Computer Science Major, Bachelor of Arts
Students must earn a grade of C- or better (or P) in each course that applies as a prerequisite for a CSC, DST, or MAT course.
- CSC 165 and 165L – Introduction to Computer Programming (Python)
- CSC 170 and 170L – Introduction to Object-Oriented Programming (Java)
- CSC 321 – Software Design and Development
- CSC 341 – Data Structures
- CSC 240 – Information Security and Assurance
- MIS 260 – Problem Solving for Business
- One data course elective
- DST 234 – Introduction to Data Science (and R)
- MIS 270 – Data Management for Business
- Two project-based elective courses chosen from:
- CSC 311 – Web Applications and Databases
- CSC 395 – Topics class with “project” designation
- CSC 399 – Internship
- CSC 421 – Mobile Computing
- CSC 431 – Introduction to AI Robotics
- DST 314 – Programming for Data Science
- MIS 476 – Information Systems Projects
- One of MAT 202 or MAT 302
- MAT 202 – Discrete Mathematics for Computing (recommended)
- MAT 302 – Discrete Mathematical Structures
- Complete one of the discipline-specific course pairings listed below
Natural Sciences and Mathematics
- Option 1: Computer and Data Science – Complete two of the following:
- Additional CSC course numbered 300 or above
- Additional CSC course numbered 300 or above
- DST 234 – Introduction to Data Science (and R)
- DST 314 – Programming for Data Science
- Option 2: Biology – both BIO 102/102L and BIO 121/121L
- BIO 102/102L – The Biological World
- BIO 121/121L – Human Biology
- Option 3: Physics – both of PHY 107/107L and PHY 108/108L
- PHY 107/107L – College Physics I
- PHY 108/108L – College Physics II
- Option 4: Psychology – both PSY 215 and PSY 354
- PSY 215 – Research Methods and Statistics I
- PSY 354 – Cognitive Psychology
Fine Arts
- Option 5: Graphic Design – both ART 102 and ART 109
- ART 102 – Visual Literacy
- ART 109 – Pixels and Vectors
- Option 6: Music – all of MUS 221, MUS 225, and MUS 306
- MUS 221 – Introduction to Music Technology
- MUS 225 – Electronic Music Production
- MUS 306 – Audio Recording
- Option 7: Theater – THR 228 and one of THR 229 or THR 275
- THR 228 – Introduction to Stagecraft
- Either THR 229 or THR 275
- THR 229 – Theatrical Design
- THR 275 – Lighting and Sound for the Stage
Business
- Option 8: Macroeconomics – both ECO 112 and ECO 312
- ECO 112 – Principles of Macroeconomics
- ECO 312 – Intermediate Macroeconomics
- Option 9: Microeconomics – both ECO 113 and ECO 313
- ECO 113 – Principles of Microeconomics
- ECO 313 – Intermediate Microeconomics
- Option 10: Entrepreneurship – both ENT 254 and ENT 354
- ENT 254 – Entrepreneurship
- ENT 354 – Managing Innovation and Design Thinking
- Option 11: Management Information Systems – both MIS 375 and MIS 376
- MIS 375 – E-Commerce
- MIS 376 – Project Management
- Option 12: Marketing – both MKT 252 and MKT 359
- MKT 252 – Principles of Marketing
- MKT 359 – Digital Marketing
Humanities and Social Sciences
- Option 13: History – One of HIS 101, 102, 121, or 122, as well as one of HIS 200 or 300
- One of HIS 101, HIS 102, HIS 121, or HIS 122
- HIS 101 – The Story of Humanity: From the Neolithic Revolution to the Columbian Exchange
- HIS 102 – Science, Religion, and Enlightenment: The Invention of Modernity, 1350-1815
- HIS 121 – An Empire for Liberty: U.S. History To 1877
- HIS 122 – Gender, Race, Class and Democracy in the Modern U.S.
