Enrollment and waitlist data for current and upcoming courses refresh every 10 minutes; all other information as of 6:00 AM.
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
| + asynchronous coursework | ||||||
Subject: Data Science (DASC)
CRN: 20971
Online: Asynchronous | Lecture
Online
This course provides students who already have a solid conceptual understanding of statistics the opportunity to apply their knowledge to analyzing data using modern statistical software. Topics include data visualization, inference for one and two samples, analysis of variance, chi-square tests for goodness of fit and association, and simple and multiple linear regression. Prerequisites: DASC 111 or AP Statistics Credit. Note, students who receive credit for DASC 112 may not receive credit for DASC 120.
2 Credits
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
| + asynchronous coursework | ||||||
Subject: Data Science (DASC)
CRN: 20972
Online: Asynchronous | Lecture
Online
This course provides students who already have a solid conceptual understanding of statistics the opportunity to apply their knowledge to analyzing data using modern statistical software. Topics include data visualization, inference for one and two samples, analysis of variance, chi-square tests for goodness of fit and association, and simple and multiple linear regression. Prerequisites: DASC 111 or AP Statistics Credit. Note, students who receive credit for DASC 112 may not receive credit for DASC 120.
2 Credits
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
8:15 am |
8:15 am |
8:15 am |
||||
Subject: Data Science (DASC)
CRN: 20973
In Person | Lecture
St Paul: In Person
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
9:35 am |
9:35 am |
9:35 am |
||||
Subject: Data Science (DASC)
CRN: 20974
In Person | Lecture
St Paul: In Person
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
10:55 am |
10:55 am |
10:55 am |
||||
Subject: Data Science (DASC)
CRN: 20975
In Person | Lecture
St Paul: In Person
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
8:00 am |
8:00 am |
|||||
Subject: Data Science (DASC)
CRN: 20976
In Person | Lecture
St Paul: In Person
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
9:55 am |
9:55 am |
|||||
Subject: Data Science (DASC)
CRN: 20977
In Person | Lecture
St Paul: In Person
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
1:30 pm |
1:30 pm |
|||||
Subject: Data Science (DASC)
CRN: 20978
In Person | Lecture
St Paul: In Person
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20979
In Person | Lab
St Paul: O'Shaughnessy Science Hall 432
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20980
In Person | Lab
St Paul: O'Shaughnessy Science Hall 434
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20981
In Person | Lab
St Paul: John Roach Center 426
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20982
In Person | Lab
St Paul: John Roach Center 426
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20983
In Person | Lab
St Paul: O'Shaughnessy Science Hall 434
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
3:25 pm |
||||||
Subject: Data Science (DASC)
CRN: 20984
In Person | Lab
St Paul: O'Shaughnessy Science Hall 434
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
3:25 pm |
||||||
Subject: Data Science (DASC)
CRN: 20985
In Person | Lab
St Paul: John Roach Center 426
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20986
In Person | Lab
St Paul: John Roach Center 426
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20987
In Person | Lab
St Paul: O'Shaughnessy Science Hall 434
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20988
In Person | Lab
St Paul: O'Shaughnessy Science Hall 432
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20989
In Person | Lab
St Paul: O'Shaughnessy Science Hall 432
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20990
In Person | Lab
St Paul: O'Shaughnessy Science Hall 434
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20991
In Person | Lab
St Paul: John Roach Center 426
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20992
In Person | Lab
St Paul: John Roach Center 426
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20993
In Person | Lab
St Paul: O'Shaughnessy Science Hall 434
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20994
In Person | Lab
St Paul: O'Shaughnessy Science Hall 434
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 20995
In Person | Lab
St Paul: John Roach Center 426
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
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
9:55 am |
9:55 am |
|||||
Subject: Data Science (DASC)
CRN: 20996
In Person | Lecture
St Paul: John Roach Center 426
This course provides students with an introduction to the field of data science. Students learn foundational skills, including basic data visualization, data wrangling, descriptive modeling techniques, and simulation-based inference. All material is grounded in contextual data examples, and consideration of data context and ethical issues is paramount. Prerequisites: 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.
4 Credits
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
3:25 pm |
3:25 pm |
|||||
Subject: Data Science (DASC)
CRN: 20997
In Person | Lecture
St Paul: John Roach Center 426
This course provides students with an introduction to the field of data science. Students learn foundational skills, including basic data visualization, data wrangling, descriptive modeling techniques, and simulation-based inference. All material is grounded in contextual data examples, and consideration of data context and ethical issues is paramount. Prerequisites: 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.
4 Credits
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
8:00 am |
8:00 am |
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Subject: Data Science (DASC)
CRN: 20998
In Person | Lecture
St Paul: John Roach Center 426
Instructor: TBD
In this course, students acquire the knowledge and skill required to effectively apply intermediate statistical methods in biology, medicine, public health, and other health-related fields. There is an emphasis on the following inferential statistical techniques: one-way and factorial ANOVA, interactions, repeated measures, and general linear models; logistic regression for cohort and case-control studies; nonparametric and distribution-free statistics; loglinear models and contingency table analyses; survival data, Kaplan-Meier methods, and proportional hazards models. Prerequisites: DASC 112, DASC 120, STAT 303, or STAT 313.
