Applied Mathematics and Statistics
201314 General Catalog
Baskin School of Engineering
(831) 4592158
http://www.soe.ucsc.edu
Program Description
Applied mathematics and statistics are disciplines devoted to the use of mathematical methods and reasoning to solve realworld problems of a scientific or decisionmaking nature in a wide variety of subjects, principally (but not exclusively) in engineering, medicine, the physical and biological sciences, and the social sciences. Applied mathematical modeling often involves the use of systems of (partial) differential equations to describe and predict the behavior of complex realworld systems that unfold dynamically in time. Statistics, construed broadly, is the study of uncertainty: how to measure it (using ideas and methods in probability theory), and what to do about it (using concepts from statistical inference and decision theory).
The Applied Mathematics and Statistics Department at UCSC offers master’s and doctoral programs in Statistics and Applied Mathematics, or Applied Mathematics and Statistics, depending on chosen emphasis. The goal of these programs is to help students develop into independent scholars who are prepared for productive careers in research, teaching, and industry. The department also offers a designated emphasis in statistics, a minor in statistics, and a minor in applied mathematics.
Additional information on these programs can be found on the department’s web pages at www.soe.ucsc.edu.
Undergraduate Programs
Requirements of the Minor in Statistics
The statistics minor is available for students who wish to gain a quantitative understanding of how to (a) measure uncertainty and (b) make good decisions on the basis of incomplete or imperfect information, and to apply these skills to their interests in another field. This minor could also be combined with a major in mathematics as preparation for a graduate degree in statistics or biostatistics.
Students are required to take a twoquarter basic calculus sequence:

Applied Mathematics and Statistics 11AB or Economics 11AB or Applied Mathematics and Statistics 15AB or Mathematics 11AB or Mathematics 19AB or Mathematics 20AB

Plus one course from each of the following seven categories:

Statistical Concepts: Applied Mathematics and Statistics 5 or 7/L

Computer Programming: Biomolecular Engineering 60/L or 160/L, Computer Science 12A/L or 5C or 5J or 5P or Computer Engineering 13/L

Linear Algebra: Applied Mathematics and Statistics 10 or Mathematics 21 (also recommended that students take Applied Mathematics and Statistics 20 or Mathematics 24)

Multivariate Calculus: Mathematics 22 or both Mathematics 23A and Mathematics 23B

Probability: Applied Mathematics and Statistics 131 Applied Mathematics and Statistics 203 or Computer Engineering 107

Statistical Inference: Applied Mathematics and Statistics 132 or Applied Mathematics and Statistics 206

Computational Methods: Applied Mathematics and Statistics 147


Plus two electives from the following category:

Statistical Elective: Applied Mathematics and Statistics 118; 156; 198; 202; 205B; 206B; 207; 256;

Biomolecular Engineering 205; Computer Engineering 108; Computer Science 142; Economics 104; 113; 114; 120; 161B; and 190; Electrical Engineering 151; Mathematics 114; Psychology 181; Sociology 103A; Technology and Information Management 230.

Note: Students planning graduate work in statistics are recommended to choose Mathematics 23AB, Applied Mathematics and Statistics 205B, and Mathematics 105AB.
Requirements of the Minor in Applied Mathematics
The applied mathematics minor is available for students who wish to develop (1) proficiency in modeling reallife problems using mathematics and (2) knowledge of standard, practical analytical and numerical methods for the solution of these models. This minor could be combined with a major in any of the physical, biological, mathematical, or engineering sciences as preparation for a graduate degree in that field or in applied mathematics.
Students are required to take the fourquarter calculus sequence:

Calculus Sequence: Mathematics 19AB or Mathematics 20AB, and Mathematics 23AB

Plus one of the following sequences:

Applied Mathematics and Statistics 10 and 20

Mathematics 21 and 24

Physics 116A and 116B

Note: Students who complete Mathematics 21 and 24 or Physics 116A and 116B, are strongly recommended to complete the MATLAB selfpaced tutorial at: http://matlabtraining.soe.ucsc.edu/

