Division of Statistics and Scientific Computation
Statistics and Scientific Computation: SSC
Lower-Division Courses
SSC 302. Data Analysis for the Health Sciences.
Basic probability and data analysis for the sciences. Subjects include randomness, sampling, distributions, probability models, inference, regression, and nonlinear curve fitting. The equivalent of four lecture hours a week. May not be counted by students with credit for Educational Psychology 371, Mathematics 316, Statistics and Scientific Computation 303, 304, 305, or 306. Prerequisite: A passing score on the College of Natural Sciences mathematics placement examination.
SSC 303. Statistics in Experimental Research.
An introduction to the fundamental concepts and methods of statistics, with emphasis on applications in experimental science. Includes exploratory data analysis, correlation and regression, descriptive statistics, sampling distributions, confidence intervals, and hypothesis testing. Three lecture hours a week for one semester. Only one of the following may be counted: Mathematics 316, Statistics and Scientific Computation 303, 304, 305, 306. Prerequisite: A passing score on the College of Natural Sciences mathematics placement examination.
SSC 304. Statistics in Health Care.
An introduction to the fundamental concepts and methods of statistics, with emphasis on applications in the health sciences. Includes exploratory data analysis, correlation and regression, descriptive statistics, sampling distributions, confidence intervals, and hypothesis testing. Three lecture hours a week for one semester. Only one of the following may be counted: Mathematics 316, Statistics and Scientific Computation 303, 304, 305, 306. Prerequisite: A passing score on the College of Natural Sciences mathematics placement examination.
SSC 305. Statistics in Policy Design.
An introduction to the fundamental concepts and methods of statistics, with emphasis on applications in policy evaluation and design. Includes exploratory data analysis, correlation and regression, descriptive statistics, sampling distributions, confidence intervals, and hypothesis testing. Three lecture hours a week for one semester. Only one of the following may be counted: Mathematics 316, Statistics and Scientific Computation 303, 304, 305, 306. Prerequisite: A passing score on the College of Natural Sciences mathematics placement examination.
SSC 306. Statistics in Market Analysis.
An introduction to the fundamental concepts and methods of statistics, with emphasis on applications in the analysis of personal and group behaviors. Includes exploratory data analysis, correlation and regression, descriptive statistics, sampling distributions, confidence intervals, and hypothesis testing. Three lecture hours a week for one semester. Only one of the following may be counted: Mathematics 316, Statistics and Scientific Computation 303, 304, 305, 306. Prerequisite: A passing score on the College of Natural Sciences mathematics placement examination.
SSC 110T, 210T, 310T, 410T. Topics in Statistics and Computation.
For each semester hour of credit earned, one lecture hour a week for one semester. May be repeated for credit when the topics vary.
SSC 318. Introduction to Statistical and Scientific Computation.
An introduction to quantitative analysis using fundamental concepts in statistics and scientific computation. Includes probability, distributions, sampling, interpolation, iteration, recursion, and visualization. Three lecture hours and one laboratory hour a week for one semester.
Upper-Division Courses
SSC 321. Introduction to Probability and Statistics.
Covers fundamentals of probability, combinatorics, discrete and continuous random variables, jointly distributed random variables, and limit theorems. Using probability to introduce fundamentals of statistics, including Bayesian and classical inference. The equivalent of four lecture hours a week. Mathematics 362K and Statistics and Scientific Computation 321 may not both be counted. Prerequisite: Mathematics 408C, 408K, or 408N; and Computer Science 311 or 313K or Mathematics 325K or the equivalent, with a grade of at least C-.
SSC 222, 322. Introduction to Scientific Programming.
Introduction to programming using both the C and Fortran (95/2003) languages, with applications to basic scientific problems. Covers common data types and structures, control structures, algorithms, performance measurement, and interoperability. For each semester hour of credit earned, one lecture hour a week for one semester. Only one of the following may be counted: Statistics and Scientific Computation 222, 322, 292, 392. Prerequisite: Credit or registration for Mathematics 408C, 408K, or 408N.
SSC 323. Statistical Learning and Inference.
An introduction to statistical influence, broadly construed as the process of drawing conclusions from data, and to quantifying uncertainty about said conclusions. Covers the major schools of thought that influence modern scientific practice, including classical frequentist methods, machine learning, and Bayesian inference. Three lecture hours a week for one semester. Prerequisite: Statistics and Scientific Computation 321 or the equivalent.
SSC 325H. Honors Statistics.
An introduction to the fundamental theories, concepts, and methods of statistics. Emphasizes probability models, exploratory data analysis, sampling distributions, confidence intervals, hypothesis testing, correlation and regression, and the use of statistical software. Three lecture hours a week for one semester. Prerequisite: Admission to the Dean's Scholars Honors Program in the College of Natural Sciences, or consent of instructor.
SSC 328M. Biostatistics.
Introduction to methods of statistical analysis of biological data. Includes data analysis, basics of experimental design, statistical inference, interval estimation, and hypothesis testing. Three lecture hours and one discussion hour a week for one semester. Only one of the following may be counted: Biology 318M, Biology 328M, Statistics and Scientific Computation 318M, 328M. Prerequisite: A passing score on the College of Natural Sciences mathematics placement examination, and six semester hours of coursework in biology.
SSC 329C. Practical Linear Algebra I.
