Teaching and Learning Statistics and Data Science

Units: 3

Methods for reading, manipulating, and combining data sources including databases. Custom functions, visualizations, and summaries. Common analyses done by data scientists. Methods for communicating results including dashboards. Regular access to a computer for homework and class exercises is required.

Offered in Fall and Summer

Units: 3

Basic concepts of statistical models and use of samples; variation, statistical measures, distributions, tests of significance, analysis of variance and elementary experimental design, regression and correlation, chi-square.

Offered in Fall Spring Summer

Units: 3

This course introduces important ideas about collecting high quality data and summarizing that data appropriately both numerically and graphically. We explore the use of probability distributions to model data and find probabilities. Estimation of parameters and properties of estimators are discussed. Construction and interpretation of commonly used confidence intervals and hypothesis tests are investigated. Students will gain considerable experience working with data. Software is used throughout the course with the expectation of students being able to produce their own analyses.

Offered in Fall and Spring

Units: 3

Course covers basic methods for summarizing and describing data, accounting for variability in data, and techniques for inference. Topics include basic exploratory data analysis, probability distributions, confidence intervals, hypothesis testing, and regression analysis. This is a calculus-based course. Statistical software is used; however, there is no lab associated with the course. Credit not given for this course and ST 511 or ST 513 or ST 515. This course does NOT count as an elective towards a degree or a minor in Statistics. Note: the course will be offered in person [Fall] and online [Fall and Summer].

Offered in Fall and Summer

Units: 3

Covariance, multiple regression, curvilinear regression, concepts of experimental design, factorial experiments, confounded factorials, individual degrees of freedom and split-plot experiments. Computing laboratory addressing computational issues and use of statistical software.

Offered in Fall Spring Summer

Units: 3

This course provides an in-depth study of building, validating, and predicting using regression models. Topics include multiple linear regression models with both continuous and categorical predictors, model selection techniques, and residual diagnostics. Bayesian regression models are also explored. Categorical data analysis is covered including contingency table analysis and logistic regression models. Students will gain considerable experience working with data. Software is used throughout the course with the expectation of students being able to produce their own analyses.

Offered in Spring and Summer

Units: 3

This second course in statistics for graduate students is intended to further expand students' background in the statistical methods that will assist them in the analysis of data. Course covers many fundamental analysis methods currently used to analyze a wide array of data, mostly arising from designed experiments. Topics include multiple regression models, factorial effects models, general linear models, mixed effect models, logistic regression analysis, and basic repeated measures analysis. This is a calculus-based course. Statistical software is used, however, there is no lab associated with the course. Credit not given for this course and ST 512 or ST 514 or ST 516. Note: this course will be offered in person [Spring] and online [Fall and Spring].

Offered in Fall and Spring

Units: 3

This course focuses on interactions between students and teachers in the mathematics classroom. Topics studied will include: whole class instruction, small group activity, questioning and facilitating classroom discussion. This course will include a field experience in the schools for which students will be required to provide their own transportation. Course restricted to graduate students in the MED, MS or MAT programs.

Offered in Spring Only

YEAR: Offered Alternate Odd Years

Units: 3

This course is designed to bridge theory and practice on how students develop understandings of key concepts in data analysis, statistics, and probability. Discussion of students' understandings, teaching strategies and the use of manipulatives and technology tools. Topics include distribution, measures of center and spread, sampling, sampling distribution, randomness, and law of large numbers.

Offered in Spring Only

YEAR: Offered Alternate Even Years

Units: 3

Provide educators with an in-depth introduction to applying technology in teaching with data. Students will explore a variety of technological tools for analyzing and visualizing data, including the role of programming in that process. Students will learn pedagogy to help them structure learning activities using a variety of technologies. Students will learn to identify key design elements in technologies and data visualizations that support pedagogical goals.

Offered in Fall Only