Applied Mathematics and Statistics
Department Head
G. Gustave Greivel, Teaching Professor
Professors
Greg Fasshauer
Mahadevan Ganesh
Paul A. Martin
Doug Nychka
Stephen Pankavich
Associate Professors
Soutir Bandyopadhyay
Cecilia Diniz Behn
Dorit Hammerling
Luis Tenorio
Assistant professors
Eileen Martin
Daniel McKenzie
Brennan Sprinkle
Samy Wu Fung
Teaching Professors
Terry Bridgman
Debra Carney
Holly Eklund
Mike Mikucki
Mike Nicholas
Scott Strong
Jennifer Strong
Rebecca Swanson
Teaching Associate Professor
Ashlyn Munson
Teaching Assistant Professors
John Griesmer
Nathan Lenssen
Daisy Philtron
Emeriti Professors
Bernard Bialecki
William C. Navidi
William R. Astle
Norman Bleistein
Ardel J. Boes
Austin R. Brown
John A. DeSanto
Graeme Fairweather
Raymond R. Gutzman
Frank G. Hagin
Donald C.B. Marsh
Willy Hereman
Steven Pruess
Emeriti Associate Professors
Barbara B. Bath
Ruth Maurer
Program Educational Objectives
Bachelor of Science in Applied Mathematics and Statistics
The Applied Mathematics and Statistics Program at Mines has established the following program educational objectives:
Students will demonstrate technical expertise within mathematics and statistics by:
- Designing and implementing solutions to practical problems in science and engineering.
- Using appropriate technology as a tool to solve problems in mathematics.
Students will demonstrate a breadth and depth of knowledge within mathematics by:
- Extending course material to solve original problems.
- Applying knowledge of mathematics to the solution of problems.
- Identifying, formulating and solving mathematics problems.
- Analyzing and interpreting statistical data.
Students will demonstrate an understanding and appreciation for the relationship of mathematics to other fields by:
- Applying mathematics and statistics to solve problems in other fields.
- Working in cooperative multidisciplinary teams.
- Choosing appropriate technology to solve problems in other disciplines.
Students will demonstrate an ability to communicate mathematics effectively by:
- Giving oral presentations.
- Completing written explanations.
- Interacting effectively in cooperative teams.
- Understanding and interpreting written material in mathematics.
Curriculum
The calculus sequence emphasizes mathematics applied to problems students are likely to see in other fields. This supports the curricula in other programs where mathematics is important and assists students who are under prepared in mathematics. Priorities in the mathematics curriculum include applied problems in the mathematics courses and ready utilization of mathematics in the science and engineering courses.
This emphasis on the utilization of mathematics continues through the upper-division courses. Another aspect of the curriculum is the use of a spiraling mode of learning in which concepts are revisited to deepen the students’ understanding.
The applications, teamwork, assessment, and communications emphasis directly address ABET criteria and the Mines graduate profile. The curriculum offers the following two areas of emphasis:
Degree Requirements (Applied Mathematics and Statistics)
Computational and Applied Mathematics (CAM) EMPHASIS
| Freshman | ||||
|---|---|---|---|---|
| Fall | lec | lab | sem.hrs | |
| MATH111 | CALCULUS FOR SCIENTISTS AND ENGINEERS I | 4.0 | 4.0 | |
| CSCI128 | COMPUTER SCIENCE FOR STEM | 3.0 | ||
| CHGN121 | PRINCIPLES OF CHEMISTRY I | 4.0 | ||
| CSM101 | FRESHMAN SUCCESS SEMINAR | 1.0 | ||
| HASS100 | NATURE AND HUMAN VALUES | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH112 | CALCULUS FOR SCIENTISTS AND ENGINEERS II | 4.0 | 4.0 | |
| MATH201 | INTRODUCTION TO STATISTICS | 3.0 | ||
| PHGN100 | PHYSICS I - MECHANICS | 4.0 | ||
| EDNS151 | CORNERSTONE - DESIGN I | 3.0 | ||
| S&W | SUCCESS AND WELLNESS | 1.0 | ||
| 15.0 | ||||
| Sophomore | ||||
| Fall | lec | lab | sem.hrs | |
| MATH213 | CALCULUS FOR SCIENTISTS AND ENGINEERS III | 4.0 | 4.0 | |
| PHGN200 | PHYSICS II-ELECTROMAGNETISM AND OPTICS | 4.0 | ||
| CSCI200 | FOUNDATIONAL PROGRAMMING CONCEPTS & DESIGN | 3.0 | ||
| CSM202 | INTRODUCTION TO STUDENT WELL-BEING AT MINES | 1.0 | ||
| HASS200 | GLOBAL STUDIES | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH225 | DIFFERENTIAL EQUATIONS | 3.0 | ||
| MATH300 | FOUNDATIONS OF ADVANCED MATHEMATICS | 3.0 | ||
| MATH324 | STATISTICAL MODELING | 3.0 | ||
| MATH332 | LINEAR ALGEBRA or 342 | 3.0 | 3.0 | |
| FREE | FREE ELECTIVE | 3.0 | ||
| 15.0 | ||||
| Junior | ||||
| Fall | lec | lab | sem.hrs | |
| MATH307 | INTRODUCTION TO SCIENTIFIC COMPUTING | 3.0 | 3.0 | |
| MATH310 | INTRODUCTION TO MATHEMATICAL MODELING | 3.0 | ||
| MATH334 | INTRODUCTION TO PROBABILITY | 3.0 | 3.0 | |
| CSCIxxx | COMPUTING ELECTIVE1 | 3.0 | ||
| EBGN321 | ENGINEERING ECONOMICS | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH301 | INTRODUCTION TO ANALYSIS | 3.0 | 3.0 | |
| MATH455 | PARTIAL DIFFERENTIAL EQUATIONS | 3.0 | ||
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | ||
| CAS Elective | Culture and Society mid-level | 3.0 | ||
| 15.0 | ||||
| Senior | ||||
| Fall | lec | lab | sem.hrs | |
| MATH408 | COMPUTATIONAL METHODS FOR DIFFERENTIAL EQUATIONS | 3.0 | 3.0 | |
| MATH431 | MATHEMATICAL BIOLOGY | 3.0 | ||
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| CAS ELECTIVE | Culture and Society mid-level | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH484 | MATHEMATICAL AND COMPUTATIONAL MODELING (CAPSTONE) | 4.0 | 4.0 | |
| MATH | MATHEMATICS-CAM ELECTIVE2 | 3.0 | 3.0 | |
| MATH | MATHEMATICS-CAM ELECTIVE2 | 3.0 | 3.0 | |
| FREE | FREE ELECTIVE | 3.0 | 3.0 | |
| CAS Elective | Culture and Society 400-level | 3.0 | ||
| 16.0 | ||||
| Total Semester Hrs: 121.0 | ||||
| Mathematics-CAM elective list. CAM students must choose at least 2 electives from this list. 2 | ||
