| source UC Santa Cruz (X) |
level |
department Applied Mathematics and Statistics (X) |
Thesis research under faculty supervision. Students submit petition to sponsoring agency. Enrollment restricted to graduate students.
Score: 10.917559 Details | Listing | Web page
Reviews and introduces mathematical methods useful in the elementary study of statistics, including logic, real numbers, inequalities, linear and quadratic equations, functions, graphs, exponential and logarithmic functions, and summation notation. Prerequisite(s): Mathematics 2 or placement exam score of 20 or higher. (General Education Code(s): Q.)
Score: 10.917559 Details | Listing | Web page
Includes real numbers, inequalities, linear and quadratic equations, functions, inverse graphs, exponential and logarithmic functions, trigonometry, and analytic geometry, and their use in real-world problems. Students cannot receive credit for both this course and Mathematics 3. Mathematics 3 can substitute for course 3. Prerequisite(s): score of 20 or higher on Mathematics Placement Exam or Mathematics 2. (General Education Code(s): Q.)
Score: 10.917559 Details | Listing | Web page
Introduction to statistical methods/reasoning, including descriptive methods, data-gathering (experimental design and sample surveys), probability, interval estimation, significance tests, one- and two-sample problems, categorical data analysis, correlation and regression. Emphasis on applications to the natural and social sciences. Students cannot receive credit for this course if they have already received credit for course 7. (General Education Code(s): IN, Q.)
Score: 10.917559 Details | Listing | Web page
Case-study-based introduction to statistical methods as practiced in the biological, environmental, and health sciences. Descriptive methods, experimental design, probability, interval estimation, hypothesis testing, one- and two-sample problems, power and sample size calculations, simple correlation and simple linear regression, one-way analysis of variance, categorical data analysis. (Formerly
Score: 10.917559 Details | Listing | Web page
Computer-based laboratory course in which students gain hands-on experience in analysis of data sets arising from statistical problem-solving in the biological, environmental, and health sciences. Descriptive methods, interval estimation, hypothesis testing, one-and two-sample problems, correlation and regression, one-way analysis of variance, categorical data analysis. (Formerly
Score: 10.917559 Details | Listing | Web page
Applications-oriented course on complex numbers and linear algebra integrating MATLAB as a computational support tool. Introduction to complex algebra. Vectors, basis and transformations. Matrix algebra. Solutions of linear systems, inverse and determinants. Eigenvalues and eigenvectors. Geometric transformations. Students cannot receive credit for this course and for courses 10A or 27L or Mathematics 21. (Formerly course 27,
Score: 10.917559 Details | Listing | Web page
Applications-oriented course on complex numbers and linear algebra integrating MATLAB as a computational support tool. Introduction to complex algebra. Vectors, basis and transformations. Matrix algebra. Solutions of linear systems, inverse and determinants. Students cannot receive credit for this course and courses 10 or 27L or Mathematics 21. Prerequisite(s): Score of 40 or higher on mathematics placement exam, or course 3, or Mathematics 3.
Score: 10.917559 Details | Listing | Web page
Mathematical tools and reasoning, with applications to Economics 1. Topics are drawn from differential calculus and include limits, continuity, techniques of differentiation, differentials, relative and absolute extrema, and applied optimization. (Also offered as Economics 11A. Students cannot receive credit for both courses.) Students who have already taken Mathematics 11A and 19A should not take this course. Prerequisite(s): score of 31 or higher on Math Placement Exam. Students who do not place into precalculus should enroll in Mathematics 2. (General Education Code(s): IN, Q.)
Score: 10.917559 Details | Listing | Web page
Mathematical tools and reasoning, with applications to Economics 2. Topics are drawn from integral calculus and multivariable calculus, including indefinite and definite integrals, separate differential equations, partial derivatives, total differentials, optimization in several variables, and Lagrange multiplier. (Also offered as Economics 11B. Students cannot receive credit for both courses.) Prerequisite(s): course 11A , Economics 11A, Mathemathics 11A, or Mathematics 19A. (General Education Code(s): IN, Q.)
Score: 10.917559 Details | Listing | Web page
Applications-oriented class on ordinary differential equations (ODEs) and systems of ODEs using MATLAB as a computational support tool. Linear ODEs and systems of linear ODEs. Nonlinear ODEs using substitution and Laplace transforms. Phase-plane analysis. Introduction to numerical methods. Students cannot receive credit for this course and for courses 20A or 27L or Mathematics 24. Prerequisite(s): Mathematics 19B, and course 10 or 10A or Mathematics 21.
Score: 10.917559 Details | Listing | Web page
Applications-oriented class on ordinary differential equations (ODEs) and systems of ODEs integrating MATLAB as a computational support tool. Linear ODEs and systems of linear ODEs. Nonlinear ODEs using substitution and Laplace transforms. Students cannot receive credit this course and for courses 20 or 27L or Mathematics 24. Prerequisite(s): Mathematics 19B, and course 10 or 10A or Mathematics 21.
