STAT 430

Spring 2025 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 spring 2025
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
63950	Lecture-Discussion	DEG	12:30PM -1:50PM	TR	2039 Campus Instructional Facility	Eck, D	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Topic: Baseball Analytics Section Info: This is a reading, seminar, and project based course on the intersection of baseball, statistics, and data science. In this course you will learn how to conduct relevant data analyses with a focus on how to quantify and visualize aspects of baseball play associated with winning games. You will also learn about the statistical history of baseball with an emphasis on comparing players across eras. Founding principles, intensive data analysis, and advanced statistical methods will be discussed for both directions. The analyses that you conduct will also develop your coding ability and critical thinking skills as a statistician and data scientist. Furthermore, practical advantages, limitations, and comparisons of methods will be discussed. If you are interested in quantifying how good Mike Trout is or in ranking the careers of Barry Bonds, Willie Mays, and Babe Ruth, then this is the course for you. Prerequisites: STAT 425; STAT 410; STAT 385 or similar programming and regression modeling experience. 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.
69110	Lecture-Discussion	DEU	12:30PM -1:50PM	TR	2039 Campus Instructional Facility	Eck, D	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 3 hours Section Title: Topics: Baseball Analytics Section Info: This is a reading, seminar, and project based course on the intersection of baseball, statistics, and data science. In this course you will learn how to conduct relevant data analyses with a focus on how to quantify and visualize aspects of baseball play associated with winning games. You will also learn about the statistical history of baseball with an emphasis on comparing players across eras. Founding principles, intensive data analysis, and advanced statistical methods will be discussed for both directions. The analyses that you conduct will also develop your coding ability and critical thinking skills as a statistician and data scientist. Furthermore, practical advantages, limitations, and comparisons of methods will be discussed. If you are interested in quantifying how good Mike Trout is or in ranking the careers of Barry Bonds, Willie Mays, and Babe Ruth, then this is the course for you. Prerequisites: STAT 425; STAT 410; STAT 385 or similar programming and regression modeling experience. 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.
69375	Lecture-Discussion	HLG	12:00PM -12:50PM	MWF	4039 Campus Instructional Facility	Lee, H	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Machine Learning w/ TimeSeries Section Info: This is a reading and project-based course. In this course students will learn how to conduct machine learning methods on time series data. Students will get to know main algorithms of machine learning methods related to time series analysis. Students will learn how to use python to design, test, and implement ML algorithms to time series data. Students will focus on (but not restricted to) financial time series data. Prerequisite: STAT 410 and 429. Restriction(s): Restricted to Graduate - Urbana-Champaign.
69376	Lecture-Discussion	HLU	12:00PM -12:50PM	MWF	4039 Campus Instructional Facility	Lee, H	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 3 hours Section Title: Machine Learning w/ TimeSeries Section Info: This is a reading and project-based course. In this course students will learn how to conduct machine learning methods on time series data. Students will get to know main algorithms of machine learning methods related to time series analysis. Students will learn how to use python to design, test, and implement ML algorithms to time series data. Students will focus on (but not restricted to) financial time series data. Prerequisite: STAT 410 and 429. Restriction(s): Restricted to Undergrad - Urbana-Champaign.