| MATH440 | PARALLEL SCIENTIFIC COMPUTING | 3.0 |
| MATH454 | COMPLEX ANALYSIS | 3.0 |
| MATH457 | INTEGRAL EQUATIONS | 3.0 |
| MATH458 | ABSTRACT ALGEBRA | 3.0 |
| MATH459 | ASYMPTOTICS | 3.0 |
| MATH472 | MATHEMATICAL AND COMPUTATIONAL NEUROSCIENCE | 3.0 |
| MATH500 | LINEAR VECTOR SPACES | 3.0 |
| MATH501 | APPLIED ANALYSIS | 3.0 |
| MATH514 | APPLIED MATHEMATICS I | 3.0 |
| MATH515 | APPLIED MATHEMATICS II | 3.0 |
| MATH550 | NUMERICAL SOLUTION OF PARTIAL DIFFERENTIAL EQUATIONS | 3.0 |
| MATH551 | COMPUTATIONAL LINEAR ALGEBRA | 3.0 |
| MATH | Department approval required for courses not on this list. | |
| AMS Elective List. CAM students may choose up to 4 electives from this list to satisfy AMS Elective requirements. 2 | ||
| MATH335 | INTRODUCTION TO MATHEMATICAL STATISTICS | 3.0 |
| MATH432 | SPATIAL STATISTICS | 3.0 |
| MATH436 | ADVANCED STATISTICAL MODELING | 3.0 |
| MATH437 | MULTIVARIATE ANALYSIS | 3.0 |
| MATH438 | STOCHASTIC MODELS | 3.0 |
| MATH439 | SURVIVAL ANALYSIS | 3.0 |
| MATH482 | STATISTICS PRACTICUM (CAPSTONE) | 3.0 |
| MATH531 | THEORY OF LINEAR MODELS | 3.0 |
| MATH534 | MATHEMATICAL STATISTICS I | 3.0 |
| MATH535 | MATHEMATICAL STATISTICS II | 3.0 |
| CSCI303 | INTRODUCTION TO DATA SCIENCE | 3.0 |
| CSCI403 | DATA BASE MANAGEMENT | 3.0 |
| CSCI406 | ALGORITHMS | 3.0 |
| MATH | Department approval required for courses not on this list. | |
Statistics (STATS) EMPHASIS
| Freshman | ||||
|---|---|---|---|---|
| Fall | lec | lab | sem.hrs | |
| MATH111 | CALCULUS FOR SCIENTISTS AND ENGINEERS I | 4.0 | 4.0 | |
| CSCI128 | COMPUTER SCIENCE FOR STEM | 3.0 | ||
| CHGN121 | PRINCIPLES OF CHEMISTRY I | 3.0 | 3.0 | 4.0 |
| HASS100 | NATURE AND HUMAN VALUES | 3.0 | 3.0 | |
| CSM101 | FRESHMAN SUCCESS SEMINAR | 1.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH112 | CALCULUS FOR SCIENTISTS AND ENGINEERS II | 4.0 | 4.0 | |
| MATH201 | INTRODUCTION TO STATISTICS | 3.0 | ||
| PHGN100 | PHYSICS I - MECHANICS | 3.0 | 3.0 | 4.0 |
| EDNS151 | CORNERSTONE - DESIGN I | 3.0 | ||
| S&W | SUCCESS AND WELLNESS | 1.0 | ||
| 15.0 | ||||
| Sophomore | ||||
| Fall | lec | lab | sem.hrs | |
| MATH213 | CALCULUS FOR SCIENTISTS AND ENGINEERS III | 4.0 | 4.0 | |
| PHGN200 | PHYSICS II-ELECTROMAGNETISM AND OPTICS | 3.0 | 3.0 | 4.0 |
| CSCI200 | FOUNDATIONAL PROGRAMMING CONCEPTS & DESIGN | 3.0 | ||
| CSM202 | INTRODUCTION TO STUDENT WELL-BEING AT MINES | 1.0 | ||
| HASS200 | GLOBAL STUDIES | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH225 | DIFFERENTIAL EQUATIONS | 3.0 | ||
| MATH300 | FOUNDATIONS OF ADVANCED MATHEMATICS | 3.0 | ||
| MATH324 | STATISTICAL MODELING | 3.0 | ||
| MATH332 | LINEAR ALGEBRA or 342 | 3.0 | 3.0 | |
| FREE | FREE ELECTIVE | 3.0 | ||
| 15.0 | ||||
| Junior | ||||
| Fall | lec | lab | sem.hrs | |
| MATH307 | INTRODUCTION TO SCIENTIFIC COMPUTING | 3.0 | 3.0 | |
| MATH310 | INTRODUCTION TO MATHEMATICAL MODELING | 3.0 | ||
| MATH334 | INTRODUCTION TO PROBABILITY | 3.0 | 3.0 | |
| CSCIxxx | COMPUTING ELECTIVE1 | 3.0 | ||
| EBGN321 | ENGINEERING ECONOMICS | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH301 | INTRODUCTION TO ANALYSIS | 3.0 | ||
| MATH335 | INTRODUCTION TO MATHEMATICAL STATISTICS | 3.0 | 3.0 | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| CAS ELECTIVE | Culture and Society mid-level | 3.0 | 3.0 | |
| 15.0 | ||||
| Senior | ||||
| Fall | lec | lab | sem.hrs | |
| MATH436 | ADVANCED STATISTICAL MODELING | 3.0 | ||
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | ||
| MATH | MATHEMATICS-STAT ELECTIVE2 | 3.0 | ||
| CAS ELECTIVE | Culture and Society mid-level | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH437 | MULTIVARIATE ANALYSIS | 3.0 | ||
| MATH482 | STATISTICS PRACTICUM (CAPSTONE) (STAT Capstone) | 4.0 | 4.0 | |
| MATH | MATHEMATICS-STAT ELECTIVE | 3.0 | ||
| CAS ELECTIVE | Culture and Society 400-level | 3.0 | ||
| FREE | FREE ELECTIVE | 3.0 | 3.0 | |
| 16.0 | ||||
| Total Semester Hrs: 121.0 | ||||
| Mathematics-STAT Elective List. STAT students must choose at least 2 electives from this list. 2 | ||
| MATH432 | SPATIAL STATISTICS | 3.0 |
| MATH438 | STOCHASTIC MODELS | 3.0 |
| CSCI403 | DATA BASE MANAGEMENT | 3.0 |
| MATH531 | THEORY OF LINEAR MODELS | 3.0 |
| MATH534 | MATHEMATICAL STATISTICS I | 3.0 |
| MATH535 | MATHEMATICAL STATISTICS II | 3.0 |
| MATH | Department approval required for courses not on this list. | |
| Mathematics-CAM Elective List. STAT students may choose up to 4 electives from this list to satisfy AMS Elective requirements. 2 | ||
| MATH408 | COMPUTATIONAL METHODS FOR DIFFERENTIAL EQUATIONS | 3.0 |
| MATH431 | MATHEMATICAL BIOLOGY | 3.0 |
| MATH440 | PARALLEL SCIENTIFIC COMPUTING | 3.0 |
| MATH454 | COMPLEX ANALYSIS | 3.0 |
| MATH455 | PARTIAL DIFFERENTIAL EQUATIONS | 3.0 |
| MATH457 | INTEGRAL EQUATIONS | 3.0 |
| MATH458 | ABSTRACT ALGEBRA | 3.0 |
| MATH459 | ASYMPTOTICS | 3.0 |
| MATH472 | MATHEMATICAL AND COMPUTATIONAL NEUROSCIENCE | 3.0 |
| MATH484 | MATHEMATICAL AND COMPUTATIONAL MODELING (CAPSTONE) | 3.0 |
| MATH500 | LINEAR VECTOR SPACES | 3.0 |
| MATH501 | APPLIED ANALYSIS | 3.0 |
| MATH514 | APPLIED MATHEMATICS I | 3.0 |
| MATH515 | APPLIED MATHEMATICS II | 3.0 |
| MATH550 | NUMERICAL SOLUTION OF PARTIAL DIFFERENTIAL EQUATIONS | 3.0 |
| MATH551 | COMPUTATIONAL LINEAR ALGEBRA | 3.0 |
| CSCI303 | INTRODUCTION TO DATA SCIENCE | 3.0 |
| CSCI406 | ALGORITHMS | 3.0 |
| MATH | Department approval required for courses not on this list. | |
Data Science (DS) Emphasis
| Freshman | ||||
|---|---|---|---|---|
| Fall | lec | lab | sem.hrs | |
| MATH111 | CALCULUS FOR SCIENTISTS AND ENGINEERS I | 4.0 | 4.0 | |
| CSCI128 | COMPUTER SCIENCE FOR STEM | 3.0 | ||
| CHGN121 | PRINCIPLES OF CHEMISTRY I | 3.0 | 3.0 | 4.0 |
| HASS100 | NATURE AND HUMAN VALUES | 3.0 | 3.0 | |
