STAT 430

Fall 2021 Part of Term 1

Part of Term 1
Aug 23-Dec 8

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 2021
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
55664	Lecture-Discussion	DZG	9:00AM -9:50AM	MWF	Architecture Building	Zhao, S	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 4 hours Section Title: Science Data Section Info: This course presents theory and methods for how to answer scientific questions using quantitative data. Topics include the philosophy of science and statistics, surveys of exploratory and confirmatory statistical methods, guided collaborative data analysis, and effective written and oral communication. This course can be thought of as a capstone to a typical undergraduate statistics curriculum, but is also beneficial for masters students. 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	Architecture Building	Zhao, S	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 3 hours Section Title: Science Data Section Info: This course presents theory and methods for how to answer scientific questions using quantitative data. Topics include the philosophy of science and statistics, surveys of exploratory and confirmatory statistical methods, guided collaborative data analysis, and effective written and oral communication. This course can be thought of as a capstone to a typical undergraduate statistics curriculum, but is also beneficial for masters students. 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.
66998	Lecture-Discussion	HNG	2:00PM -3:20PM	TR	Burrill Hall	Nguyen, H	Part of Term: 1 Date Range: 08/23/21-12/08/21 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	Lecture-Discussion	HNU	2:00PM -3:20PM	TR	Burrill Hall	Nguyen, H	Part of Term: 1 Date Range: 08/23/21-12/08/21 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.
70884	Lecture-Discussion	JBG	3:30PM -4:50PM	TR	Henry Administration Bldg	Balamuta, J	Part of Term: 1 Date Range: 08/23/21-12/08/21 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. Students should be proficient in programming. Prerequsite: STAT 432 (possibly co-requisite). 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	Lecture-Discussion	JBU	3:30PM -4:50PM	TR	Henry Administration Bldg	Balamuta, J	Part of Term: 1 Date Range: 08/23/21-12/08/21 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. Students should be proficient in programming. Prerequsite: STAT 432 (possibly co-requisite). 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.
71665	Online	OGR	ARRANGED	n.a.	n.a.	Nguyen, H	Part of Term: 1 Date Range: 08/23/21-12/08/21 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 For Statistics course registration information: go.illinois.edu/StatisticsRegistration Restriction(s): Restricted to Graduate - Urbana-Champaign.
71666	Online	OUG	ARRANGED	n.a.	n.a.	Nguyen, H	Part of Term: 1 Date Range: 08/23/21-12/08/21 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.
76242	Lecture-Discussion	VEG	11:00AM -12:20PM	TR	Campus Instructional Facility	Ellison, T	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 4 hours Section Title: Unsupervised Learning Section Info: This will be an applied course in unsupervised learning. This course will survey some of the most commonly used clustering algorithms and dimensionality reduction algorithms currently used by data scientists. Students will apply these algorithms to datasets in Python. 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.
76243	Lecture-Discussion	VEU	11:00AM -12:20PM	TR	Campus Instructional Facility	Ellison, T	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 3 hours Section Title: Unsupervised Learning Section Info: This will be an applied course in unsupervised learning. This course will survey some of the most commonly used clustering algorithms and dimensionality reduction algorithms currently used by data scientists. Students will apply these algorithms to datasets in Python. 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.