. While the coursework varies somewhat, all tracks assure the student obtains a solid mathematical foundation and a rigorous and versatile training in analytic problem solving using mathematical tools. All tracks require at least 40 graduate credit hours and can normally be completed in two years.
Many master’s students are trained and financially supported as teaching assistants and have the opportunity to teach classes as the primary instructor.
Concentrations:
The Doctoral Preparation track (MS3101) is for students intending to continue to a doctoral program here or at another university.
The Applied track (MS3101) is for students who wish to use mathematics for careers in government or industry, or to pursue a doctoral degree in a field other than mathematics. Students develop skills in the formulation, analysis, and solution of mathematical models valuable for a variety of application areas.
The Computational track (MS3111) is for students who wish to use mathematics for careers in government or industry, with an emphasis on algorithms and software.
The General track (MS3101) is for students requiring more flexibility than permitted by the more specific tracks. It is important for students in this track to work with their advisor to assure their course choices prepare them for their intended career path.
Opportunities for Graduates: Depending on their track, students may continue their graduate education, work in government or industry, or teach at the college level.
Link to Program: http://www.ohio.edu/cas/math/grad/
Graduation Requirements:
Several policies should be noted for all tracks:
 In order to count toward the credit requirements, a grade of C or better is required for those credits. In particular, courses which award a grade of CR (credit) do not count.
 Mathematics courses crosslisted with undergraduate courses numbered under 4000 do not count.
 The training course MATH 5120 College Mathematics Teaching for New Teaching Assistants does not count.
 A maximum of 8 credits may be from nonregular courses such as thesis, project, or independent study.
 The number of transfer credits is limited by university policy.
 For requirements phrased in terms of courses, a course must be at least 3 credits and a grade of at least C is required.
 If a required course is equivalent to one that a student took elsewhere, they may substitute any course for which the required course is a (direct or indirect) prerequisite.
 Conferral of a master’s degree requires at least a B (3.0) grade point average (GPA) in those courses used to satisfy the requirements.
 A thesis is optional.
Doctoral Preparation track (MS3101):
 At least 40 graduate credit hours in mathematics.
 The core courses in two of the examination subjects in the doctoral program (PH3101).
Applied track (MS3101):
 At least 40 graduate credit hours, of which at least 30 must be in mathematics and at least 6 must be from another department where mathematics is applied.
 At least 3 mathematics courses above 5999.
 MATH 5600 Introduction to Numerical Analysis.
 At least two of:
 MATH 5530 Statistical Computing
 MATH 5610 Introduction to Waves and Wavelets with Applications
 MATH 5620 Linear and Nonlinear Optimization
 MATH 5630 Discrete Modeling and Optimization
 MATH 6640 Numerical Analysis: Linear Algebra
 MATH 6650 Numerical Analysis: Approximation Methods
 MATH 6660 Numerical Analysis: Differential Equations
Computational track (MS3111):
 At least 40 graduate credit hours, of which at least 20 must be in mathematics and at least 15 must be in computer science.
 At least 3 courses above 5999 in mathematics or computer science, of which at least 1 must be in mathematics and at least 1 in computer science.
 MATH 5600 Introduction to Numerical Analysis.
 At least two of:
 MATH 5530 Statistical Computing
 MATH 5610 Introduction to Waves and Wavelets with Applications
 MATH 5620 Linear and Nonlinear Optimization
 MATH 5630 Discrete Modeling and Optimization
 MATH 6640 Numerical Analysis: Linear Algebra
 MATH 6650 Numerical Analysis: Approximation Methods
 MATH 6660 Numerical Analysis: Differential Equations
 CS 5040 Design and Analysis of Algorithms
 CS 5060 Computation Theory
 CS 5420 Operating Systems and Computer Architecture I
General track (MS3101):
 At least 40 graduate credit hours in mathematics.
 At least 4 mathematics courses above 5999.
Admission Requirements:
 A bachelor’s degree in Mathematics or closely related field with minimum grade point average (GPA) 3.0 (on a 4.0 scale).
 Core undergraduate mathematics coursework such as single and multivariable calculus, linear algebra, ordinary differential equations, and introductory statistics.
 Upper level undergraduate mathematics coursework. No specific courses are required, but you need to demonstrate that you can succeed at courses at this level, including proofbased courses. A good model of courses to take is the Mathematics Major (B.S.) (BS3101) here.
 Nonnative English speakers must demonstrate English proficiency as specified in this graduate catalog.
For students applying to the Computational track, we also require:
Core undergraduate computer science coursework, such as Discrete Structures, Organization of Programming Languages, and Data Structures.