| CSM101 | FRESHMAN SUCCESS SEMINAR | 1.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH112 | CALCULUS FOR SCIENTISTS AND ENGINEERS II | 4.0 | 4.0 | |
| MATH201 | INTRODUCTION TO STATISTICS | 3.0 | ||
| PHGN100 | PHYSICS I - MECHANICS | 3.0 | 3.0 | 4.0 |
| EDNS151 | CORNERSTONE - DESIGN I | 3.0 | ||
| S&W | SUCCESS AND WELLNESS | 1.0 | ||
| 15.0 | ||||
| Sophomore | ||||
| Fall | lec | lab | sem.hrs | |
| MATH213 | CALCULUS FOR SCIENTISTS AND ENGINEERS III | 4.0 | 4.0 | |
| PHGN200 | PHYSICS II-ELECTROMAGNETISM AND OPTICS | 3.0 | 3.0 | 4.0 |
| CSCI200 | FOUNDATIONAL PROGRAMMING CONCEPTS & DESIGN | 3.0 | ||
| CSM202 | INTRODUCTION TO STUDENT WELL-BEING AT MINES | 1.0 | ||
| HASS200 | GLOBAL STUDIES | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH225 | DIFFERENTIAL EQUATIONS | 3.0 | ||
| MATH300 | FOUNDATIONS OF ADVANCED MATHEMATICS | 3.0 | ||
| MATH324 | STATISTICAL MODELING | 3.0 | ||
| MATH332 | LINEAR ALGEBRA or 342 | 3.0 | 3.0 | |
| FREE | FREE ELECTIVE | 3.0 | ||
| 15.0 | ||||
| Junior | ||||
| Fall | lec | lab | sem.hrs | |
| MATH307 | INTRODUCTION TO SCIENTIFIC COMPUTING | 3.0 | 3.0 | |
| MATH310 | INTRODUCTION TO MATHEMATICAL MODELING | 3.0 | ||
| MATH334 | INTRODUCTION TO PROBABILITY | 3.0 | 3.0 | |
| CSCIxxx | COMPUTING ELECTIVE1 | 3.0 | ||
| EBGN321 | ENGINEERING ECONOMICS | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH301 | INTRODUCTION TO ANALYSIS | 3.0 | ||
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| CSCI303 | INTRODUCTION TO DATA SCIENCE | 3.0 | ||
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| CAS ELECTIVE | Culture and Society mid-level | 3.0 | 3.0 | |
| 15.0 | ||||
| Senior | ||||
| Fall | lec | lab | sem.hrs | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | ||
| CAS ELECTIVE | Culture and Society mid-level | 3.0 | ||
| CSCI470 | INTRODUCTION TO MACHINE LEARNING | 3.0 | ||
| MATH335 | INTRODUCTION TO MATHEMATICAL STATISTICS | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH482 | STATISTICS PRACTICUM (CAPSTONE) (STAT Capstone) | 4.0 | 4.0 | |
| MATH | MATHEMATICS-DS ELECTIVE2 | 3.0 | ||
| MATH | MATHEMATICS-DS ELECTIVE2 | 3.0 | ||
| CAS ELECTIVE | Culture and Society 400-level | 3.0 | ||
| FREE | FREE ELECTIVE | 3.0 | 3.0 | |
| 16.0 | ||||
| Total Semester Hrs: 121.0 | ||||
| Mathematics-DS elective list. DS students must choose at least 2 electives from ths list 2 | ||
| MATH432 | SPATIAL STATISTICS | 3.0 |
| MATH436 | ADVANCED STATISTICAL MODELING | 3.0 |
| MATH437 | MULTIVARIATE ANALYSIS | 3.0 |
| MATH438 | STOCHASTIC MODELS | 3.0 |
| MATH440 | PARALLEL SCIENTIFIC COMPUTING | 3.0 |
| MATH560 | INTRODUCTION TO KEY STATISTICAL LEARNING METHODS I | 3.0 |
| MATH561 | INTRODUCTION TO KEY STATISTICAL LEARNING METHODS II | 3.0 |
| CSCI403 | DATA BASE MANAGEMENT | 3.0 |
| CSCI404 | ARTIFICIAL INTELLIGENCE | 3.0 |
| CSCI406 | ALGORITHMS | 3.0 |
| CSCI423 | COMPUTER SIMULATION | 3.0 |
| CSCI475 | INFORMATION SECURITY AND PRIVACY | 3.0 |
| MATH | Department approval required for courses not on this list. | |
| AMS Elective List. DS students may choose up to 4 electives from this list to satisfy AMS elective requirements 3 | ||
| MATH408 | COMPUTATIONAL METHODS FOR DIFFERENTIAL EQUATIONS | 3.0 |
| MATH431 | MATHEMATICAL BIOLOGY | 3.0 |
| MATH454 | COMPLEX ANALYSIS | 3.0 |
| MATH455 | PARTIAL DIFFERENTIAL EQUATIONS | 3.0 |
| MATH457 | INTEGRAL EQUATIONS | 3.0 |
| MATH458 | ABSTRACT ALGEBRA | 3.0 |
| MATH459 | ASYMPTOTICS | 3.0 |
| MATH472 | MATHEMATICAL AND COMPUTATIONAL NEUROSCIENCE | 3.0 |
| MATH484 | MATHEMATICAL AND COMPUTATIONAL MODELING (CAPSTONE) | 3.0 |
| MATH500 | LINEAR VECTOR SPACES | 3.0 |
| MATH501 | APPLIED ANALYSIS | 3.0 |
| MATH514 | APPLIED MATHEMATICS I | 3.0 |
| MATH515 | APPLIED MATHEMATICS II | 3.0 |
| MATH550 | NUMERICAL SOLUTION OF PARTIAL DIFFERENTIAL EQUATIONS | 3.0 |
| MATH551 | COMPUTATIONAL LINEAR ALGEBRA | 3.0 |
| MATH | Department approval required for courses not on this list. | |
Major GPA
During the 2016-2017 academic year, the Undergraduate Council considered the policy concerning required major GPAs and which courses are included in each degree’s GPA. While the GPA policy has not been officially updated in order to provide transparency, council members agreed that publishing the courses included in each degree’s GPA is beneficial to students.
The following list details the courses that are included in the GPA for this degree:
- CSCI100 through CSCI799 inclusive
- MACS100 through MACS799 inclusive (Previous subject code)
- MATH100 through MATH799 inclusive
Degree Requirements (Applied Mathematics and Statistics)
Computational and Applied Mathematics (CAM) EMPHASIS
| Freshman | ||||
|---|---|---|---|---|
| Fall | lec | lab | sem.hrs | |
| MATH111 | CALCULUS FOR SCIENTISTS AND ENGINEERS I | 4.0 | 4.0 | |
| CSCI128 | COMPUTER SCIENCE FOR STEM | 3.0 | ||
| CHGN121 | PRINCIPLES OF CHEMISTRY I | 4.0 | ||
| CSM101 | FRESHMAN SUCCESS SEMINAR | 1.0 | ||
| HASS100 | NATURE AND HUMAN VALUES | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH112 | CALCULUS FOR SCIENTISTS AND ENGINEERS II | 4.0 | 4.0 | |
| MATH201 | INTRODUCTION TO STATISTICS | 3.0 | ||
| PHGN100 | PHYSICS I - MECHANICS | 4.0 | ||
| EDNS151 | CORNERSTONE - DESIGN I | 3.0 | ||
| S&W | SUCCESS AND WELLNESS | 1.0 | ||
| 15.0 | ||||
| Sophomore | ||||
| Fall | lec | lab | sem.hrs | |
| MATH213 | CALCULUS FOR SCIENTISTS AND ENGINEERS III | 4.0 | 4.0 | |
| PHGN200 | PHYSICS II-ELECTROMAGNETISM AND OPTICS | 4.0 | ||
| CSCI200 | FOUNDATIONAL PROGRAMMING CONCEPTS & DESIGN | 3.0 | ||
| CSM202 | INTRODUCTION TO STUDENT WELL-BEING AT MINES | 1.0 | ||
| HASS200 | GLOBAL STUDIES | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH225 | DIFFERENTIAL EQUATIONS | 3.0 | ||
| MATH300 | FOUNDATIONS OF ADVANCED MATHEMATICS | 3.0 | ||
| MATH324 | STATISTICAL MODELING | 3.0 | ||
