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

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

  Elizabeth Hoefer

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

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

  Elizabeth Hoefer

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

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

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

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

  Amelia McNamara

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Subject: Mathematics (MATH)

CRN: 21309

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

Instructor: TBD

This survey of basic mathematical concepts includes both modern and historical perspectives. Emphasis is on the development and appreciation of mathematical ideas and their relationship to other disciplines. Topics include, among others: mathematical problem-solving, set theory, graph theory, an introduction to randomness, counting and probability, statistics and data exploration, measurement and symmetry, and recursion.

4 Credits

Subject: Mathematics (MATH)

CRN: 21310

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Nick Clark

Elementary set theory, linear equations and matrices, linear programming (optional), finite probability, applications primarily in business and the social sciences. Offered Fall, J-Term, Spring and Summer. 

4 Credits

Subject: Mathematics (MATH)

CRN: 21311

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

Instructor: TBD

Elementary set theory, linear equations and matrices, linear programming (optional), finite probability, applications primarily in business and the social sciences. Offered Fall, J-Term, Spring and Summer. 

4 Credits

Subject: Mathematics (MATH)

CRN: 21312

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Junsen Tang

Elementary set theory, linear equations and matrices, linear programming (optional), finite probability, applications primarily in business and the social sciences. Offered Fall, J-Term, Spring and Summer. 

4 Credits

Subject: Mathematics (MATH)

CRN: 21313

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

Instructor: TBD

Elementary set theory, linear equations and matrices, linear programming (optional), finite probability, applications primarily in business and the social sciences. Offered Fall, J-Term, Spring and Summer. 

4 Credits

Subject: Mathematics (MATH)

CRN: 21314

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Junsen Tang

Elementary set theory, linear equations and matrices, linear programming (optional), finite probability, applications primarily in business and the social sciences. Offered Fall, J-Term, Spring and Summer. 

4 Credits

Subject: Mathematics (MATH)

CRN: 21315

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Seul ki Kang

Elementary set theory, linear equations and matrices, linear programming (optional), finite probability, applications primarily in business and the social sciences. Offered Fall, J-Term, Spring and Summer. 

4 Credits

Subject: Mathematics (MATH)

CRN: 21316

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

Instructor: TBD

Elementary set theory, linear equations and matrices, linear programming (optional), finite probability, applications primarily in business and the social sciences. Offered Fall, J-Term, Spring and Summer. 

4 Credits

Subject: Mathematics (MATH)

CRN: 21317

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Molly Peterson

Elementary set theory, linear equations and matrices, linear programming (optional), finite probability, applications primarily in business and the social sciences. Offered Fall, J-Term, Spring and Summer. 

4 Credits

Subject: Mathematics (MATH)

CRN: 21318

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Seul ki Kang

Elementary set theory, linear equations and matrices, linear programming (optional), finite probability, applications primarily in business and the social sciences. Offered Fall, J-Term, Spring and Summer. 

4 Credits

Subject: Mathematics (MATH)

CRN: 21319

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Diana Martelly

Elementary set theory, linear equations and matrices, linear programming (optional), finite probability, applications primarily in business and the social sciences. Offered Fall, J-Term, Spring and Summer. 

4 Credits

Subject: Mathematics (MATH)

CRN: 21328

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

Other Requirements Met:
     Environmental Sci. Major Appr

  Jolene Gleason

The second course of a two-course sequence designed to integrate introductory calculus material with the algebraic and trigonometric topics necessary to support that study. Review topics include: exponential and logarithmic functions, trigonometric functions and their inverses and associated graphs. Calculus topics include: derivatives of the transcendental functions, applications of those derivatives and an introduction to integration. Prerequisite: a grade of C- or better in MATH 108. NOTE: Students who receive credit for MATH 109 may not receive credit for MATH 103, 104, 105, 111, or 113.

