STAT 430

Fall 2022 Part of Term 1

Part of Term 1
Aug 22-Dec 7

Topics in Applied Statistics

SHOW FINAL EXAM SCHEDULE

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 2022
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
77737	Lecture-Discussion	DEU	11:00AM -1:50PM	W	Henry Administration Bldg	Eck, D	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: Baseball Analytics Section Info: This is a reading, seminar, and project based course on the intersection of baseball and statistics. In this course you will learn how to conduct relevant data analyses with a focus on how to quantify 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 as well as advanced statistical methods for both directions will be discussed. The analyses that you conduct will also develop your critical thinking skills as a statistician. 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 385 and STAT 410 Restriction(s): Restricted to Undergrad - Urbana-Champaign.
55664	Lecture-Discussion	DZG	9:00AM -9:50AM	MWF	Campus Instructional Facility	Zhao, S	Part of Term: 1 Date Range: 08/22/22-12/07/22 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.
55666	Lecture-Discussion	DZU	9:00AM -9:50AM	MWF	Campus Instructional Facility	Zhao, S	Part of Term: 1 Date Range: 08/22/22-12/07/22 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.
70884	Online	JBG	ARRANGED	n.a.	n.a.	Balamuta, J	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Fundamentals of Deep Learning Section Info: Deep Learning methods are rapidly becoming ingrained within everyday life. These methods strive to reveal patterns within the data. This course provides a foundation for developing and applying deep learning models through a study of its theory and application using a leading modeling framework. This course will primarily use Python. Students should understand key programming tenets like: loops, if-else statements, functions, and so on. This course will utilize a blended learning environment that necessitates a more hands-on lab aspect with short video segments introducing the idea. Prerequisite: Completion or concurrent enrollment of STAT 432. Programming skills and linear algebra are incredibly helpful. 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.
70885	Online	JBU	ARRANGED	n.a.	n.a.	Balamuta, J	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: Fundamentals of Deep Learning Section Info: Deep Learning methods are rapidly becoming ingrained within everyday life. These methods strive to reveal patterns within the data. This course provides a foundation for developing and applying deep learning models through a study of its theory and application using a leading modeling framework. This course will primarily use Python. Students should understand key programming tenets like: loops, if-else statements, functions, and so on. This course will utilize a blended learning environment that necessitates a more hands-on lab aspect with short video segments introducing the idea. Prerequisite: Completion or concurrent enrollment of STAT 432. Programming skills and linear algebra are incredibly helpful. 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	PYG	ARRANGED	n.a.	n.a.	Balamuta, J	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Python in Data Science Program Section Info: This course provides the foundation for programming and conducting statistical analysis in Python. In the first part of the course, we focus on introducing the fundamental elements of Python assuming students have little to no experience in programming. Next, we will get used to Git and Docker. Finally, we will dive into how to conduct real-world statistical analysis in Python using applications in social network analysis, machine learning, etc. as examples. 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.
46976	Online	PYU	ARRANGED	n.a.	n.a.	Balamuta, J	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: Python in Data Science Program Section Info: This course provides the foundation for programming and conducting statistical analysis in Python. In the first part of the course, we focus on introducing the fundamental elements of Python assuming students have little to no experience in programming. Next, we will get used to Git and Docker. Finally, we will dive into how to conduct real-world statistical analysis in Python using applications in social network analysis, machine learning, etc. as examples. 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.