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Mathematics & Statistics

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We provide a strong foundation for liberal arts students, preparing them for natural, social sciences, and professional fields.

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Undergraduate programs

Mathematics

Undergrad major Undergrad minor Teacher license Graduate degree

Mathematics is the science and art of pattern and idea. Applied math is a branch of mathematics that concerns itself with mathematical methods used in science, engineering, business and industry. There is no area that does not require some form of mathematical or statistical thought. It is an integral part of the liberal arts education and is the foundation for many areas of study.

Areas of study

Applied Emphasis

Applied math is a branch of mathematics that concerns itself with mathematical methods used in science, engineering, business and industry. 

Undergrad major View a sample plan for Applied Catalogfor Applied

Education

The Mathematics Education Program and associated benchmark assessments lead to endorsement for a Wisconsin teaching license in middle and high school mathematics for grades 4-12 (1400). Students in all education programs must satisfy the School of Education (SOE) core requirements.

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Undergrad dual degree

Receive an undergraduate degree in both Mathematics and Engineering. This degree path involves collaboration with partner institutions. Students who express interest in the dual degree program will be selected for entrance into the UW-Madison, UW-Milwaukee, UW-Platteville, UW-Stout, University of Minnesota Duluth, or Winona State University for a portion of the program.

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Catalogfor Mathematics
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Statistics

Undergrad major Undergrad minor Graduate degree

Statistics is the science of collecting, analyzing, and making inferences from data. There is no area of STEM, social science or business that does not require some form of statistical thought. It is an integral part of the liberal arts education and is the foundation for many areas of study.

Areas of study

Actuarial Science Concentration

Actuarial science is the use of mathematical and statistics tools to aid in decision making, particularly the assessment of risk.

 

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Applied Statistics

Graduate degree

Undergrad + graduate dual degree

This dual degree program enables students to earn both a Bachelor of Science degree with a statistics major and a Master of Science degree in applied statistics in five years.

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Data Science

Undergrad major

Data science is an interdisciplinary field that involves gathering, analyzing, and interpreting large volumes of data to help organizations make data-driven decisions. This process often uses cutting-edge technologies, including artificial intelligence, to uncover valuable insights.

At UW-La Crosse, the Data Science program integrates key fields such as mathematics, statistics, programming, and communication. Students develop a comprehensive skill set that prepares them to tackle complex data problems across industries.

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Communication, Writing, and Critical Reasoning

Undergrad minor
Quick glancefor Communication, Writing, and Critical Reasoning

Mathematics for Teachers

Undergrad minor

Graduate programs

Data Science

Graduate degree Graduate certificate The online MS and Certificate in Data Science teach you to clean, analyze, and interpret unstructured data, using visualization and clear communication.
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Applied Statistics

Graduate degree Statisticians turn numbers into insights, driving decisions in fields like medicine, marketing, space exploration, and resource management.
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Featured courses

  • Mathematical Models in Biology
    MTH 265 | 4 credits
    An introduction to the use of calculus and stochastic based models to the biological sciences. Mathematical tools such as discrete and continuous differential equations, linear algebra, phase portraits, probability theory and descriptive and inferential statistics that are necessary to analyze and interpret biological models will be covered. Biological topics may include single species and interacting population dynamics, modeling infectious diseases, enzyme kinetics, and quantitative genetics. Prerequisite: grade of "C" or better in MTH 175 or MTH 207. Offered Spring.
  • Linear Algebra with Differential Equations
    MTH 308 | 4 credits
    This course will study linear algebra with emphasis on computer programming and applications. Specific topics include systems of linear equations, matrix operations, linear independence, linear transformations, matrix factorization, vector spaces and subspaces in R^n, basis and dimension, determinants, eigenvalues and eigenvectors, diagonalization, systems of first order linear differential equations, dynamical systems, inner products and orthogonality, least squares, and singular value decomposition. Software will be integrated throughout the course to complement mathematical content. Lect. 3, Lab. 1. Prerequisite: grade of "C" or better in MTH 208 or MTH 265 or (MTH 207 and CS 225 or MTH 225). CS 120 or concurrent enrollment highly recommended. Offered Fall, Spring.
  • Real Analysis I
    MTH 407 | 3 credits
    This course covers the basic theory underlying the differential and integral calculus. Convergence of sequences is examined. Theoretical concepts of calculus are examined and particular attention is given to writing proofs. Prerequisite: grade of "C" or better in MTH 225 or CS 225; grade of "C" or better in MTH 309 and MTH 310. Offered Fall.
  • Studies in Applied Mathematics
    MTH 480 | 3 credits
    Advanced studies of applications of mathematics and computation to solve problems and understand processes from a variety of fields (for example, industry, medicine and the physical and life sciences.) Requirements include an application/ modeling project with a written report and class presentation. Prerequisite: grade of "C" or better in MTH 353. Offered Spring - Even Numbered Years.
  • Statistical Computing
    STAT 345 | 3 credits
    This is an introductory course covering fundamentals of modern statistical computing. Topics include core programming concepts such as functions, data structures and debugging. Stochastic simulations and random variable generation are introduced, as well as accessing, filtering, and analyzing data from other resources. The R language is used. Prerequisite: STAT 145 or STAT 245. Offered Spring.
  • Environmental Statistics
    STAT 444 | 3 credits
    This course is an introduction to common statistical methods used for environmental applications. Topics include spatial statistics, time series analysis, environmental monitoring and impact assessment, and integrated population modeling. This course is application driven and includes an introduction to computer programming in R. This course is taught largely at an undergraduate level. Graduate students have additional course requirements/expectations. Prerequisite: one of the following: STAT 305, STAT 345, MTH 265, MTH 435, BIO 419/519, or GEO 405/GEO 505. Offered Spring - Odd Numbered Years.
  • Applied Multivariate Statistics
    STAT 449 | 3 credits
    This course is an introduction to applied multivariate statistical methods covering multivariate analysis of variance, multivariate analysis of covariance, repeated measures design, factor analysis, principle component analysis, cluster analysis, discriminate analysis, and multivariate regression. Course participants are involved with hands-on statistical applications. This course is taught largely at an undergraduate level. Graduate students have additional course requirements/expectations. Prerequisite: grade of "C" or better in STAT 305 or STAT 345; junior standing. Offered Fall - Odd Numbered Years.
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Portrait of Haley Klundt

Hear from an alum

Haley Klundt

My math professors at UWL instilled in me the importance of perseverance, curiosity, and confidence, often through tough love and by pushing me to reach beyond my self-prescribed boundaries.

Portrait of Mary Vaughan

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Mary Vaughan

I have often wondered if I would have earned my Ph.D. if I had attended a different school for my undergraduate studies. As an undergrad, I worked hard, but I lacked confidence. It was my collective teachers and mentors at UWL that helped me seize the opportunities that have led me to where I am today — across the world in Australia!

Portrait of Kacie O'Hearn

Hear from an alum

Kacie O'Hearn

UWL taught me how to be an independent, lifelong learner. I don’t need to be in a classroom to keep learning and evolving on my own terms.