Plus one course from each of the following categories:

Probability Theory: Applied Mathematics and Statistics 131 or Computer Engineering 107

Dynamical Systems: Applied Mathematics and Statistics 114 or Applied Mathematics and Statistics 214

Introduction to Numerical Methods: Applied Mathematics and Statistics 147, Physics 115, or Earth Sciences 119

Partial Differential Equations: Applied Mathematics and Statistics 212A, Physics 116C, or Mathematics 107


Plus one appliedmathematics elective from the following list:

Applied Mathematics elective: Applied Mathematics and Statistics 107/217, 115/215, 118, 132, 198, 212B, 213, 216, 231, 290B

Electrical Engineering 103, 154; Computer Engineering 115; Mathematics 103A, 117, 121A; Physics 105, 139A, 139B, 171.

Students may also propose other electives which use applied mathematical methods, subject to approval by the department.
Graduate Programs (M.S., Ph.D.)
Requirements for a Graduate Degree in Statistics and Applied Mathematics
This track is for students emphasizing statistics. All students must complete the core courses described below.
Required core Applied Mathematics and Statistics courses:
200 Research and Teaching in Applied Mathematics and Statistics
203 Introduction to Probability Theory
205B Intermediate Classical Inference
206B Intermediate Bayesian Inference
207 Intermediate Bayesian Statistical Modeling
211 Foundations of Applied Mathematics
256 Linear Statistical Models
280B Seminar in Statistics and Applied Mathematical Modeling
In addition to these 35 credits, master of science (M.S.) students must complete two additional 5credit courses from the approved list, for a total requirement of 45 credits; doctor of philosophy (Ph.D.) students must complete four additional 5credit courses from the approved list, for a total requirement of 55 credits.
Requirements for a Graduate Degree in Applied Mathematics and Statistics
This track is for students emphasizing Applied Mathematics. All students must complete the core courses described below.
Required core Applied Mathematics and Statistics courses:
200 Research and Teaching in Applied Mathematics and Statistics
203 Introduction to Probability Theory
211 Foundations of Applied Mathematics
212A Applied Mathematical Methods I
212B Applied Mathematical Methods II
213 Numerical Solutions Differential Equations
214 Applied Dynamical Systems
280B Seminar in Statistical and Applied Mathematical Modeling
In addition to these 35 credits, master of science (M.S.) students must complete two additional 5credit courses from the approved list, for a total requirement of 45 credits; doctor of philosophy (Ph.D.) students must complete four additional 5credit courses from the approved list, for a total requirement of 55 credits.
For both emphasis tracks, M.S. students will be allowed to substitute up to two courses with their required research project in which they conduct a research program in one or two of the quarters of their second year. The project will consist of solving a problem or problems from the selected area of application and will be presented to the sponsoring faculty member as a written document.
Ph.D. students will be required to serve as teaching assistants for at least two quarters during their graduate study. Certain exceptions may be permitted for those with extensive prior teaching experience, for those who are not allowed to be employed due to visa regulations, or for other reasons approved by the graduate director.
Qualifying Examinations
At the end of the first year, all students will take a prequalifying examination covering the five or six (nonseminar) core courses. This examination will have two parts: an inclass written examination, followed by a takehome project involving data analysis. Students who do not pass this examination will be allowed to retake it before the start of the following fall quarter; if they fail the second examination they will be dismissed from the program.
Ph.D. students must complete the oral proposal defense, through which they advance to candidacy, by the end of the spring quarter of their third year. The proposal defense is a public seminar as part of an oral qualifying examination given by the qualifying committee.
Thesis and/or Dissertation Requirements
A capstone project is required for the M.S. degree and a dissertation for the Ph.D. degree.
For the M.S. degree, students will conduct a capstone research project in their second year (up to three quarters). Students must submit a proposal to the potential faculty sponsor by the start of the fourth academic quarter. If the proposal is accepted, the faculty member will become the sponsor and will supervise the research and writing of the project. The project will involve the solution of a problem or problems from the selected area of application. When the project is completed and written, it will be submitted to and must be accepted by a committee of two individuals, consisting of the faculty adviser and one additional reader. Additional readers will be chosen appropriately from within the Applied Mathematics and Statistics Department or outside of it. Either the adviser or the additional reader must be from within the Applied Mathematics and Statistics Department.
