Enrollment and waitlist data for current and upcoming courses refresh every 10 minutes; all other information as of 6:00 AM.
| 01/04 - 01/28 | ||||||
| M | T | W | Th | F | Sa | Su |
| + asynchronous coursework | ||||||
Subject: Data Science (DASC)
CRN: 10175
Online: Asynchronous | Lecture
Online
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
Instructor: TBD
This course is composed of an in-depth study of the processes through which statistics can be used to learn about environments and events. There will be an intensive focus on the application, analysis, interpretation, and presentation of both descriptive and inferential statistics in a variety of real world contexts. Topics include data collection, research design, data visualization, sampling distributions, confidence intervals and hypothesis testing, inference for one and two samples, chi-square tests for goodness of fit and association, analysis of variance, and simple and multiple linear regression. Extensive data analysis using modern statistical software is an essential component of this course. Prerequisites: Math placement at level of MATH 108 or above; or completion of MATH 006, 007, 100, 101, 103, 104, 105, 107, 108, 111, or 113. NOTE: Students who receive credit for DASC 120 may not receive credit for DASC 111 or DASC 112.
4 Credits
| 01/04 - 01/28 | ||||||
| M | T | W | Th | F | Sa | Su |
| + asynchronous coursework | ||||||
Subject: Data Science (DASC)
CRN: 10176
Online: Asynchronous | Lab
Online
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
Instructor: TBD
This course is composed of an in-depth study of the processes through which statistics can be used to learn about environments and events. There will be an intensive focus on the application, analysis, interpretation, and presentation of both descriptive and inferential statistics in a variety of real world contexts. Topics include data collection, research design, data visualization, sampling distributions, confidence intervals and hypothesis testing, inference for one and two samples, chi-square tests for goodness of fit and association, analysis of variance, and simple and multiple linear regression. Extensive data analysis using modern statistical software is an essential component of this course. Prerequisites: Math placement at level of MATH 108 or above; or completion of MATH 006, 007, 100, 101, 103, 104, 105, 107, 108, 111, or 113. NOTE: Students who receive credit for DASC 120 may not receive credit for DASC 111 or DASC 112.
0 Credits