STAT 430

Spring 2019 All Classes

All Classes

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.

My Favorites

My Schedule

Section Status updates every 10 minutes.

STAT 430 class schedule data for spring 2019
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
60247	Lecture	1GR	9:30AM -10:50AM	TR	103 Transportation Building	Hua, L	Part of Term: 1 Date Range: 01/14/19-05/01/19 Credit: 4 hours Section Title: MachineLearning Financial Data Section Info: For Statistics course registration information: go.illinois.edu/StatisticsRegistration TOPIC: Machine Learning for Financial Data Description: This course introduces modern machine learning techniques that are tailored for analyzing financial data. Topics include Financial Data Preprocessing, Ensemble Methods, Cross Validation, Convolutional Neural Networks, Recurrent Neural Networks with Long Short-Term Memory / Gated Recurrent Units, Generative Adversarial Networks. The emphasis is on the basics of these methods and their relevant applications with financial data. PREREQS: A course in linear regression, such as STAT 420 or STAT 425; and basic knowledge about classical machine learning techniques at the level of the book "An Introduction to Statistical Learning"; and basic skills in using R to implement machine learning algorithms and conduct data analysis; Restriction(s): Restricted to Graduate - Urbana-Champaign.
60249	Lecture	1UG	9:30AM -10:50AM	TR	103 Transportation Building	Hua, L	Part of Term: 1 Date Range: 01/14/19-05/01/19 Credit: 3 hours Section Title: MachineLearning Financial Data Section Info: For Statistics course registration information: go.illinois.edu/StatisticsRegistration TOPIC: Machine Learning for Financial Data Description: This course introduces modern machine learning techniques that are tailored for analyzing financial data. Topics include Financial Data Preprocessing, Ensemble Methods, Cross Validation, Convolutional Neural Networks, Recurrent Neural Networks with Long Short-Term Memory / Gated Recurrent Units, Generative Adversarial Networks. The emphasis is on the basics of these methods and their relevant applications with financial data. PREREQS: A course in linear regression, such as STAT 420 or STAT 425; and basic knowledge about classical machine learning techniques at the level of the book "An Introduction to Statistical Learning"; and basic skills in using R to implement machine learning algorithms and conduct data analysis; Restriction(s): Restricted to Undergrad - Urbana-Champaign.
69375	Lecture-Discussion	AG	2:00PM -3:50PM	M	126 Grad Sch of Lib & Info Science	Stodden, V	Part of Term: 1 Date Range: 01/14/19-05/01/19 Credit: 4 hours Section Title: Introduction to Data Science Restriction(s): Restricted to Graduate - Urbana-Champaign.
69376	Lecture-Discussion	AU	2:00PM -3:50PM	M	126 Grad Sch of Lib & Info Science	Stodden, V	Part of Term: 1 Date Range: 01/14/19-05/01/19 Credit: 3 hours Section Title: Introduction to Data Science Restriction(s): Restricted to Statistics or Statistics & Computer Science major(s). Restricted to Undergrad - Urbana-Champaign.
69346	Online	OGR	ARRANGED	n.a.	n.a.	Eddelbuettel, D	Part of Term: 1 Date Range: 01/14/19-05/01/19 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. Restriction(s): Restricted to Graduate - Urbana-Champaign.
69347	Online	OUG	ARRANGED	n.a.	n.a.	Eddelbuettel, D	Part of Term: 1 Date Range: 01/14/19-05/01/19 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. Restriction(s): Restricted to Undergrad - Urbana-Champaign.