STAT 430

Fall 2020 Part of Term 1

Part of Term 1
Aug 24-Dec 9

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

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STAT 430 class schedule data for fall 2020
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
55664	Online	AG	9:30AM -11:30AM	T	n.a.	He, J	Part of Term: 1 Date Range: 08/24/20-12/09/20 Credit: 4 hours Section Title: Introduction to Data Science Section Info: TOPIC: Introduction to Data Science This course introduces students to data science approaches that have emerged from recent advances in programming and computing technology. They will learn to collect and use data from a variety of sources, including the web, in a modern statistical inference and visualization paradigm. The course will be based in the programming language R, but will also use HTML, regular expressions, basic unix tools, XML, and SQL. Supervised and unsupervised statistical learning techniques made possible by recent advances in computing power will also be covered. 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	Online	AU	9:30AM -11:30AM	T	n.a.	He, J	Part of Term: 1 Date Range: 08/24/20-12/09/20 Credit: 3 hours Section Title: Introduction to Data Science Section Info: TOPIC: Introduction to Data Science This course introduces students to data science approaches that have emerged from recent advances in programming and computing technology. They will learn to collect and use data from a variety of sources, including the web, in a modern statistical inference and visualization paradigm. The course will be based in the programming language R, but will also use HTML, regular expressions, basic unix tools, XML, and SQL. Supervised and unsupervised statistical learning techniques made possible by recent advances in computing power will also be covered. 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	HNG	ARRANGED	n.a.	n.a.	Nguyen, H	Part of Term: 1 Date Range: 08/24/20-12/09/20 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 students in the Statistics department. Restricted to Graduate - Urbana-Champaign.
46976	Online	HNU	ARRANGED	n.a.	n.a.	Nguyen, H	Part of Term: 1 Date Range: 08/24/20-12/09/20 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 Statistics or Statistics & Computer Science major(s). Restricted to Undergrad - Urbana-Champaign.
74814	Online	JJB	ARRANGED	n.a.	n.a.	Balamuta, J	Part of Term: 1 Date Range: 08/24/20-12/09/20 Credit: 3 hours Section Title: Fundamentals of Deep Learning Section Info: Topic: Fundamentals of Deep Learning (Undergrads only) 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-Requisite: STAT 432 (possibly co-requisite). Restriction(s): Restricted to Undergrad - Urbana-Champaign.
60255	Online	JLG	ARRANGED	n.a.	n.a.	Liu, J	Part of Term: 1 Date Range: 08/24/20-12/09/20 Credit: 4 hours Section Title: Nonparametric Statistics Section Info: Topic: Nonparametric statistics. This course considers nonparametric methods of statistical analysis. Topics include smoothing and spline methods for estimation of probability density and regression functions, as well as resampling techniques for inference. Prerequisites: STAT 410 and STAT 425. Restriction(s): Restricted to Graduate - Urbana-Champaign.
60257	Online	JLU	ARRANGED	n.a.	n.a.	Liu, J	Part of Term: 1 Date Range: 08/24/20-12/09/20 Credit: 3 hours Section Title: Nonparametric Statistics Section Info: Topic: Nonparametric statistics. This course considers nonparametric methods of statistical analysis. Topics include smoothing and spline methods for estimation of probability density and regression functions, as well as resampling techniques for inference. Prerequisites: STAT 410 and STAT 425. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
71665	Online	OGR	ARRANGED	n.a.	n.a.	Eddelbuettel, D	Part of Term: 1 Date Range: 08/24/20-12/09/20 Credit: 4 hours Section Title: DataScience ProgrammingMethods Section Info: This course provides the computational foundation for rigorous data science work, both applied and in research. Starting from key foundations (the shell, git, Markdown and SQL), we focus on a solid introduction to programming in R. Next we discuss keys to reproducible computing (R packages, Docker) as well as some computational and algorithmic foundations. Finally, we examine in some detail extensions for better performance, notably using C++ with R. Course Information: 3 undergraduate hours. 4 graduate hours. May be repeated with approval. Prerequisite: STAT 410, STAT 420, and STAT 425 or consent of instructor. Students who previously enrolled in STAT 385 should not register for this course. For Statistics course registration information: go.illinois.edu/StatisticsRegistration Restriction(s): Restricted to Graduate - Urbana-Champaign.
71666	Online	OUG	ARRANGED	n.a.	n.a.	Eddelbuettel, D	Part of Term: 1 Date Range: 08/24/20-12/09/20 Credit: 3 hours Section Title: DataScience ProgrammingMethods Section Info: This course provides the computational foundation for rigorous data science work, both applied and in research. Starting from key foundations (the shell, git, Markdown and SQL), we focus on a solid introduction to programming in R. Next we discuss keys to reproducible computing (R packages, Docker) as well as some computational and algorithmic foundations. Finally, we examine in some detail extensions for better performance, notably using C++ with R. Course Information: 3 undergraduate hours. 4 graduate hours. May be repeated with approval. Prerequisite: STAT 410, STAT 420, and STAT 425 or consent of instructor. Students who previously enrolled in STAT 385 should not register for this course. For Statistics course registration information: go.illinois.edu/StatisticsRegistration Restriction(s): Restricted to Undergrad - Urbana-Champaign.
70884	Online	VEG	6:30PM -7:50PM	TR	n.a.	Ellison, T	Part of Term: 1 Date Range: 08/24/20-12/09/20 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.
70885	Online	VEU	6:30PM -7:50PM	TR	n.a.	Ellison, T	Part of Term: 1 Date Range: 08/24/20-12/09/20 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.