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| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
Subject: Data Science (DASC)
CRN: 21005
Online: Asynchronous | Lecture
Online
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
Sustainability (SUST)
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
10:55 am |
10:55 am |
10:55 am |
||||
Subject: Data Science (DASC)
CRN: 21006
In Person | Lecture
St Paul: John Roach Center 126
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
12:15 pm |
12:15 pm |
12:15 pm |
||||
Subject: Data Science (DASC)
CRN: 21007
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
8:00 am |
8:00 am |
|||||
Subject: Data Science (DASC)
CRN: 21008
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
9:55 am |
9:55 am |
|||||
Subject: Data Science (DASC)
CRN: 21009
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
1:30 pm |
1:30 pm |
|||||
Subject: Data Science (DASC)
CRN: 21010
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
8:00 am |
||||||
Subject: Data Science (DASC)
CRN: 21011
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 21012
In Person | Lab
St Paul: O'Shaughnessy Science Hall 431
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 21013
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 21015
In Person | Lab
St Paul: O'Shaughnessy Science Hall 431
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 21016
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 21017
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
3:25 pm |
||||||
Subject: Data Science (DASC)
CRN: 21018
In Person | Lab
St Paul: O'Shaughnessy Science Hall 431
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
3:25 pm |
||||||
Subject: Data Science (DASC)
CRN: 21019
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 21020
In Person | Lab
St Paul: O'Shaughnessy Science Hall 431
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 21021
In Person | Lab
St Paul: O'Shaughnessy Science Hall 431
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 22622
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
8:00 am |
||||||
Subject: Data Science (DASC)
CRN: 21022
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 21023
In Person | Lab
St Paul: O'Shaughnessy Science Hall 431
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 21024
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 21025
In Person | Lab
St Paul: O'Shaughnessy Science Hall 431
Core Requirements Met:
[Core] Quant Analysis
Other Requirements Met:
Liberal Arts Bus Minor Appr
School of Ed Transfer Course
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
7:30 pm |
||||||
Subject: Data Science (DASC)
CRN: 21026
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
8:15 am |
8:15 am |
8:15 am |
||||
Subject: Data Science (DASC)
CRN: 21027
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: 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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
8:00 am |
8:00 am |
|||||
Subject: Data Science (DASC)
CRN: 21028
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 432
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
9:55 am |
9:55 am |
|||||
Subject: Data Science (DASC)
CRN: 21029
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 434
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
3:25 pm |
3:25 pm |
|||||
Subject: Data Science (DASC)
CRN: 21030
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 434
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
10:55 am |
10:55 am |
10:55 am |
||||
Subject: Data Science (DASC)
CRN: 22468
In Person | Lecture
St Paul: John Roach Center 426
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
1:30 pm |
1:30 pm |
|||||
Subject: Data Science (DASC)
CRN: 21031
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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
12:15 pm |
12:15 pm |
12:15 pm |
||||
Subject: Data Science (DASC)
CRN: 21032
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 428
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. Software used in the course includes, but is not limited to, JMP, Excel, Java, R, Python, and Minitab. 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
| 02/02 - 05/22 | ||||||
| M | T | W | Th | F | Sa | Su |
1:35 pm |
1:35 pm |
1:35 pm |
||||
Subject: Data Science (DASC)
CRN: 22623
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 428
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. Software used in the course includes, but is not limited to, JMP, Excel, Java, R, Python, and Minitab. 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