| MATH332 | LINEAR ALGEBRA or 342 | 3.0 | 3.0 | |
| FREE | FREE ELECTIVE | 3.0 | ||
| 15.0 | ||||
| Junior | ||||
| Fall | lec | lab | sem.hrs | |
| MATH307 | INTRODUCTION TO SCIENTIFIC COMPUTING | 3.0 | 3.0 | |
| MATH310 | INTRODUCTION TO MATHEMATICAL MODELING | 3.0 | ||
| MATH334 | INTRODUCTION TO PROBABILITY | 3.0 | 3.0 | |
| CSCIxxx | COMPUTING ELECTIVE1 | 3.0 | ||
| EBGN321 | ENGINEERING ECONOMICS | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH301 | INTRODUCTION TO ANALYSIS | 3.0 | 3.0 | |
| MATH455 | PARTIAL DIFFERENTIAL EQUATIONS | 3.0 | ||
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | ||
| CAS Elective | Culture and Society mid-level | 3.0 | ||
| 15.0 | ||||
| Senior | ||||
| Fall | lec | lab | sem.hrs | |
| MATH408 | COMPUTATIONAL METHODS FOR DIFFERENTIAL EQUATIONS | 3.0 | 3.0 | |
| MATH431 | MATHEMATICAL BIOLOGY | 3.0 | ||
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| CAS ELECTIVE | Culture and Society mid-level | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH484 | MATHEMATICAL AND COMPUTATIONAL MODELING (CAPSTONE) | 4.0 | 4.0 | |
| MATH | MATHEMATICS-CAM ELECTIVE2 | 3.0 | 3.0 | |
| MATH | MATHEMATICS-CAM ELECTIVE2 | 3.0 | 3.0 | |
| FREE | FREE ELECTIVE | 3.0 | 3.0 | |
| CAS Elective | Culture and Society 400-level | 3.0 | ||
| 16.0 | ||||
| Total Semester Hrs: 121.0 | ||||
| Mathematics-CAM elective list. CAM students must choose at least 2 electives from this list. 2 | ||
| MATH440 | PARALLEL SCIENTIFIC COMPUTING | 3.0 |
| MATH454 | COMPLEX ANALYSIS | 3.0 |
| MATH457 | INTEGRAL EQUATIONS | 3.0 |
| MATH458 | ABSTRACT ALGEBRA | 3.0 |
| MATH459 | ASYMPTOTICS | 3.0 |
| MATH472 | MATHEMATICAL AND COMPUTATIONAL NEUROSCIENCE | 3.0 |
| MATH500 | LINEAR VECTOR SPACES | 3.0 |
| MATH501 | APPLIED ANALYSIS | 3.0 |
| MATH514 | APPLIED MATHEMATICS I | 3.0 |
| MATH515 | APPLIED MATHEMATICS II | 3.0 |
| MATH550 | NUMERICAL SOLUTION OF PARTIAL DIFFERENTIAL EQUATIONS | 3.0 |
| MATH551 | COMPUTATIONAL LINEAR ALGEBRA | 3.0 |
| MATH | Department approval required for courses not on this list. | |
| AMS Elective List. CAM students may choose up to 4 electives from this list to satisfy AMS Elective requirements. 2 | ||
| MATH335 | INTRODUCTION TO MATHEMATICAL STATISTICS | 3.0 |
| MATH432 | SPATIAL STATISTICS | 3.0 |
| MATH436 | ADVANCED STATISTICAL MODELING | 3.0 |
| MATH437 | MULTIVARIATE ANALYSIS | 3.0 |
| MATH438 | STOCHASTIC MODELS | 3.0 |
| MATH439 | SURVIVAL ANALYSIS | 3.0 |
| MATH482 | STATISTICS PRACTICUM (CAPSTONE) | 3.0 |
| MATH531 | THEORY OF LINEAR MODELS | 3.0 |
| MATH534 | MATHEMATICAL STATISTICS I | 3.0 |
| MATH535 | MATHEMATICAL STATISTICS II | 3.0 |
| CSCI303 | INTRODUCTION TO DATA SCIENCE | 3.0 |
| CSCI403 | DATA BASE MANAGEMENT | 3.0 |
| CSCI406 | ALGORITHMS | 3.0 |
| MATH | Department approval required for courses not on this list. | |
Statistics (STATS) EMPHASIS
| Freshman | ||||
|---|---|---|---|---|
| Fall | lec | lab | sem.hrs | |
| MATH111 | CALCULUS FOR SCIENTISTS AND ENGINEERS I | 4.0 | 4.0 | |
| CSCI128 | COMPUTER SCIENCE FOR STEM | 3.0 | ||
| CHGN121 | PRINCIPLES OF CHEMISTRY I | 3.0 | 3.0 | 4.0 |
| HASS100 | NATURE AND HUMAN VALUES | 3.0 | 3.0 | |
| CSM101 | FRESHMAN SUCCESS SEMINAR | 1.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH112 | CALCULUS FOR SCIENTISTS AND ENGINEERS II | 4.0 | 4.0 | |
| MATH201 | INTRODUCTION TO STATISTICS | 3.0 | ||
| PHGN100 | PHYSICS I - MECHANICS | 3.0 | 3.0 | 4.0 |
| EDNS151 | CORNERSTONE - DESIGN I | 3.0 | ||
| S&W | SUCCESS AND WELLNESS | 1.0 | ||
| 15.0 | ||||
| Sophomore | ||||
| Fall | lec | lab | sem.hrs | |
| MATH213 | CALCULUS FOR SCIENTISTS AND ENGINEERS III | 4.0 | 4.0 | |
| PHGN200 | PHYSICS II-ELECTROMAGNETISM AND OPTICS | 3.0 | 3.0 | 4.0 |
| CSCI200 | FOUNDATIONAL PROGRAMMING CONCEPTS & DESIGN | 3.0 | ||
| CSM202 | INTRODUCTION TO STUDENT WELL-BEING AT MINES | 1.0 | ||
| HASS200 | GLOBAL STUDIES | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH225 | DIFFERENTIAL EQUATIONS | 3.0 | ||
| MATH300 | FOUNDATIONS OF ADVANCED MATHEMATICS | 3.0 | ||
| MATH324 | STATISTICAL MODELING | 3.0 | ||
| MATH332 | LINEAR ALGEBRA or 342 | 3.0 | 3.0 | |
| FREE | FREE ELECTIVE | 3.0 | ||
| 15.0 | ||||
| Junior | ||||
| Fall | lec | lab | sem.hrs | |
| MATH307 | INTRODUCTION TO SCIENTIFIC COMPUTING | 3.0 | 3.0 | |
| MATH310 | INTRODUCTION TO MATHEMATICAL MODELING | 3.0 | ||
| MATH334 | INTRODUCTION TO PROBABILITY | 3.0 | 3.0 | |
| CSCIxxx | COMPUTING ELECTIVE1 | 3.0 | ||
| EBGN321 | ENGINEERING ECONOMICS | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH301 | INTRODUCTION TO ANALYSIS | 3.0 | ||
| MATH335 | INTRODUCTION TO MATHEMATICAL STATISTICS | 3.0 | 3.0 | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| CAS ELECTIVE | Culture and Society mid-level | 3.0 | 3.0 | |
| 15.0 | ||||
| Senior | ||||
| Fall | lec | lab | sem.hrs | |
| MATH436 | ADVANCED STATISTICAL MODELING | 3.0 | ||
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | ||
| MATH | MATHEMATICS-STAT ELECTIVE2 | 3.0 | ||
| CAS ELECTIVE | Culture and Society mid-level | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH437 | MULTIVARIATE ANALYSIS | 3.0 | ||
| MATH482 | STATISTICS PRACTICUM (CAPSTONE) (STAT Capstone) | 4.0 | 4.0 | |
| MATH | MATHEMATICS-STAT ELECTIVE | 3.0 | ||
| CAS ELECTIVE | Culture and Society 400-level | 3.0 | ||
| FREE | FREE ELECTIVE | 3.0 | 3.0 | |
| 16.0 | ||||
| Total Semester Hrs: 121.0 | ||||
| Mathematics-STAT Elective List. STAT students must choose at least 2 electives from this list. 2 | ||
| MATH432 | SPATIAL STATISTICS | 3.0 |
| MATH438 | STOCHASTIC MODELS | 3.0 |
| CSCI403 | DATA BASE MANAGEMENT | 3.0 |
| MATH531 | THEORY OF LINEAR MODELS | 3.0 |
| MATH534 | MATHEMATICAL STATISTICS I | 3.0 |
| MATH535 | MATHEMATICAL STATISTICS II | 3.0 |
| MATH | Department approval required for courses not on this list. | |