- One of HIS 200 or HIS 300
- HIS 200 – Why History Matters
- HIS 300 – Doing History in Public
- One of HIS 101, HIS 102, HIS 121, or HIS 122
- Option 14: Law – both NMS 371 and POL170
- NMS 371 – Internet Law
- POL 170 – Law in the United States
- Option 15: Game Design – both NMS 115 and NMS 352
- NMS 115 – Introduction to Game History and Design
- NMS 352 – Unity I: Working With Unity
- Option 16: New Media – NMS 220 and one of NMS 260 or ART 340
- NMS 220 – Foundations of New Media
- One of NMS 260 or ART 340
- NMS 260 – New Media Production
- ART 340 – Digital Imaging
Each course may count only once towards the major.
At most 4 credits of internship may count towards the major.
Students must earn a grade of C- or better (or P) in each course that applies toward the BA major in Computer Science, Applied.
Students completing a major in Computer Science are not eligible for a minor in Computer Science.
Graduation Skills
The Critical Thinking (CT), Quantitative Reasoning (QR) and Writing (W) graduation skills are embedded throughout the offered courses and are met by completing the BA major in Computer Science, Applied. The Speaking (S) graduation skill is met by MAT 201, COM 111, COM 115, or other speaking courses approved by the department.
Transfer students must consult an advisor about potential adjustments to their course requirements to fulfill each of these skills.
Computer Science Major, Bachelor of Science
- CSC 165 and 165L – Introduction to Computer Programming (Python)
- CSC 170 and 170L – Introduction to Object-Oriented Programming (Java)
- CSC 321 – Software Design and Development
- CSC 341 – Data Structures
- CSC 351 – Algorithms
- CSC 371 – Computer Organization
- CSC 391 – Programming Languages
- Complete two project-based electives chosen from:
- CSC 373 – Artificial Intelligence
- CSC 395 – Topics with “project” designation
- CSC 399 – Internship
- CSC 421 – Mobile Computing
- CSC 431 – Introduction to AI Robotics
- CSC 495 – Topics with “project” designation
- MIS 476 – Information Systems Projects
- One data course from:
- DST 234 – Introduction to Data Science (and R)
- DST 314 – Programming for Data Science
- One elective chosen from:
- Additional CSC course numbered 200 or above
- DST 234 – Introduction to Data Science (and R)
- MIS 270 – Data Management for Business
- PHY 361 – Electronics
- Two advanced electives chosen from:
- Additional CSC course numbered 300 or above
- Additional CSC course numbered 300 or above
- DST 314 – Programming for Data Science
- DST 475 – Machine Learning
- MAT 350 – Graph Theory
- MAT 455 – Numerical Mathematics and Computation
- MAT 145 and 145L – Calculus I
- MAT 302 – Discrete Mathematical Structures
- One specialized mathematics elective
- MAT 146 – Calculus II
- MAT 315 – Linear Algebra
Each course may count only once towards the major.
Students may apply up to 4 credit hours of internship towards the major.
Students must earn a grade of C- or better (or P) in each course that applies toward the BS major in Computer Science.
Students completing a major in Computer Science are not eligible for a minor in Computer Science.
Graduation Skills
The Critical Thinking (CT), Quantitative Reasoning (QR), and Writing (W) graduation skills are embedded throughout the offered courses and are met by completing the BS major in Computer Science. The Speaking (S) graduation skill is met by MAT 201, COM 111, COM 115, or other speaking courses approved by the department.
Transfer students must consult an advisor about potential adjustments to their course requirements to fulfill each of these skills.
Computer Science Minor
- CSC 165 and 165L – Introduction to Computer Programming (Python)
- CSC 170 and 170L – Introduction to Object-Oriented Programming (Java)
- CSC 321 – Software Design and Development
- CSC 341 – Data Structures
- One additional CSC elective numbered 300 or above (except for CSC 396, CSC 397, CSC 398, or CSC 399) or DST 314
- One discrete mathematics course
- MAT 202 – Discrete Mathematics for Computing
- MAT 302 – Discrete Mathematical Structures
Each course may count only once towards the minor.
Internship credits do not apply towards the minor.
