STAT 430

Spring 2023 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 2023
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
63950	Lecture-Discussion	DEG	11:00AM -12:20PM	TR	2078 Natural History Building	Eck, D	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: 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	11:00AM -12:20PM	TR	2078 Natural History Building	Eck, D	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 3 hours Section Title: 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.
69346	Lecture-Discussion	DZG	9:00AM -9:50AM	MWF	2101 Everitt Laboratory	Zhao, S	Part of Term: 1 Date Range: 01/17/23-05/03/23 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 students in the Statistics department. Restricted to Graduate - Urbana-Champaign.
69347	Lecture-Discussion	DZU	9:00AM -9:50AM	MWF	2101 Everitt Laboratory	Zhao, S	Part of Term: 1 Date Range: 01/17/23-05/03/23 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 Statistics or Statistics & Computer Science major(s). Restricted to Undergrad - Urbana-Champaign.
36200	Lecture-Discussion	ESG	4:00PM -4:50PM	MWF	101 Transportation Building	Ellison, T	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Data Science Ethics Section Info: This is an applied course in data science ethics. Students will have the opportunity to enhance their ethical reasoning skills, particularly applied to both small-scale and large-scale decisions made by data scientists as they progress through each phase of the data science pipeline. Students will gain practice identifying the various stakeholders and values affected by each of these decisions, and learn and evaluate new methods and algorithms which seek to address these values and stakeholder interests. Students will apply these methods and algorithms to datasets in Python. Background in Python is not required, but you should have experience in some programming language. Prerequisites: STAT 410 and either MATH 415 or MATH 257. 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.
36199	Lecture-Discussion	ESU	4:00PM -4:50PM	MWF	101 Transportation Building	Ellison, T	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 3 hours Section Title: Data Science Ethics Section Info: This is an applied course in data science ethics. Students will have the opportunity to enhance their ethical reasoning skills, particularly applied to both small-scale and large-scale decisions made by data scientists as they progress through each phase of the data science pipeline. Students will gain practice identifying the various stakeholders and values affected by each of these decisions, and learn and evaluate new methods and algorithms which seek to address these values and stakeholder interests. Students will apply these methods and algorithms to datasets in Python. Background in Python is not required, but you should have experience in some programming language. Prerequisites: STAT 410 and either MATH 415 or MATH 257. 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.
60247	Lecture-Discussion	LBG	11:00AM -11:50AM	MWF	432 Armory	Bravo De Guenni, L	Part of Term: 1 Date Range: 01/17/23-05/03/23 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 statistic 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. Prerequisites: Completion of or concurrent enrollment in 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 Graduate - Urbana-Champaign.
60249	Lecture-Discussion	LBU	11:00AM -11:50AM	MWF	432 Armory	Bravo De Guenni, L	Part of Term: 1 Date Range: 01/17/23-05/03/23 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 statistic 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. Prerequisites: Completion of or concurrent enrollment in 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 Undergrad - Urbana-Champaign.
69375	Lecture-Discussion	PMG	11:00AM -12:20PM	TR	1047 Sidney Lu Mech Engr Bldg	Martinez Vargas, P	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Infectious Diseases Modeling Section Info: This course will introduce basic concepts of infectious disease modeling and epidemiology, with an emphasis on the use of mathematical models. The topics that are going to be covered include infectious disease studies, the transmission dynamics of human pathogens with different transmission routes, modeling approaches to understand disease outbreaks, ways in which mathematical models can be used to understand the mechanisms driving infectious disease dynamics, and the impacts of public health interventions. Restriction(s): Restricted to Graduate - Urbana-Champaign.
69376	Lecture-Discussion	PMU	11:00AM -12:20PM	TR	1047 Sidney Lu Mech Engr Bldg	Martinez Vargas, P	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 3 hours Section Title: Infectious Diseases Modeling Section Info: This course will introduce basic concepts of infectious disease modeling and epidemiology, with an emphasis on the use of mathematical models. The topics that are going to be covered include infectious disease studies, the transmission dynamics of human pathogens with different transmission routes, modeling approaches to understand disease outbreaks, ways in which mathematical models can be used to understand the mechanisms driving infectious disease dynamics, and the impacts of public health interventions. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
72306	Lecture-Discussion	VEG	12:00PM -12:50PM	MWF	106B8 Engineering Hall	Ellison, T	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Unsupervised Learning Section Info: This course surveys clustering and dimensionality reduction algorithms in data science, focusing on methods, evaluation metrics, and interpretation of results. The methodologies enable discovery of and inference about hidden insights contained in high-dimensional unlabeled data. Applications on real and artificial datasets are emphasized using programming languages such as Python. Prerequisites: STAT 410 and either MATH 415 or MATH 257. 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.
72307	Lecture-Discussion	VEU	12:00PM -12:50PM	MWF	106B8 Engineering Hall	Ellison, T	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 3 hours Section Title: Unsupervised Learning Section Info: This course surveys clustering and dimensionality reduction algorithms in data science, focusing on methods, evaluation metrics, and interpretation of results. The methodologies enable discovery of and inference about hidden insights contained in high-dimensional unlabeled data. Applications on real and artificial datasets are emphasized using programming languages such as Python. Prerequisites: STAT 410 and either MATH 415 or MATH 257. 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.