Score: 10.917559 Details | Listing | Web page
Introduction to MATLAB; elementary programming. Visualization of functions and data. Linear algebra and numerical solutions of differential equations using the supplied MATLAB programs. Previous knowledge of linear algebra and differential equation is expected. (Formerly
Score: 10.917559 Details | Listing | Web page
Games of chance and strategy motivated early developments in probability, statistics, and decision theory. Course uses popular games to introduce students to these concepts, which underpin recent scientific developments in economics, genetics, ecology, and physics. (General Education Code(s): Q.)
Score: 10.917559 Details | Listing | Web page
Fundamental topics in fluid dynamics. Euler and Lagrange descriptions of continuum dynamics. Conservation laws for inviscid and viscous flows. Potential flows. Exact solutions of the Navier-Stokes equation. Boundary layer theory. Gravity waves. Students cannot receive credit for this course and Applied Mathematics and Statistics 217. (Also offered as Physics 107. Students cannot receive credit for both courses.) Prerequisite(s): course 27 or 20 or 20A, or Physics 116A and 116B and 116C, or equivalent.
Score: 10.917559 Details | Listing | Web page
Practical methods for analyzing data relevant to the management sciences, with particular emphasis on information systems management. Reviews basic topics in probability and statistics, including correlation and simple linear regression and multiple regression. Experience using statistical software package. Case studies drawn from business problems. Students cannot receive credit for this course and Economics 113. Prerequisite(s): course 11B or Economics 11B or Mathematics 11B or 19B. (General Education Code(s): Q.)
Score: 10.917559 Details | Listing | Web page
Linear difference equations and the calculus of differences. Nonlinear difference equations and maps. Fixed points, stability, bifurcations, and cycles. The logistic map and the period-doubling cascade to chaos. Strange attractors and measures of chaos. Students cannot receive credit for this course and Mathematics 145. (Formerly course 146.) Prerequisite(s): course 27 or 20 or 20A, or Mathematics 27 or Mathematics 21 and 24.
Score: 10.917559 Details | Listing | Web page
Application of differential equations, probability, and stochastic processes to problems in cell, organismal, and population biology. Topics include life-history theory, behavioral ecology, and population biology. Students may not receive credit for this course and course 215. Prerequisite(s): course 131, a university-level course in biology, and operational knowledge of a programming language; or consent of instructor.
Score: 10.917559 Details | Listing | Web page
Introduction to probability theory and its applications. Combinatorial analysis, axioms of probability and independence, random variables (discrete and continuous), joint probability distributions, properties of expectation, Central Limit Theorem, Law of Large Numbers, Markov chains. Students cannot receive credit for this course and Computer Engineering 107. Prerequisite(s): course 11B or Economics 11B or Mathematics 11B or 19B. (General Education Code(s): Q.)
Score: 10.917559 Details | Listing | Web page
Introduction to statistical inference at a calculus-based level: maximum likelihood estimation, sufficient statistics, distributions of estimators, confidence intervals, hypothesis testing, and Bayesian inference. Prerequisite(s): course 131 or Computer Engineering 107.
Score: 10.917559 Details | Listing | Web page
Applications of computational methods to solving mathematical problems using MATLAB. Solution of nonlinear equations, linear systems, differential equations, sparse matrix solver, and eigenvalue problems. Prerequisite(s): course 27 or 10 or 10A, or Mathematics 21. Knowledge of differential equations is recommended (course 20 or 20A, or Mathematics 24).
Score: 10.917559 Details | Listing | Web page
Methods for the design and analysis of computer simulation experiments: random number generation; estimation of sample size necessary to achieve desired precision goals; antithetic variables and other devices for increasing simulation efficiency; analysis of the output of large "deterministic" computer programs, exploring the sensitivity of outputs to changes in the inputs. Applications drawn mainly from engineering and environmental sciences. Prerequisite(s): course 5 or 7 or 113 or 131 or Computer Engineering 107 or permission of instructor. (General Education Code(s): Q.)
Score: 10.917559 Details | Listing | Web page
Students submit petition to sponsoring agency. May be repeated for credit.
Score: 10.917559 Details | Listing | Web page
Students submit petition to sponsoring agency. May be repeated for credit.
Score: 10.917559 Details | Listing | Web page
Case study-based course teaches statistical linear modeling using the SAS software package. Teaches generalized linear models; linear regression; analysis of variance/covariance; analysis of data with random effects and repeated measures. Prerequisite(s): course 156 or 256, or permission of instructor. Enrollment restricted to graduate students.
Score: 10.917559 Details | Listing | Web page