Students must earn a grade of C- or better (or P) in each course that applies toward the minor in Computer Science.
Students completing a major in Computer Science are not eligible for a minor in Computer Science.
Data Science Major, Bachelor of Science
- Introductory statistics course chosen from:
- DST 164 – Introduction to Statistics (with R)
- MAT 163 – Introductory Statistics (offered infrequently)
- MIS 379 – Quantitative Methods for Business and Economics
- PSY 215 – Research Methods and Statistics I
- SOC 362 – Statistical Analysis
- MAT 145 and MAT 145L – Calculus I
- CSC 165 and CSC 165 L – Introduction to Computer Programming (Python)
- DST 234 – Introduction to Data Science (and R)
- One Introduction to Social Justice course, chosen from:
- AIS 105 – Introduction to American Indian Studies
- AIS 205 – Contemporary American Indian Issues
- ANT 141 – Introduction to Cultural Anthropology
- CCS 100 – Introduction to Cultural Studies
- CRS 101 – Introduction to Critical Race and Ethnicity Studies
- HIS 122 – Gender, Race, Class and Democracy in the Modern U.S.
- HIS 225 – History of the Twin Cities
- HIS 316 – Nature, Cities, and Justice: U.S. Urban Environmental History
- POL 122/URB 122 – Social Justice in Urban America
- SOC 265 – Race, Class, and Gender
- SWK 210 – Environmental Justice and Social Change
- SWK 280 – Diversity and Inequality in Social Practice
- WST 201 – Introduction to Gender, Sexuality, and Women’s Studies
- One Databases (SQL) course chosen from:
- MIS 270 – Data Management for Business
- CSC 311 – Web Applications and Databases
- MAT 315 – Linear Algebra
- DST 334 – Statistical Modeling
- One Machine Learning course chosen from:
- DST 314 – Programming for Data Science
- DST 475 – Machine Learning
- One additional programming course chosen from:
- CSC 170 and CSC 170L – Introduction to Object-Oriented Programming (Java)
- CSC 311 – Web Applications and Databases
- DST 314 – Programming for Data Science
- URB 295 – Topics: Geographic Information Systems (this topic only)
- One communications course chosen from:
- ART 102 – Design
- ART 201 – Introduction to Graphic Design
- COM 352 – Persuasion
- ENL 220 – Critical and Analytical Writing
- ENL 226 – Introduction to Creative Writing
- NMS 220 – Foundations of New Media
- Two advanced electives, completed in one of the following four ways:
- Option 1: Complete both MAT 302 and MAT 350
- MAT 302 – Discrete Mathematical Structures
- MAT 350 – Graph Theory
- Option 2: Complete both CSC 341 and CSC 351
- CSC 341 – Data Structures
- CSC 351 – Algorithms
- Option 3: Complete both MAT 373 and DST 374
- MAT 373 – Probability Theory
- DST 374 – Mathematical Statistics
- Option 4: Complete two courses chosen from:
- ART 315 – Graphic Systems
- CSC 311 – Web Applications and Databases
- DST 314 – Programming for Data Science
- DST 394 – Topics in Statistics
- DST 395 – Topics in Data Science
- DST 399 – Data Science Internship (or 4 credits of DST 396, 397, 398)
- DST 475 – Machine Learning
- MAT 465 – Modeling and Differential Equations in Biological and Natural Sciences
- Option 1: Complete both MAT 302 and MAT 350
- DST 490 – Data Visualization for Social Justice (keystone)
Note: MPG 4 is a prerequisite to the Data Science major. Students in MPG 3 should complete MAT 114 as soon as possible. Students in MPG 2 should complete MAT 106 and MAT 114 as soon as possible. It is recommended that students complete ENL 111 – Effective Writing II (and ENL 101 if needed) and a Speaking skill course early in the major program.
Each course may count only once towards the major.
At most 4 credits of internship may count towards the major.
Students must earn a grade of C- or better (or P) in each course that applies toward the BS major in Data Science.
