STAT 430

Fall 2024 All Classes

All Classes

Topics in Applied Statistics

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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 2024
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
55664	Lecture-Discussion	DZG	9:00AM -9:50AM	MWF	259 English Building	Zhao, S	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Practice of Applied Statistics Section Info: It can be difficult to recognize how to properly apply statistical methods to answer complex research questions found "in the wild". This process can be very different from answering the relatively well-defined and straightforward questions encountered when first learning statistical methods. This course teaches students how to formulate complex research questions into precise statistical ones, and how to choose, learn, and implement appropriate statistical procedures for answering those questions. Topics in this course include the core framework of statistics, surveys of statistical methods, guided data analysis, and effective written and oral communication. The idea that motivates this course is that the methods of statistics are constantly changing, and the ones that will likely dominate the landscape a decade from now likely haven't been invented yet. Therefore, this course tries to teach the principles of data analysis rather than the mathematics of specific methods, which can make it easier to adapt to a fast-moving field. This course can be thought of as a capstone to a typical undergraduate statistics curriculum, but may also be beneficial for masters students. Pre-req: STAT 425. STAT 410 preferred, but not required. For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration Restriction(s): Restricted to Graduate - Urbana-Champaign.
55666	Lecture-Discussion	DZU	9:00AM -9:50AM	MWF	259 English Building	Zhao, S	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 3 hours Section Title: Practice of Applied Statistics Section Info: It can be difficult to recognize how to properly apply statistical methods to answer complex research questions found "in the wild". This process can be very different from answering the relatively well-defined and straightforward questions encountered when first learning statistical methods. This course teaches students how to formulate complex research questions into precise statistical ones, and how to choose, learn, and implement appropriate statistical procedures for answering those questions. Topics in this course include the core framework of statistics, surveys of statistical methods, guided data analysis, and effective written and oral communication. The idea that motivates this course is that the methods of statistics are constantly changing, and the ones that will likely dominate the landscape a decade from now likely haven't been invented yet. Therefore, this course tries to teach the principles of data analysis rather than the mathematics of specific methods, which can make it easier to adapt to a fast-moving field. This course can be thought of as a capstone to a typical undergraduate statistics curriculum, but may also be beneficial for masters students. Pre-req: STAT 425. STAT 410 preferred, but not required. For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration Restriction(s): Restricted to Undergrad - Urbana-Champaign.
66998	Online	OPG	11:00AM -12:20PM	TR	n.a.	Ellison, T	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Math’l Optimization with ML Section Info: Mathematical Optimization with Machine Learning Applications 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. Prerequisites: STAT 410 and MATH 257 Restriction(s): Restricted to Graduate - Urbana-Champaign.
46976	Online	OPU	11:00AM -12:20PM	TR	n.a.	Ellison, T	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 3 hours Section Title: Math’l Optimization with ML Section Info: Mathematical Optimization with Machine Learning Applications 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. Prerequisites: STAT 410 and MATH 257 Restriction(s): Restricted to Undergrad - Urbana-Champaign.