STAT 480

Fall 2026 All Classes

All Classes

Big Data Analytics

Credit: 3 OR 4 hours.

Examines current topics and techniques for efficiently and effectively managing and analyzing large-scale data. The course focuses on applications of advanced statistical analysis in data science for massive data sets. Topics include current best practices and technologies for implementation such as parallel and distributed processing, distributed storage techniques, and modern computational frameworks such as cloud computing.

3 undergraduate hours. 4 graduate hours. Prerequisite: (STAT 440 or STAT 447) and (STAT 420 or STAT 425); or permission of the instructor.

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STAT 480 class schedule data for fall 2026
	Status		CRN	Type	Section	Time	Day	Location	Instructor	Section Details
			65110	Lecture-Discussion	1GR	12:30PM -1:50PM	TR	2310 Everitt Laboratory	Wang, Y	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Info: The course focuses on practical approaches for working with high-dimensional and large-sample datasets using modern statistical learning and computing tools. Topics include data exploration and visualization, regression and model selection in high-dimensional settings, multiple testing, and scalable algorithms for large datasets. The course also introduces efficient computing techniques in R, parallel and GPU computing, and distributed and cloud computing frameworks. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MS:Statistics -UIUC, PHD:Statistics -UIUC, MS:Statistics -- Applied -UIUC, MS: Statistics: Analytics, or MS:PA Risk Mgmt - UIUC.
			65111	Lecture-Discussion	1UG	12:30PM -1:50PM	TR	2310 Everitt Laboratory	Wang, Y	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Info: The course focuses on practical approaches for working with high-dimensional and large-sample datasets using modern statistical learning and computing tools. Topics include data exploration and visualization, regression and model selection in high-dimensional settings, multiple testing, and scalable algorithms for large datasets. The course also introduces efficient computing techniques in R, parallel and GPU computing, and distributed and cloud computing frameworks. Restriction(s): Restricted to Statistics or Statistics & Computer Science major(s). Restricted to Undergrad - Urbana-Champaign.