| Mathematics-CAM Elective List. STAT students may choose up to 4 electives from this list to satisfy AMS Elective requirements. 2 | ||
| MATH408 | COMPUTATIONAL METHODS FOR DIFFERENTIAL EQUATIONS | 3.0 |
| MATH431 | MATHEMATICAL BIOLOGY | 3.0 |
| MATH440 | PARALLEL SCIENTIFIC COMPUTING | 3.0 |
| MATH454 | COMPLEX ANALYSIS | 3.0 |
| MATH455 | PARTIAL DIFFERENTIAL EQUATIONS | 3.0 |
| MATH457 | INTEGRAL EQUATIONS | 3.0 |
| MATH458 | ABSTRACT ALGEBRA | 3.0 |
| MATH459 | ASYMPTOTICS | 3.0 |
| MATH472 | MATHEMATICAL AND COMPUTATIONAL NEUROSCIENCE | 3.0 |
| MATH484 | MATHEMATICAL AND COMPUTATIONAL MODELING (CAPSTONE) | 3.0 |
| MATH500 | LINEAR VECTOR SPACES | 3.0 |
| MATH501 | APPLIED ANALYSIS | 3.0 |
| MATH514 | APPLIED MATHEMATICS I | 3.0 |
| MATH515 | APPLIED MATHEMATICS II | 3.0 |
| MATH550 | NUMERICAL SOLUTION OF PARTIAL DIFFERENTIAL EQUATIONS | 3.0 |
| MATH551 | COMPUTATIONAL LINEAR ALGEBRA | 3.0 |
| CSCI303 | INTRODUCTION TO DATA SCIENCE | 3.0 |
| CSCI406 | ALGORITHMS | 3.0 |
| MATH | Department approval required for courses not on this list. | |
Data Science (DS) Emphasis
| Freshman | ||||
|---|---|---|---|---|
| Fall | lec | lab | sem.hrs | |
| MATH111 | CALCULUS FOR SCIENTISTS AND ENGINEERS I | 4.0 | 4.0 | |
| CSCI128 | COMPUTER SCIENCE FOR STEM | 3.0 | ||
| CHGN121 | PRINCIPLES OF CHEMISTRY I | 3.0 | 3.0 | 4.0 |
| HASS100 | NATURE AND HUMAN VALUES | 3.0 | 3.0 | |
| CSM101 | FRESHMAN SUCCESS SEMINAR | 1.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH112 | CALCULUS FOR SCIENTISTS AND ENGINEERS II | 4.0 | 4.0 | |
| MATH201 | INTRODUCTION TO STATISTICS | 3.0 | ||
| PHGN100 | PHYSICS I - MECHANICS | 3.0 | 3.0 | 4.0 |
| EDNS151 | CORNERSTONE - DESIGN I | 3.0 | ||
| S&W | SUCCESS AND WELLNESS | 1.0 | ||
| 15.0 | ||||
| Sophomore | ||||
| Fall | lec | lab | sem.hrs | |
| MATH213 | CALCULUS FOR SCIENTISTS AND ENGINEERS III | 4.0 | 4.0 | |
| PHGN200 | PHYSICS II-ELECTROMAGNETISM AND OPTICS | 3.0 | 3.0 | 4.0 |
| CSCI200 | FOUNDATIONAL PROGRAMMING CONCEPTS & DESIGN | 3.0 | ||
| CSM202 | INTRODUCTION TO STUDENT WELL-BEING AT MINES | 1.0 | ||
| HASS200 | GLOBAL STUDIES | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH225 | DIFFERENTIAL EQUATIONS | 3.0 | ||
| MATH300 | FOUNDATIONS OF ADVANCED MATHEMATICS | 3.0 | ||
| MATH324 | STATISTICAL MODELING | 3.0 | ||
| MATH332 | LINEAR ALGEBRA or 342 | 3.0 | 3.0 | |
| FREE | FREE ELECTIVE | 3.0 | ||
| 15.0 | ||||
| Junior | ||||
| Fall | lec | lab | sem.hrs | |
| MATH307 | INTRODUCTION TO SCIENTIFIC COMPUTING | 3.0 | 3.0 | |
| MATH310 | INTRODUCTION TO MATHEMATICAL MODELING | 3.0 | ||
| MATH334 | INTRODUCTION TO PROBABILITY | 3.0 | 3.0 | |
| CSCIxxx | COMPUTING ELECTIVE1 | 3.0 | ||
| EBGN321 | ENGINEERING ECONOMICS | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH301 | INTRODUCTION TO ANALYSIS | 3.0 | ||
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| CSCI303 | INTRODUCTION TO DATA SCIENCE | 3.0 | ||
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| CAS ELECTIVE | Culture and Society mid-level | 3.0 | 3.0 | |
| 15.0 | ||||
| Senior | ||||
| Fall | lec | lab | sem.hrs | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | 3.0 | |
| MATH | MATHEMATICS-AMS ELECTIVE2 | 3.0 | ||
| CAS ELECTIVE | Culture and Society mid-level | 3.0 | ||
| CSCI470 | INTRODUCTION TO MACHINE LEARNING | 3.0 | ||
| MATH335 | INTRODUCTION TO MATHEMATICAL STATISTICS | 3.0 | ||
| 15.0 | ||||
| Spring | lec | lab | sem.hrs | |
| MATH482 | STATISTICS PRACTICUM (CAPSTONE) (STAT Capstone) | 4.0 | 4.0 | |
| MATH | MATHEMATICS-DS ELECTIVE2 | 3.0 | ||
| MATH | MATHEMATICS-DS ELECTIVE2 | 3.0 | ||
| CAS ELECTIVE | Culture and Society 400-level | 3.0 | ||
| FREE | FREE ELECTIVE | 3.0 | 3.0 | |
| 16.0 | ||||
| Total Semester Hrs: 121.0 | ||||
| Mathematics-DS elective list. DS students must choose at least 2 electives from ths list 2 | ||
| MATH432 | SPATIAL STATISTICS | 3.0 |
| MATH436 | ADVANCED STATISTICAL MODELING | 3.0 |
| MATH437 | MULTIVARIATE ANALYSIS | 3.0 |
| MATH438 | STOCHASTIC MODELS | 3.0 |
| MATH440 | PARALLEL SCIENTIFIC COMPUTING | 3.0 |
| MATH560 | INTRODUCTION TO KEY STATISTICAL LEARNING METHODS I | 3.0 |
| MATH561 | INTRODUCTION TO KEY STATISTICAL LEARNING METHODS II | 3.0 |
| CSCI403 | DATA BASE MANAGEMENT | 3.0 |
| CSCI404 | ARTIFICIAL INTELLIGENCE | 3.0 |
| CSCI406 | ALGORITHMS | 3.0 |
| CSCI423 | COMPUTER SIMULATION | 3.0 |
| CSCI475 | INFORMATION SECURITY AND PRIVACY | 3.0 |
| MATH | Department approval required for courses not on this list. | |
| AMS Elective List. DS students may choose up to 4 electives from this list to satisfy AMS elective requirements 3 | ||
| MATH408 | COMPUTATIONAL METHODS FOR DIFFERENTIAL EQUATIONS | 3.0 |
| MATH431 | MATHEMATICAL BIOLOGY | 3.0 |
| MATH454 | COMPLEX ANALYSIS | 3.0 |
| MATH455 | PARTIAL DIFFERENTIAL EQUATIONS | 3.0 |
| MATH457 | INTEGRAL EQUATIONS | 3.0 |
| MATH458 | ABSTRACT ALGEBRA | 3.0 |
| MATH459 | ASYMPTOTICS | 3.0 |
| MATH472 | MATHEMATICAL AND COMPUTATIONAL NEUROSCIENCE | 3.0 |
| MATH484 | MATHEMATICAL AND COMPUTATIONAL MODELING (CAPSTONE) | 3.0 |
| MATH500 | LINEAR VECTOR SPACES | 3.0 |
| MATH501 | APPLIED ANALYSIS | 3.0 |
| MATH514 | APPLIED MATHEMATICS I | 3.0 |
| MATH515 | APPLIED MATHEMATICS II | 3.0 |
| MATH550 | NUMERICAL SOLUTION OF PARTIAL DIFFERENTIAL EQUATIONS | 3.0 |
| MATH551 | COMPUTATIONAL LINEAR ALGEBRA | 3.0 |
| MATH | Department approval required for courses not on this list. | |
Major GPA
During the 2016-2017 academic year, the Undergraduate Council considered the policy concerning required major GPAs and which courses are included in each degree’s GPA. While the GPA policy has not been officially updated in order to provide transparency, council members agreed that publishing the courses included in each degree’s GPA is beneficial to students.