A dissertation is required for the Ph.D. degree. Ph.D. students must select a faculty research adviser by the end of the second year. A written dissertation proposal will be submitted to the adviser, and filed with the graduate secretary. A qualifying examination committee will be formed, consisting of the adviser and three additional members, approved by the Chair of the Graduate Program and the Dean of the Graduate Division. The student will submit the written dissertation proposal to all members of the committee and the graduate secretary no less than one month in advance of the qualifying examination. The dissertation proposal will be formally presented in a public oral qualifying examination with the committee, followed by a private examination.
Students will advance to candidacy after they have completed all course requirements (including removal of all incompletes), passed the qualifying examination, and paid the filing fee. Under normal progress, a student will advance to candidacy by the end of the spring quarter of her/his third year. A student who has not advanced to candidacy by the start of the fourth year will be subject to academic probation. Upon advancement to candidacy, a dissertation reading committee will be formed, consisting of the dissertation supervisor and at least two additional readers appointed by the Graduate Program chair upon recommendation of the dissertation supervisor. At least one of these additional readers must be in the Applied Mathematics and Statistics Department. The committee is subject to the approval of the Graduate Division.
The dissertation will consist of a minimum of three chapters composed of material suitable for submission and publication in major professional journals in statistics and stochastic modeling. The completed dissertation will be submitted to the reading committee at least one month before the dissertation defense, which consists of a public presentation of the research followed by a private examination by the reading committee. Successful completion of the dissertation defense is the final requirement for the Ph.D. degree.
Relationship of Masters and Doctoral Programs
The M.S. and Ph.D. programs are freestanding and independent, so that students can be admitted to either. Students completing the M.S. program may proceed into the Ph.D. program, and students in the Ph.D. program can receive a M.S. degree upon completion of M.S. requirements, including the capstone research project. Each Ph.D. student will be required to have knowledge of statistics and applied mathematics equivalent to that required for the M.S. degree. In addition, Ph.D. candidates will be required to complete coursework beyond the M.S. level.
Transfer Credit
Up to three School of Engineering courses fulfilling the degree requirements of either the M.S. or Ph.D. degrees may be taken before beginning the graduate program through the concurrent enrollment program. Ph.D. students who have previously earned a master’s degree in a related field at another institution may substitute courses from their previous university with approval of the adviser and the graduate committee. Courses from other institutions may not be applied to the M.S. degree course requirements.
Petitions should be submitted along with the transcript from the other institution or UCSC Extension. For courses taken at other institutions, copies of the syllabi, exams, and other course work should accompany the petition. Such petitions are not considered until the completion of at least one quarter at UCSC. At most, a total of three courses may be transferred from concurrent enrollment and other institutions.
Review of Progress
Each year, the faculty reviews the progress of every student. Students not making adequate progress toward completion of degree requirements are subject to dismissal from the program (see the Graduate Handbook for the policy on satisfactory academic progress). For specific guidelines on the annual student reviews, please refer to http://www.soe.ucsc.edu/programs/ssm/graduate/index.html.
Requirements for a designated emphasis to an external degree program
Students from another degree program who meet the following requirements can have the designated emphasis of “statistics” annotated to their degree title. For example, a Ph.D. student in electrical engineering who meets the requirements would get a certification that read “Ph.D. Electrical Engineering (Statistics).” The course requirements are:
Required Core Applied Mathematics and Statistics courses:
203 Introduction to Probability Theory
206 Classical Bayesian Inference (or 206B Intermediate Bayesian Inference)
207 Intermediate Bayesian Statistical Modeling
256 Linear Statistical Models
and one other statistics course from a list of approved courses in AMS (currently 202, 205B, 221, 223, 225, 241, 245, 261, 263, 274, and 291).
Revised: 09/01/13