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

Please note: SDS does not currently house an undergraduate degree program. CNS Students: To find out who your advisor is, CLICK HERE.

Registration questions regarding SDS courses can be directed at Abby Black (Program Coordinator)–please include 1) your EID, and 2) the course unique number in your inquiry

• Questions about the SDS Undergraduate Certificate in Scientific Computation & Data Sciences (needs SDS 379R) and the SDS Undergraduate Certificate in Applied Statistical Modeling can be directed at Sally Ragsdale.

• Instructional Faculty contact information can be found here.

Helpful SDS Course Registration Info:

 SDS FOCUS - Same Great Courses, New Names
• SDS FOCUS - Fall 2020 Registration
• SDS FOCUS - Why is SDS 302 Closed?
• SDS FOCUS - When Stats isn't Stats (STA 309, EDP 371, etc)

 

 

 

Click on a course to be taken to its description.

 

Statistics & Data Science Courses

SDS 301: Elementary Statistical Methods
SDS 302F: Foundations of Data Analysis
SDS 110T, 210T, 310T, 410T: Topics in Statistics and Computation
SDS 318: Introduction to Statistical and Scientific Computing
SDS 320E: Elements of Statistics
SDS 321: Introduction to Probability and Statistics
SDS 322E: Elements of Data Science
SDS 323: Statistical Learning and Inference
SDS 324E: Elements of Regression Analysis
SDS 325H: Honors Statistics
SDS 150K: Data Analysis Applications
SDS 352: Statistical Methods
SDS 353: Advanced Multivariate Models
SDS 358: Special Topics in Statistics
SDS 378: Introduction to Mathematical Statistics
SDS 379R: Undergraduate Research

 

Scientific Computation


SDS 322: Introduction to Scientific Programming
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


Course Descriptions

SDS 301:  Elementary Statistical Methods

Covers the fundamental procedures for data organization and analysis. Subjects include frequency distributions, graphical presentation, sampling, experimental design, inference, and regression. Three lecture hours a week for one semester. Only one of the following may be counted: Educational Psychology 371, Mathematics 316, Statistics 309 or Statistics and Data Sciences 301. A student may not earn credit for Educational Psychology 371, Mathematics 316, Statistics 309 or Statistics and Data Sciences 301 after having received credit for any of the following with a grade of at least C-: Statistics and Data Sciences 302, 303, 304, 305, 306, 328M, Statistics and Scientific Computation 302, 303, 304, 305, 306, or 328M.

SDS 302F: Foundations of Data Analysis

Introduction to data analysis and statistical methods. Topics may include: random sampling; principles of observational study and experimental design; data summaries and graphics; and statistical models and inference, including the simple linear regression model and one-way analysis of variance. Only one of the following may be counted: Statistics and Data Sciences 302, 306, 302F. Flags: Ethics, Quantitative Reasoning. Core: Math.

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

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 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 320E: Elements of Statistics

Introduction to statistics. Topics may include: probability; principles of observational study and experimental design; statistical models and inference, including the multiple linear regression model and one-way analysis of variance. R programming is introduced. Only one of the following may be counted: Statistics and Data Sciences 320E and 328M. Flags: Ethics, Quantitative Reasoning, Independent Inquiry. Core: Math.

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 322E: Elements of Data Science

Introduction to data science. Topics may include: data wrangling; exploratory data analysis, including data visualization; markdown and data workflow; simulation-based inference; and classification methods. R programming is emphasized and Python programming is introduced. Prerequisite: Credit for an introductory statistics course. Only one of the following may be counted: Statistics and Data Sciences 322E and 348.

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 324E: Elements of Regression Analysis

A follow-up to an introductory statistics course, with an emphasis on the use of regression analysis in applied research. Topics may include: multiple linear regression; ANOVA; logistic regression; random and mixed effects models; and models for dependent data. Emphasis is placed on identification of appropriate statistical methods and interpretation of software output. R programming is introduced. Prerequisite: SDS 302F or SDS 320E (or SDS 302, 304, 306, 328M). Only one of the following may be counted: Statistics and Data Sciences 324E or 332. Flag: Quantitative Reasoning.

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 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 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.

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 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.