Students completing a major in Data Science are not eligible for a minor in Data Science or Statistics.
Graduation Skills
The Critical Thinking (CT), Quantitative Reasoning (QR) and Writing (W) graduation skills are embedded throughout the offered courses and are met by completing the BS major in Data Science. The Speaking (S) graduation skill is met by MAT 201, COM 111, COM 115, or other speaking courses approved by the department. Transfer students must consult an advisor about potential adjustments to their course requirements to fulfill each of these skills.
Data Science Minor
- DST 234 – Introduction to Data Science (and R)
- CSC 165 and CSC 165L – Introduction to Computer Programming (Python)
- One advanced data science elective:
- CSC 311 – Web Applications and Databases
- DST 314 – Programming for Data Science
- DST 475 – Machine Learning
- One additional advanced elective:
- ART 201 – Introduction to Graphic Design
- CSC 311 – Web Applications and Databases
- DST 164 – Introduction to Statistics (with R)
- DST 314 – Programming for Data Science
- DST 334 – Statistical Modeling
- DST 395 – Topics in Data Science
- DST 475 – Machine Learning
- MAT 163 – Introductory Statistics
- MIS 270 – Data Management for Business
- MIS 379 – Quantitative Methods for Business and Economics
- NMS 220 – Foundations of New Media
- PSY 215 – Research Methods and Statistics I
- SOC 362 – Statistical Analysis
- URB 295 – Topics: Geographic Information Systems (this topic only)
- DST 490 – Data Visualization for Social Justice
Each course may count only once towards the minor.
Internship credits do not apply towards the minor.
Students must earn a grade of C- or better (or P) in each course that applies toward the minor in Data Science.
Students completing a major in Data Science, a minor in Statistics, or a minor in Business Analytics are not eligible for a minor in Data Science.
Mathematics Major, Bachelor of Arts
- MAT 145 and 145L – Calculus I
- MAT 146 and 146L – Calculus II
- MAT 255 – Multivariable Calculus or MAT 335 – Exploring Geometry
- MAT 302 – Discrete Mathematical Structures
- MAT 315 – Linear Algebra
- One theoretical structures course
- MAT 350 – Graph Theory
- MAT 360 – Dynamical Systems
- MAT 370 – Real Analysis
- MAT 380 – Abstract Algebra
- Mathematics elective chosen from: MAT courses numbered 300 or above.
- One advanced mathematics course numbered 350 or above
- MAT 350 – Graph Theory
- MAT 360 – Dynamical Systems
- MAT 370 – Real Analysis
- MAT 373 – Probability Theory
- MAT 380 – Abstract Algebra
- MAT 395 – Topics
- MAT 399 – Internship (or 4 credits of MAT 396, 397, 398)
- MAT 455 – Numerical Mathematics and Computation
- MAT 465 – Modeling and Differential Equations in Biological and Natural Sciences
- MAT 485 – Visualizing Mathematics with 3D Printing
- MAT 499 – Independent Study
- Advanced elective chosen from: MAT courses numbered 300 or above, DST courses numbered 300 or above, ECO 416 – Mathematical Economics, PHY 327 – Special Functions of Mathematical Physics.
- MAT 491 – Mathematics Colloquium (to be taken during junior and senior years)
Each course may count only once towards the major.
At most 4 credits of internship may count towards the major.
Students must earn a grade of C- or better (or P) in each course that applies toward the BA major in Mathematics.
Students completing a major in Mathematics are not eligible for a minor in Mathematics.
At least two MAT courses numbered 300 or above must be taken at Augsburg.
Graduation Skills
The Critical Thinking (CT), Quantitative Reasoning (QR) and Writing (W) graduation skills are embedded throughout the offered courses and are met by completing the BA major in Mathematics. The Speaking (S) graduation skill is met by MAT 201, COM 111, COM 115, or other speaking courses approved by the department. Transfer students must consult an advisor about potential adjustments to their course requirements to fulfill each of these skills.
