STAT 430

Fall 2026 All Classes

All Classes

Topics in Applied Statistics

Credit: 3 OR 4 hours.

Formulation and analysis of mathematical models for random phenomena; extensive involvement with the analysis of real data; and instruction in statistical and computing techniques as needed.

3 undergraduate hours. 4 graduate hours. May be repeated in the same or separate terms if topics vary. Prerequisite: STAT 410; STAT 425. Some topics may require additional prerequisites. Read the section text for each topic.

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STAT 430 class schedule data for fall 2026
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
66998	Discussion/ Recitation Online Lecture	1AG 1AG	ARRANGED 11:00AM -12:20PM	n.a. TR	Location Pending n.a.	Ellison, T Ellison, T	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Math’l Optimization for DS Section Info: Topic: Mathematical Optimization for Data Science This course explores the application, theory, techniques, and algorithms that are used to find the optimal solutions to constrained and unconstrained optimization problems. Students will learn to formulate and solve these problems using Python. We will cover the theory and algorithms behind common mathematical programming techniques including linear programming, integer programming, nonlinear programming, and multicriteria optimization. Additionally, we will explore how to enhance traditional statistical and machine learning models by integrating additional objectives and functions, tailored to meet specific problem requirements. Through this course, students will gain practical skills and theoretical insights necessary for applying optimization techniques in data science. Credit is not granted toward graduation for STAT 430: Mathematical Optimization with Machine Learning Applications and STAT 430: Mathematical Optimization for Data Science. Prerequisites: STAT 410 and MATH 257. This offering of Mathematical Optimization for Data Science covers largely the same content as Mathematical Optimization with Machine Learning Applications offered in Fall 2024. Restriction(s): Restricted to students in the Statistics department. Restricted to Graduate - Urbana-Champaign.
46976	Discussion/ Recitation Online Lecture	1AU 1AU	ARRANGED 11:00AM -12:20PM	n.a. TR	Location Pending n.a.	Ellison, T Ellison, T	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Math’l Optimization for DS Section Info: Topic: Mathematical Optimization for Data Science This course explores the application, theory, techniques, and algorithms that are used to find the optimal solutions to constrained and unconstrained optimization problems. Students will learn to formulate and solve these problems using Python. We will cover the theory and algorithms behind common mathematical programming techniques including linear programming, integer programming, nonlinear programming, and multicriteria optimization. Additionally, we will explore how to enhance traditional statistical and machine learning models by integrating additional objectives and functions, tailored to meet specific problem requirements. Through this course, students will gain practical skills and theoretical insights necessary for applying optimization techniques in data science. Credit is not granted toward graduation for STAT 430: Mathematical Optimization with Machine Learning Applications and STAT 430: Mathematical Optimization for Data Science. Prerequisites: STAT 410 and MATH 257. This offering of Mathematical Optimization for Data Science covers largely the same content as Mathematical Optimization with Machine Learning Applications offered in Fall 2024. Restriction(s): Restricted to Statistics or Statistics & Computer Science major(s). Restricted to Undergrad - Urbana-Champaign.
55664	Lecture-Discussion	1BG	9:00AM -9:50AM	MWF	1302 Siebel Center for Comp Sci	Bravo De Guenni, L	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Environmental Statistics Section Info: The purpose of this course is to provide an overview of the most common statistical methods applied in Environmental Sciences including but not restricted to environmental problems such as air quality, water quality and climate change. We will cover descriptive statistics for univariate and multivariate environmental data and the main probability models and statistical inference methods for environmental science applications; models for understanding the relationships between environmental variables including linear models, generalized linear models and non-linear models; model for spatial data including models for the spatial trend estimation and spatial interpolation and prediction; models for temporal data including temporal trend estimation and time series methods in the time domain and frequency domain; issues on data collection and monitoring network design and the statistical analysis of extreme events. All topics would be driven by analyzing real data set examples using R. Prerequisite: STAT 410 For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration Restriction(s): Restricted to students in the Statistics department. Restricted to Graduate - Urbana-Champaign.
55666	Lecture-Discussion	1BU	9:00AM -9:50AM	MWF	1302 Siebel Center for Comp Sci	Bravo De Guenni, L	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Environmental Statistics Section Info: The purpose of this course is to provide an overview of the most common statistical methods applied in Environmental Sciences including but not restricted to environmental problems such as air quality, water quality and climate change. We will cover descriptive statistics for univariate and multivariate environmental data and the main probability models and statistical inference methods for environmental science applications; models for understanding the relationships between environmental variables including linear models, generalized linear models and non-linear models; model for spatial data including models for the spatial trend estimation and spatial interpolation and prediction; models for temporal data including temporal trend estimation and time series methods in the time domain and frequency domain; issues on data collection and monitoring network design and the statistical analysis of extreme events. All topics would be driven by analyzing real data set examples using R. Prerequisite: STAT 410 For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration Restriction(s): Restricted to Statistics or Statistics & Computer Science major(s). Restricted to Undergrad - Urbana-Champaign.