Enrollment and waitlist data for current and upcoming courses refresh every 10 minutes; all other information as of 6:00 AM.
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
| + asynchronous coursework | ||||||
Subject: Data Science (DASC)
CRN: 41296
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
| + asynchronous coursework | ||||||
Subject: Data Science (DASC)
CRN: 41297
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
9:35 am |
9:35 am |
9:35 am |
||||
Subject: Data Science (DASC)
CRN: 41298
In Person | Lecture
St Paul: Owens Science Hall 150
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
Sustainability (SUST)
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
10:55 am |
10:55 am |
10:55 am |
||||
Subject: Data Science (DASC)
CRN: 41299
In Person | Lecture
St Paul: Owens Science Hall 150
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
Sustainability (SUST)
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
8:00 am |
8:00 am |
|||||
Subject: Data Science (DASC)
CRN: 41300
In Person | Lecture
St Paul: Owens Science Hall 150
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
Sustainability (SUST)
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
9:55 am |
9:55 am |
|||||
Subject: Data Science (DASC)
CRN: 41301
In Person | Lecture
St Paul: Owens Science Hall 150
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
Sustainability (SUST)
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
1:30 pm |
1:30 pm |
|||||
Subject: Data Science (DASC)
CRN: 41302
In Person | Lecture
St Paul: Owens Science Hall 150
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
Sustainability (SUST)
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
8:00 am |
||||||
Subject: Data Science (DASC)
CRN: 41303
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 41304
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 41305
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 41306
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 41307
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
3:25 pm |
||||||
Subject: Data Science (DASC)
CRN: 41308
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
3:25 pm |
||||||
Subject: Data Science (DASC)
CRN: 41309
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 41310
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 41311
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 41312
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 41313
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
8:00 am |
||||||
Subject: Data Science (DASC)
CRN: 41314
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 41315
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 41316
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 41317
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 41318
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
1:30 pm |
1:30 pm |
|||||
Subject: Data Science (DASC)
CRN: 41319
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 434
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
9:55 am |
9:55 am |
|||||
Subject: Data Science (DASC)
CRN: 41320
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
3:25 pm |
3:25 pm |
|||||
Subject: Data Science (DASC)
CRN: 41321
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
1:35 pm |
1:35 pm |
1:35 pm |
||||
Subject: Data Science (DASC)
CRN: 41322
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 434
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
8:00 am |
8:00 am |
|||||
Subject: Data Science (DASC)
CRN: 41323
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 434
Core Requirements Met:
[Core] Global Perspective
Other Requirements Met:
Sustainability (SUST)
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
9:55 am |
9:55 am |
|||||
Subject: Data Science (DASC)
CRN: 41324
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 434
Core Requirements Met:
[Core] Global Perspective
Other Requirements Met:
Sustainability (SUST)
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
| 09/09 - 12/22 | ||||||
| M | T | W | Th | F | Sa | Su |
8:15 am |
8:15 am |
8:15 am |
||||
Subject: Data Science (DASC)
CRN: 41325
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 431
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. 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