Mathematics Major, Bachelor of Science
- MAT 145 and 145L – Calculus I
- MAT 146 and 146L – Calculus II
- One data analysis course
- DST 164 – Introduction to Statistics (with R) (NSM) (recommended)
- DST 234 – Introduction to Data Science (and R) (recommended)
- MAT 163 – Introductory Statistics (offered infrequently)
- Both PHY 395 and PHY 396 – Comprehensive Laboratory I and II
- PSY 215 – Research Methods and Statistics I
- One computational reasoning course
- CSC 165 and 165L – Introduction to Computer Programming (Python) (recommended)
- CHM 280 and 280L – Quantitative Analytical Chemistry
- PHY 327 – Special Functions of Mathematical Physics
- One geometric perspective course
- MAT 255 – Multivariable Calculus
- MAT 335 – Exploring Geometry
- MAT 302 – Discrete Mathematical Structures
- MAT 315 – Linear Algebra
- One theoretical structures course
- MAT 350 – Graph Theory
- MAT 360 – Dynamical Systems
- MAT 370 – Real Analysis
- MAT 380 – Abstract Algebra
- One applied projects course
- DST 475 – Machine Learning
- DST 490 – Data Visualization for Social Justice
- MAT 455 – Numerical Mathematics and Computation
- MAT 465 – Modeling and Differential Equations in Biological and Natural Sciences
- MAT 485 – Visualizing Mathematics with 3D Printing
- One advanced mathematics course numbered 350 or above
- MAT 350 – Graph Theory
- MAT 360 – Dynamical Systems
- MAT 370 – Real Analysis
- MAT 373 – Probability Theory
- MAT 380 – Abstract Algebra
- MAT 395 – Topics
- MAT 399 – Internship (or 4 credits of MAT 396, 397, 398)
- MAT 455 – Numerical Mathematics and Computation
- MAT 465 – Modeling and Differential Equations in Biological and Natural Sciences
- MAT 485 – Visualizing Mathematics with 3D Printing
- MAT 499 – Independent Study
- One advanced elective course
- BIO/CHM 369 and 369L – Biochemistry
- CHM 362 – Physical Chemistry: Macroscopic Theory
- CHM 368 – Physical Chemistry: Microscopic Theory
- CSC 391 – Programming Languages
- An additional DST elective numbered 300 or above
- ECO 416 – Mathematical Economics
- An additional MAT elective numbered 300 or above
- PHY 327 – Special Functions of Mathematical Physics
- PHY 351 – Classical Mechanics
- PHY 365 – Electricity and Magnetism
- One additional supporting course
- ACC 221 – Introduction to Financial Accounting
- BIO/CHM 369 and 369L – Biochemistry
- BIO 444 and 444L – Genomics and Biotechnology
- BIO 481 and 481L – Ecology
- CHM 362 – Physical Chemistry: Macroscopic Theory
- CHM 368 – Physical Chemistry: Microscopic Theory
- CSC 170 and 170L – Introduction to Object-Oriented Programming (Java)
- CSC 341 – Data Structures
- DST 234 – Introduction to Data Science (and R)
- ECO 112 – Principles of Macroeconomics
- ECO 113 – Principles of Microeconomics
- ESE 330 – 5-12 Methods: Mathematics
- MIS 270 – Data Management for Business
- MKT 352 – Marketing Research and Analysis
- PHY 121 and 121L – General Physics I
- PSY 315 – Research Methods and Statistics II
- POL 483 – Political Statistics and Methodology
- SOC 363 – Research Methods
- SWK 401 – Social Work Research and Evaluation
- URB 295 – Topics: Geographic Information Systems (this topic only)
- MAT 491 – Mathematics Colloquium (to be taken during junior and senior years)
Students are encouraged to work with a faculty mentor in Mathematics to select electives within a coherent focus area. Sample focus areas include Actuarial Science (with University of St. Thomas), Business, Computational Mathematics, Data Science, Economics, Finance, Mathematical Biology, Mathematical Chemistry, Physics, Statistics, Teaching Mathematics, and Theoretical Mathematics
Each course may count only once towards the major.