4 Credits

Subject: Mathematics (MATH)

CRN: 21329

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

Other Requirements Met:
     Environmental Sci. Major Appr

  Jolene Gleason

The second course of a two-course sequence designed to integrate introductory calculus material with the algebraic and trigonometric topics necessary to support that study. Review topics include: exponential and logarithmic functions, trigonometric functions and their inverses and associated graphs. Calculus topics include: derivatives of the transcendental functions, applications of those derivatives and an introduction to integration. Prerequisite: a grade of C- or better in MATH 108. NOTE: Students who receive credit for MATH 109 may not receive credit for MATH 103, 104, 105, 111, or 113.

4 Credits

Subject: Mathematics (MATH)

CRN: 21330

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

Other Requirements Met:
     Environmental Sci. Major Appr

  Nathan Harding

The second course of a two-course sequence designed to integrate introductory calculus material with the algebraic and trigonometric topics necessary to support that study. Review topics include: exponential and logarithmic functions, trigonometric functions and their inverses and associated graphs. Calculus topics include: derivatives of the transcendental functions, applications of those derivatives and an introduction to integration. Prerequisite: a grade of C- or better in MATH 108. NOTE: Students who receive credit for MATH 109 may not receive credit for MATH 103, 104, 105, 111, or 113.

4 Credits

Subject: Mathematics (MATH)

CRN: 21331

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

Other Requirements Met:
     Environmental Sci. Major Appr

  Nathan Harding

The second course of a two-course sequence designed to integrate introductory calculus material with the algebraic and trigonometric topics necessary to support that study. Review topics include: exponential and logarithmic functions, trigonometric functions and their inverses and associated graphs. Calculus topics include: derivatives of the transcendental functions, applications of those derivatives and an introduction to integration. Prerequisite: a grade of C- or better in MATH 108. NOTE: Students who receive credit for MATH 109 may not receive credit for MATH 103, 104, 105, 111, or 113.

4 Credits

Subject: Mathematics (MATH)

CRN: 21332

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

Other Requirements Met:
     Environmental Sci. Major Appr

  Tom Rogers

The second course of a two-course sequence designed to integrate introductory calculus material with the algebraic and trigonometric topics necessary to support that study. Review topics include: exponential and logarithmic functions, trigonometric functions and their inverses and associated graphs. Calculus topics include: derivatives of the transcendental functions, applications of those derivatives and an introduction to integration. Prerequisite: a grade of C- or better in MATH 108. NOTE: Students who receive credit for MATH 109 may not receive credit for MATH 103, 104, 105, 111, or 113.

4 Credits

Subject: Mathematics (MATH)

CRN: 21333

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

Other Requirements Met:
     Environmental Sci. Major Appr

  Diana Martelly

The second course of a two-course sequence designed to integrate introductory calculus material with the algebraic and trigonometric topics necessary to support that study. Review topics include: exponential and logarithmic functions, trigonometric functions and their inverses and associated graphs. Calculus topics include: derivatives of the transcendental functions, applications of those derivatives and an introduction to integration. Prerequisite: a grade of C- or better in MATH 108. NOTE: Students who receive credit for MATH 109 may not receive credit for MATH 103, 104, 105, 111, or 113.

4 Credits

Subject: Mathematics (MATH)

CRN: 21334

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

Other Requirements Met:
     Environmental Sci. Major Appr

  Diana Martelly

The second course of a two-course sequence designed to integrate introductory calculus material with the algebraic and trigonometric topics necessary to support that study. Review topics include: exponential and logarithmic functions, trigonometric functions and their inverses and associated graphs. Calculus topics include: derivatives of the transcendental functions, applications of those derivatives and an introduction to integration. Prerequisite: a grade of C- or better in MATH 108. NOTE: Students who receive credit for MATH 109 may not receive credit for MATH 103, 104, 105, 111, or 113.

4 Credits

Subject: Mathematics (MATH)

CRN: 21335

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Eric Rawdon

An introductory course in calculus with motivation and examples drawn from business and the social sciences whenever possible. Does not include the calculus of trigonometric functions. Offered Fall and Spring. Prerequisite: a grade of C- or above in MATH 103 or MATH 105 or placement at MATH 111 or above. Four years of high school mathematics, including college algebra, are also recommended as background for this course. Students who are considering taking MATH 114 should take MATH 113 instead of MATH 111. NOTE: Students who receive credit for MATH 111 may not receive credit for MATH 108, 109, or 113.

