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 332. Statistical Modeling For Health/Behavioral Science
SDS 321. Introduction to Probability and Statistics
SDS 323. Statistical Learning and Inference
SDS 150K. Data Analysis Applications
SDS 352. Statistical Methods
SDS 353. Advanced Multivariate Models
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 348. Computational Biology and Bioinformatics
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 378. Introduction to Mathematical Statistics
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 Data Sciences 303, 304, 305, or 306. 

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 Data Sciences 303, 304, 305, 306 or Mathematics 316. 

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 Data Sciences 303, 304, 305, 306 or Mathematics 316. 

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 Data Sciences 303, 304, 305, 306 or Mathematics 316. 

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 Data Sciences 303, 304, 305, 306 or Mathematics 316. 

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.

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 lectures hours a week. Only one of the following may be counted: Mathematics 362K, Statistics and Data Sciences 321, Statistics and Scientific Computation 321. Prerequisite: The following with a grade of at least C-: Mathematics 408C, 408L, 408S or 408R.

SDS 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. Statistics and Data Sciences 323 and Statistics and Scientific Computation 323 may not both be counted. Prerequisite: Statistics and Data Sciences 321 (or Statistical and Scientific Computation 321) or the equivalent.

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 Data Sciences 321 or 323, or Biology 318M, or Mathematics 358K.

SDS 332. STATISTICAL MODELING FOR HEALTH/BEHAVIORAL SCIENCE.

Follow up to introductory statistics with an overview of advanced statistical modeling topic. Subjects may include multiple regression, ANOVA, logistic regression, random and mixed effects models including longitudinal data, time series analysis, survival analysis, factor analysis, and SEM. Use of statistical software is emphasized. Prerequisite: Statistics and Data Sciences 302, 304, 306 , 328M or the equivalent.

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 Data Sciences 303, 304, 305, 306, or Mathematics 316.

SDS 353. ADVANCED MULTIVARIATE MODELS.

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. Statistics and Data Sciences 353 and Statistics and Scientific Computation 353 may not both be counted. Prerequisite: Mathematics 408D or 408M; and Statistics and Data Sciences 325H or 352.

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 SCientific 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 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. 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 Data Sciences 329C, or Mathematics 340L or 341.



SDS 335. Scientific/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 SCience.

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 Data Sciences 335 and 329D or the equivalent.

SDS 348. Computational Biology and Bioinformatics.

Computational-based data sorting, data transformation, and data analysis; programming in Python and R. Three lecture hours and one laboratory hour per week. Prerequisite: Statistics and Data Sciences 328M (or Statistics and Scientific Computation 328M) with a grade of at least C-.

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 SCientists 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 SCientists 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 SCientists 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 SCientific 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 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. Only one the following may be counted: Mathematics 378K, Statistics and Data Sciences 378, Statistics and Scientific Computation 378. Prerequisite: Mathematics 362K with a grade of at least C-.

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.