The following list details the courses that are included in the GPA for this degree:
- CSCI100 through CSCI799 inclusive
- MACS100 through MACS799 inclusive (Previous subject code)
- MATH100 through MATH799 inclusive
Overview
The Mines guidelines for Minor/ASI can be found in the Undergraduate Information section of the Mines Catalog. The Department of Applied Mathematics and Statistics offers the following:
Minors are available in:
| Applied Mathematics and Statistics (AMS) | ||
| Required Courses | ||
| MATH201 | INTRODUCTION TO STATISTICS | 3.0 |
| MATH225 | DIFFERENTIAL EQUATIONS | 3.0 |
| or MATH235 | DIFFERENTIAL EQUATIONS HONORS | |
| MATH332 | LINEAR ALGEBRA | 3.0 |
| or MATH342 | HONORS LINEAR ALGEBRA | |
| Plus 9 credits from any MATHxxx courses, at least one of which at the 400-level or above. Recall that at most 9 credits can simultaneously satisfy both a minor requirement and another program (major or minor) requirement. | ||
| To complete an AMS Minor, students must choose 9 credits from MATHxxx courses. The following courses from other departments will also be accepted: | ||
| CSCI303 | INTRODUCTION TO DATA SCIENCE | 3.0 |
| CSCI406 | ALGORITHMS | 3.0 |
| CSCI441 | COMPUTER GRAPHICS | 3.0 |
| CSCI444 | ADVANCED COMPUTER GRAPHICS | 3.0 |
| CSCI470 | INTRODUCTION TO MACHINE LEARNING | 3.0 |
| CSCI474 | INTRODUCTION TO CRYPTOGRAPHY | 3.0 |
Courses
MATH100. INTRODUCTORY TOPICS FOR CALCULUS. 3.0 Semester Hrs.
(S) An introduction and/or review of topics which are essential to the background of an undergraduate student at CSM. This course serves as a preparatory course for the Calculus curriculum and includes material from Algebra, Trigonometry, Mathematical Analysis, and Calculus. Topics include basic algebra and equation solving, solutions of inequalities, trigonometric functions and identities, functions of a single variable, continuity, and limits of functions. Does not apply toward undergraduate degree or GPA. 3 hours lecture; 3 semester hours.
MATH111. CALCULUS FOR SCIENTISTS AND ENGINEERS I. 4.0 Semester Hrs.
(I, II, S) First course in the calculus sequence, including elements of plane geometry. Functions, limits, continuity, derivatives and their application. Definite and indefinite integrals; Prerequisite: precalculus. 4 hours lecture; 4 semester hours. Approved for Colorado Guaranteed General Education transfer. Equivalency for GT-MA1.
MATH112. CALCULUS FOR SCIENTISTS AND ENGINEERS II. 4.0 Semester Hrs.
Equivalent with MATH122,
Vectors, applications and techniques of integration, infinite series, and an introduction to multivariate functions and surfaces. 4 hours lecture; 4 semester hours. Approved for Colorado Guaranteed General Education transfer. Equivalency for GT-MA1. Prerequisite: Grade of C- or better in MATH111.
MATH122. CALCULUS FOR SCIENTISTS AND ENGINEERS II HONORS. 4.0 Semester Hrs.
Equivalent with MATH112,
Same topics as those covered in MATH112 but with additional material and problems. Prerequisite: Grade of C- or better in MATH111.
MATH198. SPECIAL TOPICS. 6.0 Semester Hrs.
(I, II) Pilot course or special topics course. Topics chosen from special interests of instructor(s) and student(s). Usually the course is offered only once. Prerequisite: none. Variable credit; 1 to 6 credit hours. Repeatable for credit under different titles.
MATH199. INDEPENDENT STUDY. 1-6 Semester Hr.
(I, II) Individual research or special problem projects supervised by a faculty member, also, when a student and instructor agree on a subject matter, content, and credit hours. Prerequisite: ?Independent Study? form must be completed and submitted to the Registrar. Variable credit; 1 to 6 credit hours. Repeatable for credit.
MATH201. INTRODUCTION TO STATISTICS. 3.0 Semester Hrs.
Equivalent with MATH323,
This course is an introduction to Statistics, including fundamentals of experimental design and data collection, the summary and display of data, propagation of error, interval estimation, hypothesis testing, and linear regression with emphasis on applications to science and engineering. Prerequisite: MATH111.
View Course Learning Outcomes
- Choose appropriate descriptive statistics and graphical displays to summarize a data set.
- Distinguish between commonly used random variables and sampling distributions in order to identify the appropriate statistical tools based on the context of a given problem.
- Identify, formulate, and evaluate appropriate tools for statistical inference based on the context of a given problem.
- Disseminate/Communicate statistical analysis.
MATH213. CALCULUS FOR SCIENTISTS AND ENGINEERS III. 4.0 Semester Hrs.
Multivariable calculus, including partial derivatives, multiple integrals, and vector calculus. 4 hours lecture; 4 semester hours. Approved for Colorado Guaranteed General Education transfer. Equivalency for GT-MA1. Prerequisites: Grade of C- or better in MATH112 or MATH122. Corequisites: CSCI128 or CSCI102.
MATH223. CALCULUS FOR SCIENTISTS AND ENGINEERS III HONORS. 4.0 Semester Hrs.
Same topics as those covered in MATH213 but with additional material and problems. Prerequisite: MATH112 with a grade of B- or higher, MATH112 with a grade of B- or higher.
MATH225. DIFFERENTIAL EQUATIONS. 3.0 Semester Hrs.
Classical techniques for first and higher order equations and systems of equations. Laplace transforms. Phase-plane and stability analysis of non-linear equations and systems. Applications from physics, mechanics, electrical engineering, and environmental sciences. Prerequisites: Grade of C- or better in MATH112 or MATH122. Corequisites: CSCI128 or CSCI102. 3 hours lecture; 3 semester hours.
MATH235. DIFFERENTIAL EQUATIONS HONORS. 3.0 Semester Hrs.
Same topics as those covered in MATH225 but with additional material and problems. 3 hours lecture; 3 semester hours. Prerequisite: Grade of B- or better in MATH112 or MATH 113 or MATH122.
MATH298. SPECIAL TOPICS. 1-6 Semester Hr.
(I, II) Pilot course or special topics course. Topics chosen from special interests of instructor(s) and student(s). Usually the course is offered only once. Prerequisite: none. Variable credit; 1 to 6 credit hours. Repeatable for credit under different titles.
MATH299. INDEPENDENT STUDY. 1-6 Semester Hr.
(I, II) Individual research or special problem projects supervised by a faculty member, also, when a student and instructor agree on a subject matter, content, and credit hours. Prerequisite: ?Independent Study? form must be completed and submitted to the Registrar. Variable credit; 1 to 6 credit hours. Repeatable for credit.
MATH300. FOUNDATIONS OF ADVANCED MATHEMATICS. 3.0 Semester Hrs.
(WI) This course is an introduction to communication in mathematics. This writing intensive course provides a transition from the Calculus sequence to theoretical mathematics curriculum in CSM. Topics include logic and recursion, techniques of mathematical proofs, reading and writing proofs. 3 hours lecture; 3 semester hours. Prerequisite: MATH112 or MATH122.
View Course Learning Outcomes
- Apply the rules of logic in order to construct proofs. In particular, students should be able to work symbolically with connectives and quantifiers to produce logically valid, correct and clear arguments.
- Apply abstract definitions and previous results (from areas such as set theory, discrete mathematics, introductory analysis, or introductory abstract algebra) as well as create intuition-forming examples or counterexamples in order to prove or disprove a conjecture.
