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

 

Starting Fall 2014 Undergraduate level courses are offered under the course code SDS.

 

Click on a course to be taken to its description.

SDS Statistics Courses

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 Scientific Computation Courses

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

SDS 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. 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.

SDS 303. Statistics in Experimental Research.

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.

SDS 304. Statistics in Health Care.

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.

SDS 305. Statistics in Policy Design.

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.

SDS 306. Statistics in Market Analysis.

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.

SDS 110T, 210T, 310T, 410T.

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.

SDS 321. Introduction to Probability and Statistics.

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.

 

SDS 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. Prerequisite: Admission to the Dean's Scholars Honors Program in the College of Natural Sciences, or consent of instructor.

 

SDS 328M. Biostatistics.

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.

SDS 150K. Data Analysis Applications.

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

SDS 352. Statistical Methods.

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.

SDS 358. Special Topics in Statistics.

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.

SDS 318. Introduction to Statistical and DSientific Computing.

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.



SDS 322. Introduction to DSientific 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. Prerequisite: Credit or registration for Mathematics 408K or 408C.



SDS 329C. Practical Linear Algebra I.

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.



SDS 329D. Practical Linear Algebra II.

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.



SDS 335. DSientific/Technical Computing.

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.

SDS 339. Applied Computational DSience.

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.

 

SDS 358 Statistics Learning and Data Mining

Introduction to the topic of data mining: data preprocessing regression, classification, clustering, dimensionality reduction, association analysis and anomaly detection. Computer Science 363D and 378 (Topic: Introduction to Data Mining) may not both be counted. Prerequisite: Upper-division standing. Additional prerequisites may vary with the topic and are given in the Course Schedule.

 



SDS 374C. Parallel Computing for DSientists and Engineers.

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.

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

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.



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

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. 



SDS 375. Special Topics in DSientific Computation.

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.



SDS 379R. Undergraduate Research.

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.