4 Credits

Subject: Mathematics (MATH)

CRN: 21336

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

Other Requirements Met:
     Environmental Sci. Major Appr

Instructor: TBD

An introductory course in calculus: limits; derivatives and integrals of algebraic, exponential, logarithmic and trigonometric functions of one real variable; applications of the derivative in engineering and the natural sciences. Offered Fall, Spring and Summer. Prerequisite: a grade of C- or above in MATH 104 or 105 or placement at MATH 113 or above. Four years of high school mathematics, including college algebra and trigonometry, also are recommended as background for this course. NOTE: Students who receive credit for MATH 113 may not receive credit for MATH 108, 109, or 111.

4 Credits

Subject: Mathematics (MATH)

CRN: 21337

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Thomas Hoft

Techniques of integration; applications of integration; infinite series; parametric/polar equations. Offered Fall, Spring and Summer. Prerequisite: a grade of C- or above in MATH 112 or in MATH 113 or MATH 109

4 Credits

Subject: Mathematics (MATH)

CRN: 21338

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Brenda Kroschel

Techniques of integration; applications of integration; infinite series; parametric/polar equations. Offered Fall, Spring and Summer. Prerequisite: a grade of C- or above in MATH 112 or in MATH 113 or MATH 109

4 Credits

Subject: Mathematics (MATH)

CRN: 21339

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Brenda Kroschel

Techniques of integration; applications of integration; infinite series; parametric/polar equations. Offered Fall, Spring and Summer. Prerequisite: a grade of C- or above in MATH 112 or in MATH 113 or MATH 109

4 Credits

Subject: Mathematics (MATH)

CRN: 21340

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Natasa Dragovic

Techniques of integration; applications of integration; infinite series; parametric/polar equations. Offered Fall, Spring and Summer. Prerequisite: a grade of C- or above in MATH 112 or in MATH 113 or MATH 109

4 Credits

Subject: Mathematics (MATH)

CRN: 21341

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Natasa Dragovic

Techniques of integration; applications of integration; infinite series; parametric/polar equations. Offered Fall, Spring and Summer. Prerequisite: a grade of C- or above in MATH 112 or in MATH 113 or MATH 109

4 Credits

Subject: Mathematics (MATH)

CRN: 21342

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Debbie Monson

An examination of the mathematical underpinnings of the K-8 school curriculum with an emphasis on the conceptual understanding of mathematics. Topics include foundations of integer and rational arithmetic, notions of place-value and base, number sense and estimation, ratio and proportion, and mathematical problem-solving. This course is recommended as the first course in a three-course sequence in mathematics for prospective elementary teachers. Offered Spring. Prerequisites: Any EDUC course or concurrent registration in any EDUC course.

4 Credits

Subject: Mathematics (MATH)

CRN: 21343

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Sarah Anderson

A survey of basic discrete mathematical concepts. Topics include: Boolean algebra, logic, analysis of algorithms, mathematical induction and matrices. Focus on applications to computer science. Offered Fall and Spring.Prerequisite: A C‐ or better in either Math 109, Math 111, or Math 113 and a C‐ or better  in either CISC 130 or CISC 131, or instructor permission

4 Credits

Subject: Mathematics (MATH)

CRN: 21344

In Person | Lecture

St Paul: In Person

Core Requirements Met:
     [Core] Quant Analysis

  Sarah Anderson

A survey of basic discrete mathematical concepts. Topics include: Boolean algebra, logic, analysis of algorithms, mathematical induction and matrices. Focus on applications to computer science. Offered Fall and Spring.Prerequisite: A C‐ or better in either Math 109, Math 111, or Math 113 and a C‐ or better  in either CISC 130 or CISC 131, or instructor permission

4 Credits