- Construct direct and indirect proofs and proofs by induction and determine the appropriateness of each type in a particular setting (such as set theory, discrete mathematics, introductory analysis, or introductory abstract algebra).
- Write solutions to problems and proofs of theorems that meet rigorous standards based on content, organization and coherence, argument and support, and style and mechanics.
- Analyze and critique proofs with respect to logic and correctness.
MATH301. INTRODUCTION TO ANALYSIS. 3.0 Semester Hrs.
Equivalent with MATH401,
This course is a first course in real analysis that lays out the context and motivation of analysis in terms of the transition from power series to those less predictable series. The course is taught from a historical perspective. It covers an introduction to the real numbers, sequences and series and their convergence, real-valued functions and their continuity and differentiability, sequences of functions and their pointwise and uniform convergence, and Riemann-Stieltjes integration theory. 3 hours lecture; 3 semester hours. Prerequisite: MATH300.
MATH307. INTRODUCTION TO SCIENTIFIC COMPUTING. 3.0 Semester Hrs.
Equivalent with CSCI407,MATH407,
This course is designed to introduce scientific computing to scientists and engineers. Students in this course will be taught various numerical methods and programming techniques to solve basic scientific problems. Emphasis will be made on implementation of various numerical and approximation methods to efficiently simulate several applied mathematical models. 3 hours lecture; 3 semester hours. Prerequisite: MATH213 or MATH223; CSCI102 or CSCI128 or CSCI200. Co-requisite: MATH225 or MATH235.
MATH310. INTRODUCTION TO MATHEMATICAL MODELING. 3.0 Semester Hrs.
An introduction to modeling and communication in mathematics. A writing intensive course providing a transition from the core math sequence to the upper division AMS curriculum. Topics include a variety of mathematical and statistical modeling techniques. Students will formulate and solve applied problems and will present results orally and in writing. In addition, students will be introduced to the mathematics software that will be used in upper division courses. Prerequisite: MATH201, MATH213, MATH225; CSCI128.
View Course Learning Outcomes
- Formulate and investigate mathematical and statistical models
- Identify mulitple types of models and techniques
- Communicate the results of a modeling study in writing and orally
MATH324. STATISTICAL MODELING. 3.0 Semester Hrs.
Linear regression, analysis of variance, and design of experiments, focusing on the construction of models and evaluation of their fit. Techniques covered will include stepwise and best subsets regression, variable transformations, and residual analysis. Emphasis will be placed on the analysis of data with statistical software. Prerequisite: MATH201.
MATH332. LINEAR ALGEBRA. 3.0 Semester Hrs.
Systems of linear equations, matrices, determinants and eigenvalues. Linear operators. Abstract vector spaces. Applications selected from linear programming, physics, graph theory, and other fields. Prerequisite: CSCI128; MATH112, MATH122, or PHGN100.
MATH334. INTRODUCTION TO PROBABILITY. 3.0 Semester Hrs.
An introduction to the theory of probability essential for problems in science and engineering. Topics include axioms of probability, combinatorics, conditional probability and independence, discrete and continuous probability density functions, expectation, jointly distributed random variables, Central Limit Theorem, laws of large numbers. 3 hours lecture, 3 semester hours. Prerequisite: CSCI128 or CSCI102; MATH213, MATH223.
MATH335. INTRODUCTION TO MATHEMATICAL STATISTICS. 3.0 Semester Hrs.
An introduction to the theory of statistics essential for problems in science and engineering. Topics include sampling distributions, methods of point estimation, methods of interval estimation, significance testing for population means and variances and goodness of fit, linear regression, analysis of variance. 3 hours lecture, 3 semester hours. Prerequisite: MATH334.
MATH342. HONORS LINEAR ALGEBRA. 3.0 Semester Hrs.
Same topics as those covered in MATH332 but with additional material and problems as well as a more rigorous presentation. 3 hours lecture; 3 semester hours. Prerequisite: MATH213, MATH223.
MATH398. SPECIAL TOPICS. 6.0 Semester Hrs.
(I, II) Pilot course or special topics course. Topics chosen from special interests of instructor(s) and student(s). Usually the course is offered only once. Prerequisite: none. Variable credit; 1 to 6 credit hours. Repeatable for credit under different titles.
MATH399. INDEPENDENT STUDY. 0.5-6 Semester Hr.
(I, II) Individual research or special problem projects supervised by a faculty member, also, when a student and instructor agree on a subject matter, content, and credit hours. Prerequisite: ?Independent Study? form must be completed and submitted to the Registrar. Variable credit; 1 to 6 credit hours. Repeatable for credit.
MATH408. COMPUTATIONAL METHODS FOR DIFFERENTIAL EQUATIONS. 3.0 Semester Hrs.
This course is designed to introduce computational methods to scientists and engineers for developing differential equations based computer models. Students in this course will be taught various numerical methods and programming techniques to simulate systems of nonlinear ordinary differential equations. Emphasis will be on implementation of various numerical and approximation methods to efficiently simulate several systems of nonlinear differential equations. Prerequisite: MATH307. 3 hours lecture, 3 semester hours.
MATH431. MATHEMATICAL BIOLOGY. 3.0 Semester Hrs.
This course will discuss methods for building and solving both continuous and discrete mathematical models. These methods will be applied to population dynamics, epidemic spread, pharmacokinetics and modeling of physiologic systems. Modern Control Theory will be introduced and used to model living systems. Some concepts related to self-organizing systems will be introduced. Prerequisite: MATH307, MATH310 or BIOL300, and MATH332 or MATH342.
View Course Learning Outcomes
- Describe the assumptions and implementations of some of the classical models of mathematical biology in your own words.
- Derive models for biological phenomena including both discrete and continuous classes of models.
- Solve models using both analytical and numerical techniques.
- Apply techniques for model analysis and interpret results.
- Generate and professionally communicate novel results in mathematical biology.
MATH432. SPATIAL STATISTICS. 3.0 Semester Hrs.
Modeling and analysis of data observed in a 2- or 3-dimensional region. Random fields, variograms, covariances, stationarity, nonstationarity, kriging, simulation, Bayesian hierarchical models, spatial regression, SAR, CAR, QAR, and MA models, Geary/Moran indices, point processes, K-function, complete spatial randomness, homogeneous and inhomogeneous processes, marked point processes. Prerequisite: MATH324, MATH332, MATH335.
MATH433. TIME SERIES AND ITS APPLICATIONS. 3.0 Semester Hrs.
Equivalent with BELS331,BELS433,MACS433,MATH331,
Exploratory Analysis of Time Series, Stationary Time Series, Autocorrelation and Partial Autocorrelation, Autoregressive Moving Average (ARMA) Models, Forecasting, Estimation, ARIMA Models for Nonstationary Data, Multiplicative Seasonal ARIMA Models, The Spectral Density, Periodogram and Discrete Fourier Transform, Spectral Estimation, Multiple Series and Cross-Spectra, Linear Filters, Long Memory ARMA and Fractional Differencing, GARCH Models, Threshold Models, Regression with Autocorrelated Errors, Lagged Regression, Multivariate ARMAX Models. Prerequisite: MATH324, MATH335.
View Course Learning Outcomes
- This course is designed to be useful for students facing the analysis of time-correlated data in the physical, biological, and social sciences.
MATH436. ADVANCED STATISTICAL MODELING. 3.0 Semester Hrs.
Modern methods for constructing and evaluating statistical models. Topics include generalized linear models, generalized additive models, hierarchical Bayes methods, and resampling methods. Time series models, including moving average, autoregressive, and ARIMA models, estimation and forecasting, confidence intervals. 3 hours lecture; 3 semester hours. Prerequisite: MATH332, MATH324.
View Course Learning Outcomes
- Fit and interpret standard and weighted linear models
- Fit and interpret ANOVA models
- Fit and interpret generalized linear models
- Fit models to time series data
- Perform diagnostic tests on statistical models
MATH437. MULTIVARIATE ANALYSIS. 3.0 Semester Hrs.
Introduction to applied multivariate techniques for data analysis. Topics include principal components, cluster analysis, MANOVA and other methods based on the multivariate Gaussian distribution, discriminant analysis, classification with nearest neighbors. 3 hours lecture; 3 semester hours. Prerequisite: MATH335 or MATH201, MATH332 or MATH342, MATH324.