At most 4 credits of internship may count towards the major.
Students must earn a grade of C- or better (or P) in each course that applies toward the BS major in Mathematics.
Students completing a major in Mathematics are not eligible for a minor in Mathematics.
At least two MAT courses numbered 300 or above must be taken at Augsburg.
Graduation Skills
The Critical Thinking (CT), Quantitative Reasoning (QR) and Writing (W) graduation skills are embedded throughout the offered courses and are met by completing the BS major in Mathematics. The Speaking (S) graduation skill is met by MAT 201, COM 111, COM 115, or other speaking courses approved by the department. Transfer students must consult an advisor about potential adjustments to their course requirements to fulfill each of these skills.
Mathematics: Secondary Education, Bachelor of Science
The State of Minnesota has specific licensing requirements for mathematics teachers in K-12 schools. The state requirements are subject to change after publication of this catalog. Students therefore should consult with the Augsburg Education Department to identify current Minnesota teacher licensure requirements.
Grades 5-12 Teaching Licensure in Mathematics: At the time of publication, undergraduate students seeking secondary education licensure (Grades 5-12) and a major in Mathematics should complete the requirements for a BS major in Mathematics with the following choices:
- Data Analysis: DST 164 – Introduction to Statistics (with R) or MAT 163 – Introductory Statistics (offered infrequently)
- Computational Reasoning: CSC 165 and 165L – Introduction to Computer Programming (Python)
- Geometric Perspective: MAT 335 – Exploring Geometry
- Theoretical Structures: MAT 360 – Dynamical Systems or MAT 370 – Real Analysis
- Advanced Mathematics Elective: MAT 380 – Abstract Algebra
- Advanced Elective: MAT 325 – History of Mathematics
- Supporting Course: ESE 330 – 5-12 Methods: Mathematics
Grades 5-8 Teaching Endorsement in Mathematics: At the time of publication, undergraduate students seeking a middle school (Grades 5-8) endorsement in mathematics and a major in Elementary Education should complete the requirements for a BS major in Elementary Education and the following courses:
- MAT 114 – Precalculus (or MPG 4)
- MAT 145 and 145L – Calculus I
- One of: DST 164 – Introduction to Statistics (with R) or MAT 163 – Introductory Statistics (offered infrequently)
- MAT 302 – Discrete Mathematical Structures
- MAT 325 – History of Mathematics
- MAT 335 – Exploring Geometry
- ESE 300 – Reading/Writing in the Content Area**
- ESE 331 – Middle School Methods: Mathematics**
- EDC 482 – Student Teaching: Additional License or Endorsement
Elementary Education majors seeking middle school mathematics licensure are strongly encouraged to consult with a Mathematics faculty mentor before enrolling in the 200 level MAT courses.
Students must earn a grade of C- or better (or P) in each course that applies as a prerequisite for a CSC or MAT course. Students must also earn a grade of C- or better in each course that applies towards education licensure.
For the Grades 5-12 teaching license, a minimum GPA of 2.50 is required for courses required for the mathematics major. For the Grades 5-8 endorsement, a minimum GPA of 2.00 for required MAT courses needed for the endorsement.
Mathematics Minor
Five courses including:
- MAT 145 and 145L – Calculus I
- Additional calculus course chosen from: MAT 146 and 146L – Calculus II or MAT 255 – Multivariable Calculus
- Mathematics elective chosen from MAT course numbered 250 or above
- Advanced mathematics elective chosen from MAT 325 or MAT course numbered 350 or above
- Advanced elective chosen from MAT course numbered 300 or above, DST course numbered 300 or above, ECO 416 – Mathematical Economics, or PHY 327 – Special Functions of Mathematical Physics.
Each course may count only once towards the minor.
Internship credits do not apply towards the minor.
Students must earn a grade of C- or better (or P) in each course that applies as a prerequisite for a CSC, DST, or MAT course. Students must also earn a grade of C- or better (or P) in each course that applies toward the minor in Mathematics.