Matrix representations and properties of matrices; linear equations, eigenvalue problems and their physical interpretation; and linear least squares and elementary numerical analysis. Emphasis on physical interpretation, practical numerical algorithms, and proofs of fundamental principles. Three lecture hours a week for one semester. Prerequisite: Credit or registration for Mathematics 408C, 408K, or 408N.
SSC 329D. Practical Linear Algebra II.
Iterative solutions to linear equations and eigenvalue problems; properties of symmetric and nonsymmetric matrices, exploitation of parsity and diagonal dominance; introduction to multivariate nonlinear equations; numerical analysis; and selected applications and topics in the physical sciences. Three lecture hours a week for one semester. Prerequisite: Mathematics 340L, 341, or Statistics and Scientific Computation 329C.
SSC 335. Scientific and Technical Computing.
A comprehensive introduction to computing techniques and methods applicable to many scientific disciplines and technical applications. Covers computer hardware and operating systems, systems software and tools, code development, numerical methods and math libraries, and basic visualization and data analysis tools. Three lecture hours a week for one semester. Prerequisite: Mathematics 408D or 408M, and prior programming experience.
SSC 339. Applied Computational Science.
Concentrated study in a specific area or areas of application, with an emphasis on modeling and visualization. Areas may include computational biology, computational chemistry, computational applied mathematics, computational economics, computational physics, or computational geology. Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Varies with the topic and is given in the Course Schedule.
SSC 150K. Data Analysis Applications.
Introduction to the use of statistical or mathematical applications for data analysis. Two lecture hours a week for eight weeks. Offered on the pass/fail basis only. May be repeated for credit when the topics vary. Offered on the pass/fail basis only. Prerequisite: Varies with the topic and is given in the Course Schedule.
Topic 1: SPSS Software. Offered on the pass/fail basis only. Prerequisite: Upper-division standing.
Topic 2: SAS Software. Offered on the pass/fail basis only. Prerequisite: Upper-division standing.
Topic 3: Stata Software. Offered on the pass/fail basis only. Prerequisite: Upper-division standing.
Topic 4: The R Software Environment. Offered on the pass/fail basis only. Prerequisite: Upper-division standing.
SSC 352. Statistical Methods.
Study of simple and multiple regression, fundamentals of experimental design, and analysis of variance methods. May include logistic regression, Poisson regression, resampling methods, introduction to Bayesian methods, and probability models. Includes substantial use of statistical software. Three lecture hours and one laboratory hour a week for one semester. Prerequisite: Mathematics 316, Statistics and Scientific Computation 303, 304, 305, or 306.
SSC 353. Advanced Multivariate Modeling.
Advanced topics in statistical modeling, including models for categorical and count data; spatial and time-series data; and survival, hazard, and hierarchical models. Extensive use of statistical software to build on knowledge of introductory probability and statistics, as well as multiple regression. Three lecture hours a week for one semester Prerequisite: Mathematics 408D; and Statistics and Scientific Computation 325H or 352.
SSC 358. Special Topics in Statistics.
Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Upper-division standing. Additional prerequisites may vary with the topic and are given in the Course Schedule.
SSC 367S. Simulation Modeling.
Basic concepts of discrete-event simulation. Statistical input and output analysis; application of simulation software; modeling of systems under uncertainty. Three lecture hours a week for one semester. Statistics and Scientific Computation 358 (Topic: Simulation Modeling) and 367S may not both be counted. Prerequisite: Upper-division standing; Statistics and Scientific Computation 321 or an equivalent introductory statistics course, with a grade of at least C-; and Mathematics 408C or 408K, with a grade of at least C-.
SSC 374C. Parallel Computing for Science and Engineering.
Study of parallel computing principles, architectures, and technologies; and parallel application development, performance, and scalability. Designed to help prepare students to formulate and develop parallel algorithms to implement effective applications for parallel computing systems. Three lecture hours a week for one semester. Prerequisite: Mathematics 408D or 408M; Mathematics 340L; and prior programming experience using C or Fortran on Linux or Unix systems.
SSC 374D. Distributed and Grid Computing for Science and Engineering.
Distributed and grid computing principles and technologies. Covers common modes of grid computing for scientific applications, development of grid-enabled applications, and future trends in grid computing. Three lecture hours a week for one semester. Prerequisite: Mathematics 408D or 408M; Mathematics 340L; and prior programming experience using C or Fortran on Linux or Unix systems.
SSC 374E. Visualization and Data Analysis for Science and Engineering.
Scientific visualization principles, practices, and technologies, including remote and collaborative visualization. Introduces statistical analysis, data mining, and feature detection. Three lecture hours a week for one semester. Prerequisite: Mathematics 408D or 408M; Mathematics 340L; and prior programming experience using C or Fortran on Linux or Unix systems.
SSC 375. Special Topics in Scientific Computation.
Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Upper-division standing. Additional prerequisites may vary with the topic and are given in the Course Schedule.
SSC 378. Introduction to Mathematical Statistics.
Same as Mathematics 378K. Sampling distributions of statistics, estimation of parameters (confidence intervals, method of moments, maximum likelihood, comparison of estimators using mean square error and efficiency, sufficient statistics), hypothesis tests (p-values, power, likelihood ratio tests), and other topics. Three lecture hours a week for one semester. Prerequisite: Mathematics 362K with a grade of at least C-.
SSC 179R, 279R, 379R, 479R. Undergraduate Research.
Students work on an individual research project under the supervision of one or more faculty members. For each semester hour of credit earned, the equivalent of one lecture hour a week for one semester. May be repeated for credit. Prerequisite: Upper-division standing and consent of instructor.