4 Credits
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
1:30 pm |
1:30 pm |
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Subject: Data Science (DASC)
CRN: 20999
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 432
Requirements Met:
Writing in the Discipline
This course provides students with the knowledge to effectively use various forms of regression models to address problems in a variety of fields. Students learn both simple and multiple forms of linear, ordinal, nominal, and beta regression models. There is an emphasis on simultaneous inference, model selection and validation, detecting collinearity and autocorrelation, and remedial measures for model violations. Students are also introduced to the use of time series and forecasting methods. Prerequisites: Grade of C- or higher in DASC 112 or DASC 120.
4 Credits
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
3:25 pm |
3:25 pm |
|||||
Subject: Data Science (DASC)
CRN: 21000
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 432
Requirements Met:
Writing in the Discipline
This course provides students with the knowledge to effectively use various forms of regression models to address problems in a variety of fields. Students learn both simple and multiple forms of linear, ordinal, nominal, and beta regression models. There is an emphasis on simultaneous inference, model selection and validation, detecting collinearity and autocorrelation, and remedial measures for model violations. Students are also introduced to the use of time series and forecasting methods. Prerequisites: Grade of C- or higher in DASC 112 or DASC 120.
4 Credits
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
1:35 pm |
1:35 pm |
1:35 pm |
||||
Subject: Data Science (DASC)
CRN: 21001
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 432
This course will prepare students to effectively communicate the insights from data analysis. The course will cover the three main methods of communicating information about data – visually, orally, and in writing. Students will learn to tailor their communication to their audience and create publication-ready and boardroom-ready presentations of their results. Prerequisites: CISC 130 or CISC 131; and DASC 112, DASC 120, STAT 303, or STAT 314.
4 Credits
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
1:30 pm |
1:30 pm |
|||||
Subject: Data Science (DASC)
CRN: 21002
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 428
This course introduces students to advanced computational methods in statistics and data analysis that require a thorough knowledge of a programming language such as Python or R. There will be an intensive focus on investigating the correlation and covariance structure of data, including data extraction and modification, dimensionality reduction, and structural equation modeling. Prerequisites: Grades of C- or higher in CISC 130 or 131 and in MATH 109 or 112 or 113 and in DASC 240, STAT 303, STAT 314, or ECON 315.
4 Credits
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
8:15 am |
8:00 am |
8:15 am |
8:15 am |
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Subject: Data Science (DASC)
CRN: 21003
In Person | Lecture/Lab
St Paul: O'Shaughnessy Science Hall 428
St Paul: O'Shaughnessy Science Hall 432
In this course students will learn methods for working with massive and complex data. They will explore these topics from both statistical and computational perspectives. Topics include data preparation, defining and exploring data sources, pattern discovery, cluster analysis, decision trees, regression, neural networks, memory-based reasoning, survival analysis, and genetic algorithms. Lab included. Prerequisites: Grades of C- or higher in CISC 130 or 131 and in MATH 109 or 112 or 113 and in DASC 240, STAT 333, or ECON 315.
4 Credits
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
10:55 am |
9:55 am |
10:55 am |
10:55 am |
|||
Subject: Data Science (DASC)
CRN: 21004
In Person | Lecture/Lab
St Paul: O'Shaughnessy Science Hall 428
St Paul: O'Shaughnessy Science Hall 432
In this course students will learn methods for working with massive and complex data. They will explore these topics from both statistical and computational perspectives. Topics include data preparation, defining and exploring data sources, pattern discovery, cluster analysis, decision trees, regression, neural networks, memory-based reasoning, survival analysis, and genetic algorithms. Lab included. Prerequisites: Grades of C- or higher in CISC 130 or 131 and in MATH 109 or 112 or 113 and in DASC 240, STAT 333, or ECON 315.
4 Credits
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
3:25 pm |
3:25 pm |
|||||
Subject: Data Science (DASC)
CRN: 22148
Lecture
St Paul: In Person
Requirements Met:
[Core] Signature Work
Instructor: TBD
This course provides students the opportunity to develop and pursue an advanced statistical data analysis with real world relevance and application. In addition to working with a faculty instructor, students are also given the opportunity to collaborate with professional mentors from various industries and to participate in national competitions. Previous sponsors include the Minnesota Department of Natural Resources, the Travelers Companies, U.S. Bancorp, SCOR Reinsurance, Drake Bank, and numerous professors from other departments at St. Thomas. Prerequisites: Grade of C- or higher in DASC 360 and senior standing.
4 Credits
| 02/01 - 05/21 | ||||||
| M | T | W | Th | F | Sa | Su |
3:25 pm |
3:25 pm |
|||||
Subject: Statistics (STAT)
CRN: 21582
In Person | Lecture
St Paul: In Person
Requirements Met:
[Core] Signature Work
This course provides students the opportunity to develop and pursue an advanced statistical analysis with real world relevance and application. In addition to working with a faculty instructor, students are also given the opportunity to collaborate with professional mentors from various industries and to participate in national competitions. Previous sponsors include the Minnesota Department of Natural Resources, the Travelers Companies, U.S. Bancorp, SCOR Reinsurance, Drake Bank, and numerous professors from other departments at St. Thomas. Grade of C- or higher in STAT 360 and senior standing.
4 Credits