MATH438. STOCHASTIC MODELS. 3.0 Semester Hrs.
(II) An introduction to stochastic models applicable to problems in engineering, physical science, economics, and operations research. Markov chains in discrete and continuous time, Poisson processes, and topics in queuing, reliability, and renewal theory. Prerequisite: MATH332, MATH334.
MATH439. SURVIVAL ANALYSIS. 3.0 Semester Hrs.
Basic theory and practice of survival analysis. Topics include survival and hazard functions, censoring and truncation, parametric and non-parametric inference, hypothesis testing, the proportional hazards model, model diagnostics. 3 hours lecture; 3 semester hours. Prerequisite: MATH335.
MATH440. PARALLEL SCIENTIFIC COMPUTING. 3.0 Semester Hrs.
Equivalent with CSCI440,
This course is designed to facilitate students' learning of high-performance computing concepts and techniques to efficiently perform large-scale mathematical modelling and data analysis using modern high-performance architectures (e.g. multi-core processors, multiple processors, and/or accelerators). Emphasis will be placed on analysis and implementation of various scientific computing algorithms in high-level languages using their interfaces for parallel or accelerated computing. Use of scripting to manage HPC workflows is included. Prerequisite: MATH307, CSCI200.
MATH454. COMPLEX ANALYSIS. 3.0 Semester Hrs.
The complex plane. Analytic functions, harmonic functions. Mapping by elementary functions. Complex integration, power series, calculus of residues. Conformal mapping. Prerequisite: MATH225 or MATH235 and MATH213 or MATH223. 3 hours lecture, 3 semester hours.
MATH455. PARTIAL DIFFERENTIAL EQUATIONS. 3.0 Semester Hrs.
Linear partial differential equations, with emphasis on the classical second-order equations: wave equation, heat equation, Laplace's equation. Separation of variables, Fourier methods, Sturm-Liouville problems. Prerequisites: MATH225 or MATH235 and MATH213 or MATH223. 3 hours lecture; 3 semester hours.
MATH457. INTEGRAL EQUATIONS. 3.0 Semester Hrs.
This is an introductory course on the theory and applications of integral equations. Abel, Fredholm and Volterra equations. Fredholm theory: small kernels, separable kernels, iteration, connections with linear algebra and Sturm-Liouville problems. Applications to boundary-value problems for Laplace's equation and other partial differential equations. Prerequisites: MATH332 or MATH342, and MATH455. 3 hours lecture; 3 semester hours.
MATH458. ABSTRACT ALGEBRA. 3.0 Semester Hrs.
This course is an introduction to the concepts of contemporary abstract algebra and applications of those concepts in areas such as physics and chemistry. Topics include groups, subgroups, isomorphisms and homomorphisms, rings, integral domains and fields. Prerequisites: MATH300. 3 hours lecture; 3 semester hours.
MATH459. ASYMPTOTICS. 3.0 Semester Hrs.
Equivalent with MATH559,
Asymptotic methods are used to find approximate solutions to problems when exact solutions are unavailable or too complicated to be useful. A broad range of asymptotic methods is developed, covering algebraic problems, integrals and differential equations. Prerequisites: MATH213 and MATH225. 3 hours lecture; 3 semester hours.
View Course Learning Outcomes
- Use asymptotic methods to solve algebraic problems.
- Use asymptotic methods to estimate integrals
- Use asymptotic methods to solve differential equations.
MATH470. MATHEMATICAL MODELING OF SPATIAL PROCESSES IN BIOLOGY. 3.0 Semester Hrs.
(II) This course is an introduction to mathematical modeling of spatial processes in biology. The emphasis is on partial differential equation models from a diverse set of biological topics such as cellular homeostasis, muscle dynamics, neural dynamics, calcium handling, epidemiology, and chemotaxis. We will survey a variety of models and analyze their results in the context of the biology. Mathematically, we will examine the diffusion equation, advection equation, and combinations of the two that include reactions. There will be a significant computational component to the course including bi-weekly computational labs; students will solve the model equations and perform computations using MATLAB. Prerequisite: MATH431, MATH455 or equivalent courses and familiarity with MATLAB.
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- Describe classical spatial-temporal models in mathematical biology including diffusion-reaction, advection-reaction, and advection-diffusion-reaction
- Derive partial differential equations models for spatial-temporal phenomena
- Implement analytical and numerical techniques to solve and analyze spatial-temporal models
- Assimilate current literature, extend it in a final project that advances the field, and communicate results professionally and effectively
MATH472. MATHEMATICAL AND COMPUTATIONAL NEUROSCIENCE. 3.0 Semester Hrs.
This course will focus on mathematical and computational techniques applied to neuroscience. Topics will include nonlinear dynamics, hysteresis, the cable equation, and representative models such as Wilson-Cowan, Hodgkin-Huxley, and FitzHugh-Nagumo. Applications will be motivated by student interests. In addition to building basic skills in applied math, students will gain insight into how mathematical sciences can be used to model and solve problems in neuroscience; develop a variety of strategies (computational, theoretical, etc.) with which to approach novel mathematical situations; and hone skills for communicating mathematical ideas precisely and concisely in an interdisciplinary context. In addition, the strong computational component of this course will help students to develop computer programming skills and apply appropriate technological tools to solve mathematical problems. 3 hours lecture; 3 semester hours. Prerequisite: MATH431.
View Course Learning Outcomes
- Describe the classical models of mathematical neuroscience including Hodgkin-Huxley, Wilson-Cowan, and FitzHugh-Nagumo
- Implement analytical and numerical techniques to analyze models at different spatial and temporal scales;
- Apply concepts from nonlinear dynamics including phase plane analysis, bifurcation theory, and model reduction techniques to analyze models in neuroscience.
- Assimilate current literature, extend it in a final project that advances the field, and communicate results professionally and effectively.
MATH482. STATISTICS PRACTICUM (CAPSTONE). 3.0 Semester Hrs.
This is the capstone course in the Statistics option. Students will apply statistical principles to data analysis through advanced work, leading to a written report and an oral presentation. Choice of project is arranged between the student and the individual faculty member who will serve as advisor. Prerequisite: MATH335, MATH324, MATH436.
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- Gain hands-on data analysis experience on a substantial problem seeing it all the way through.
- Communicate effectively with clients, who might have limited statistical background.
- Develop well-documented and reproducible code.
- Document results in a technical report and potentially co-author a scientific publication.
MATH484. MATHEMATICAL AND COMPUTATIONAL MODELING (CAPSTONE). 3.0 Semester Hrs.
This is the capstone course in the Computational and Applied Mathematics option. Students will apply computational and applied mathematics modeling techniques to solve complex problems in biological, engineering and physical systems. Mathematical methods and algorithms will be studied within both theoretical and computational contexts. The emphasis is on how to formulate, analyze and use nonlinear modeling to solve typical modern problems. Prerequisite: MATH431, MATH307, MATH455.
View Course Learning Outcomes
- Construct, interpret, and critique fundamental models of physical, chemical, and biological systems throughout the fundamental and applied sciences.
- Utilize computational tools, such as MATLAB, to simulate behavior arising from mathematical models
- Describe and interpret, via oral and written means, pertinent information obtained from mathematical analysis and simulation in order to draw scientific conclusions concerning applied model
MATH491. UNDERGRADUATE RESEARCH. 1-3 Semester Hr.
(I) (WI) Individual investigation under the direction of a department faculty member. Written report required for credit. Variable - 1 to 3 semester hours. Repeatable for credit to a maximum of 12 hours.
MATH498. SPECIAL TOPICS. 1-6 Semester Hr.
(I, II) Pilot course or special topics course. Topics chosen from special interests of instructor(s) and student(s). Usually the course is offered only once. Prerequisite: none. Variable credit; 1 to 6 credit hours. Repeatable for credit under different titles.
MATH499. INDEPENDENT STUDY. 0.5-6 Semester Hr.
(I, II) Individual research or special problem projects supervised by a faculty member, also, when a student and instructor agree on a subject matter, content, and credit hours. Prerequisite: ?Independent Study? form must be completed and submitted to the Registrar. Variable credit; 1 to 6 credit hours. Repeatable for credit.