Students completing a major in Mathematics or Mathematical Economics are not eligible for a minor in Mathematics.
At least one MAT course numbered 250 or above must be taken at Augsburg.
Mathematical Economics Major, Bachelor of Science (joint offering with the Department of Economics)
- ECO 112 – Principles of Macroeconomics
- ECO 113 – Principles of Microeconomics
- ECO 312 – Intermediate Macroeconomics
- ECO 313 – Intermediate Microeconomics
- ECO 490 – Research Methods in Econometrics
- One introduction to statistics, chosen from:
- DST 164 – Introduction to Statistics (with R)
- MAT 163 – Introductory Statistics (offered infrequently)
- MIS 379 – Quantitative Methods for Business and Economics (recommended)
- MAT 145 and 145L – Calculus I
- MAT 146 and 146L – Calculus II
- MAT 255 – Multivariable Calculus
- MAT 315 – Linear Algebra
- Two upper division mathematics/statistics courses, chosen from:
- DST 374 – Mathematical Statistics
- MAT 370 – Real Analysis
- MAT 373 – Probability Theory
- MAT 465 – Modeling and Differential Equations in Biological and Natural Sciences
- Three four-credit upper division Economics courses (ECO 416 recommended)
CSC 165 – Introduction to Computer Programming (Python) is also recommended.
Students must earn a grade of C- or better (or P) in each course that applies as a prerequisite for a CSC, DST, or MAT course. Students must earn a grade of C- or better (or P) in each course that applies toward the major in Mathematical Economics.
Students completing the major in Mathematical Economics are not eligible for a major in Economics, Applied Economics, or the Combined major in Economics and Business Administration or a minor in Mathematics or Economics. Students completing the major in Mathematical Economics may complete a major in Mathematics.
Graduation Skills
Graduation skills in Critical Thinking (CT), Quantitative Reasoning (QR), Speaking (S), and Writing (W) are embedded throughout the offered courses and are met by completing the major. Transfer students must consult an advisor about potential adjustments to their course requirements to fulfill each of these skills.
Statistics Minor
- An introduction to statistics – one of:
- DST 164 – Introduction to Statistics (with R)
- MAT 163 – Introduction to Statistics
- MIS 379 – Quantitative Methods for Business and Economics
- PSY 215 – Research Methods and Statistics I
- SOC 362 – Statistical Analysis
- DST 234 – Introduction to Data Science (and R)
- One advanced statistics course:
- DST 334 – Statistical Modeling
- DST 374 – Mathematical Statistics
- One applied statistics course:
- BIO 444 and 444L – Genomics and Biotechnology
- BIO 481 and 481L – Ecology
- CHM 280 and 280L – Quantitative Analytical Chemistry
- DST 334 – Statistical Modeling
- DST 499 – Independent Study, with MSCS approval
- ECO 490 – Research Methods in Econometrics
- MKT 352 – Marketing Research and Analysis
- POL 483 – Political Statistics and Methodology
- PSY 315 – Research Methods and Statistics II
- SOC 363 – Research Methods
- SWK 401 – Social Work Research and Evaluation
- URB 295 – Topics: Geographic Information Systems (this topic only)
- One additional elective chosen from:
- CSC 165 and 165L – Introduction to Computer Programming (Python)
- DST 334 – Statistical Modeling
- DST 374 – Mathematical Statistics
- DST 394 – Topics in Statistics
- DST 490 – Data Visualization for Social Justice
- MAT 373 – Probability Theory
- PSY 491 – Advanced Research Seminar
- PSY 495 – Clinical Research and Lab
Either the applied statistics course or the additional elective must be numbered 300 or above. Only one of CHM280, URB295, and CSC165 may be applied toward the minor.
DST 499 may count towards the minor if it contains a significant, independent statistical research project, typically in an area where no applied statistics elective is available. Requires prior approval from the MSCS Department Chair. May be mentored by a faculty member with statistical expertise outside of MSCS.
Each course may count only once towards the minor.
Internship credits do not apply towards the minor.
