STAT 430

Spring 2021 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 with approval. Prerequisite: STAT 410 or STAT 420; or consent of instructor.

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STAT 430 class schedule data for spring 2021
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
36200	Online	DZG	12:30PM -1:50PM	TR	n.a.	Zhao, S	Part of Term: 1 Date Range: 01/25/21-05/05/21 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.
36199	Online	DZU	12:30PM -1:50PM	TR	n.a.	Zhao, S	Part of Term: 1 Date Range: 01/25/21-05/05/21 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.
60247	Online	HNG	ARRANGED	n.a.	n.a.	Nguyen, H	Part of Term: 1 Date Range: 01/25/21-05/05/21 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.
60249	Online	HNU	ARRANGED	n.a.	n.a.	Nguyen, H	Part of Term: 1 Date Range: 01/25/21-05/05/21 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.
69110	Online	JJB	ARRANGED	n.a.	n.a.	Balamuta, J	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Fundamentals of Deep Learning Section Info: Topic: Fundamentals of Deep Learning 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. Pre-Requsite: STAT 432 (possibly co-requisite) Restriction(s): Restricted to Undergrad - Urbana-Champaign.
69375	Online	VEG	6:30PM -7:50PM	TR	n.a.	Ellison, T	Part of Term: 1 Date Range: 01/25/21-05/05/21 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.
69376	Online	VEU	6:30PM -7:50PM	TR	n.a.	Ellison, T	Part of Term: 1 Date Range: 01/25/21-05/05/21 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.