STAT 430

Fall 2019 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 fall 2019
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
55664	Lecture-Discussion	AG	1:00PM -3:50PM	M	126 Grad Sch of Lib & Info Science	Stodden, V	Part of Term: 1 Date Range: 08/26/19-12/11/19 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 Graduate - Urbana-Champaign.
55666	Lecture-Discussion	AU	1:00PM -3:50PM	M	126 Grad Sch of Lib & Info Science	Stodden, V	Part of Term: 1 Date Range: 08/26/19-12/11/19 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.
71665	Online	OGR	ARRANGED	n.a.	n.a.	Eddelbuettel, D	Part of Term: 1 Date Range: 08/26/19-12/11/19 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: 08/26/19-12/11/19 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.