Students completing a major or minor in Data Science are not eligible for a minor in Statistics.
Students interested in graduate work in biostatistics or applied statistics are encouraged to complete CSC 165, DST 164, DST 234, DST 334, DST 374, MAT 145, MAT 146, MAT 255, MAT 315, and MAT 373.
Students must earn a grade of C- or better (or P) in each course that applies as a prerequisite for a CSC or MAT course. Students must also earn a grade of C- or better (or P) in each course that applies toward the minor in Statistics.
Departmental Honors in Mathematics, Statistics, and Computer Science
The faculty in the Department of Mathematics, Statistics, and Computer Science awards departmental honors to a few graduating seniors each year. This honor may be given in one (or more) of the disciplines: Mathematics, Statistics, Data Science, or Computer Science. Departmental honors recognizes:
- Depth of study in the discipline (minimum of the BS Major in Mathematics; BS degree in Mathematics with focus area in Statistics and the Statistics minor; BS Major in Data Science or BS Major in Computer Science, respectively);
- Excellent performance in courses: Minimum 3.00 overall GPA and 3.50 disciplinary GPA;
- Independent investigation or application of the discipline, including public presentation; and
- Involvement in the life of the discipline.
Students who might be eligible for departmental honors should discuss the process with their MSCS faculty mentor during junior year. Detailed requirements and information on the application process are available from the department.
National Honor Society
Membership in the Augsburg chapter of the national Pi Mu Epsilon honor society is by invitation. To be considered, students must have a declared Mathematics major, junior or senior status, and a GPA of 3.00 in their major and overall. Detailed requirements are available from the department.
Prerequisites
A course must be completed with a grade of C- or higher to count as a prerequisite for a Mathematics, Data Science, or Computer Science course.
Math Placement Group (MPG)
Before enrolling in any Mathematics course and many other courses that have Math Placement prerequisites, students must have the required Math Placement. All students are required to have their Math Placement Group (MPG) determined. MPG measures students’ current skill in and understanding of Basic Math (MPG 2), Algebra (MPG 3), Precalculus (MPG 4), and Calculus I (MPG 5).
Students who have taken the ACT test within the last five years are assigned an initial MPG based on their mathematics subscore, illustrated below. Students who have recently completed a mathematics course at another college or university are assigned an initial MPG by the Registrar’s Office as part of Transfer Credit Evaluation, if the course covers Basic Math, Algebra, Precalculus, or Calculus. Students who receive a grade of 4 or 5 on the Advanced Placement Exam in Calculus (AB or BC) are assigned MPG 5. Students are allowed to take the Math Placement Exam to determine if they should be placed into a higher MPG than their initial placement. In particular, students whose initial placement is MPG 1 or MPG 2 are expected to take the Math Placement Exam.
All other students must take the Augsburg Math Placement Exam, which is administered by Academic Advising. The exam is given during Summer Orientation and Registration (SOAR) sessions for first-year students, and before new student registration appointments for transfer and AU students. Other times can be scheduled on an individual basis. Students are also permitted to retake the Math Placement Exam once during their first semester of enrollment at Augsburg University.
Practice questions and other information are available from Academic Advising. Students in MPG 1 take MAT 090 to advance to MPG 2. Students in MPG 2 take MAT 105 or MAT 106 to advance to MPG 3. Students in MPG 3 may take MAT 114 to advance to MPG 4. No other MAT course changes a student’s MPG.
| ACT Math Subscore | Initial Math Placement | Required Math Placement Exam |
|---|---|---|
| 18 and below | MPG 1 | Basic Math; Algebra |
| 19-21 | MPG 2 | Algebra |
| 22-23 | MPG 2 | Algebra |
| 24-25 | MPG 3 | No exam needed |
| 22-25 and successful high school precalculus, trigonometry, or calculus | MPG 3 | Precalculus (if seek MPG 4) |
| 26+ and successful high school precalculus, trigonometry, or calculus | MPG 4 | No exam needed |
For a complete list of courses and descriptions, see the Course Description Search.