Undergraduate level courses are offered throughout the year under course code SDS.

Click on a course to be taken to its description.

SDS 302. Data Analysis for the Health Sciences

SDS 303. Statistics in Experimental Research

SDS 304. Statistics in Health Care

SDS 305. Statistics in Policy Design

SDS 306. Statistics in Market Analysis

SDS 110T, 210T, 310T, 410T. Topics in Statistics and Computation

SDS 328M. Biostatistics

SDS 321. Introduction to Probability and Statistics

SDS 150K. Data Analysis Applications

SDS 352. Statistical Methods

SDS 358. Special Topics in Statistics

SDS 318. Introduction to Statistical and Scientific Computing

SDS 322. Introduction to Scientific Programming

SDS 325H. Honors Statistics

SDS 329C. Practical Linear Algebra I

SDS 329D. Practical Linear Algebra II

SDS 335. Scientific/Technical Computing

SDS 339. Applied Computational Science

SDS 358. Statistics Learning and Data Mining

SDS 374C. Parallel Computing for Scientists and Engineers

SDS 374D. Distributed and Grid Computing for Scientists and Engineers

SDS 374E. Visualization and Data Analysis for Scientists and Engineers

SDS 375. Special Topics in Scientific Computation

SDS 379R. Undergraduate Research

Course Descriptions

Basic probability and data analysis for the sciences. Subjects include randomness, sampling, distributions, probability models, inference, regression, and nonlinear curve fitting. Three lecture hours and one discussion hour a week for one semester. May not be counted by students with credit for Educational Psychology 371, Mathematics 316, Statistics and Scientific Computation 303, 304, 305, or 306. Prerequisite: An appropriate score of 30 on the ALEKS placement examination.

An introduction to the fundamental concepts and methods of statistics emphasizing applications in experimental science. Exploratory data analysis, correlation and regression, descriptive statistics, sampling distributions, confidence intervals and hypothesis testing. Students may receive credit for only one of the following: Statistics and Scientific Computation 303, 304, 305, 306 or Mathematics 316. Prerequisite: An appropriate score of 30 on the ALEKS placement examination.

An introduction to the fundamental concepts and methods of statistics emphasizing applications in the health sciences. Exploratory data analysis, correlation and regression, descriptive statistics, sampling distributions, confidence intervals and hypothesis testing. Students may receive credit for only one of the following: Statistics and Scientific Computation 303, 304, 305, 306 or Mathematics 316. Prerequisite: An appropriate score or 30 on the ALEKS placement examination.

An introduction to the fundamental concepts and methods of statistics emphasizing applications in policy evaluation and design. Exploratory data analysis, correlation and regression, descriptive statistics, sampling distributions, confidence intervals and hypothesis testing. Students may receive credit for only one of the following: Statistics and Scientific Computation 303, 304, 305, 306 or Mathematics 316. Prerequisite: An appropriate score of 30 on the ALEKS placement examination.

An introduction to the fundamental concepts and methods of statistics emphasizing applications in the analysis of individual and group behaviors. Exploratory data analysis, correlation and regression, descriptive statistics, sampling distributions, confidence intervals and hypothesis testing. Students may receive credit for only one of the following: Statistics and Scientific Computation 303, 304, 305, 306 or Mathematics 316. Prerequisite: An appropriate score of 30 on the ALEKS placement examination.

Topics in Statistics and Computation. For each credit hour, one hour per week for one semester. May be repeated for credit when the topic varies.

Basic theory of probability and statistics with practical applications. Includes fundamentals of probability, distribution theory, sampling models, 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. Prerequisite: Mathematics 408D or 408L with a grade of at least C. Students may receive credit for only one of the following: Statistics and Scientific Computation 321 or 323, or Mathematics 358K.

Basic theory of probability and statistics with practical applications with biological data. Includes fundamentals of probability, distribution theory, sampling models, 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. Prerequisite: A passing score on the College of Natural Sciences mathematics placement examination, and six semester hours of coursework in biology. Students may receive credit for only one of the following: Statistics and Scientific Computation 321 or 323, or Biology 318M, or Mathematics 358K.

Introduction to the use of statistical or mathematical applications for data analysis. Two hours per week for eight weeks. May be repeated for credit when the topics vary. Offered on the credit/no credit basis only. Prerequisites vary with the topic and are given in the Course Schedule.
Topic 1: SPSS
Topic 2: SAS
Topic 3: STATA
Topic 4: Selected Topics

Covers simple and multiple regression, fundamentals of experimental design, and analysis of variance methods. Other topics will be selected from the following: 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: Statistics and Scientific Computation 303, 304, 305, 306, or Mathematics 316.

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.

An introduction to quantitative analysis using fundamental concepts in statistics and scientific computation. Probability, distributions, sampling, interpolation, iteration, recursion and visualization. Three lecture hours and one laboratory hour a week for one semester.

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. Prerequisite: Credit or registration for Mathematics 408K or 408C.

Matrix representations and properties of matrices; linear equations, eigenvalue problems and their physical interpretation; linear least squares and elementary numerical analysis. Emphasis will be placed on physical interpretation, practical numerical algorithms and proofs of fundamental principles. Prerequisite: Credit or registration for Mathematics 408K or 408C.

Iterative solution to linear equations and eigenvalue problems; properties of symmetric and asymmetric matrices, exploitation of parsity and diagonal dominance; introduction to multivariate nonlinear equations; numerical analysis; selected applications and topics in the physical sciences. Prerequisite: Statistics and Scientific Computation 329C, or Mathematics 340L or 341.

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.

Concentrated study in a specific area or areas of application. Areas may include computational biology, computational chemistry, computational applied mathematics, computational economics, computational physics, or computational geology. Prerequisite: Mathematics 408D or 408M, and Statistics and Scientific Computation 335 and 329D or the equivalent.

Parallel computing principles, architectures, and technologies. Parallel application development, performance, and scalability. Prepares 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 Unix/Linux systems.

Distributed and grid computing principles and technologies. Covers common modes of grid computing for scientific applications, developing grid enabled applications, 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 Unix/Linux systems.

Scientific visualization principles, practices and technologies, including remote and collaborative visualization. Also 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 Unix/Linux systems.

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.

Individual research project under the supervision of one or more faculty members. The equivalent of three lecture hours a week for one semester. May be repeated for credit. Prerequisite: Upper-division standing, and consent of instructor.