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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

Earn a degree in mathematics. UW-La Crosse offers a small class sizes, clubs, research opportunities, strong faculty mentorship and more in the field of math.

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. 

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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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Mathematics & Engineering

Receive an degree from UWL and from UW-Madison, UW-Milwaukee, UW-Platteville, UW-Stout, University of Minnesota Duluth, or Winona State University.

Mathematics Undergrad major
Mathematics Undergrad major
Catalogfor Bachelor of Science in Mathematics & Bachelor of Science in Engineering Dual Degree Program Learn more about Mathematics
Catalogfor Mathematics
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Statistics

Undergrad major Undergrad minor Graduate degree

Learn to collect, analyze and interpret data with a degree in statistics from UW-La Crosse.

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

Statistics & Applied Statistics

The dual degree program allows students to earn a Bachelor’s in Statistics and a Master’s in Applied Statistics.

Statistics Undergrad major
Applied Statistics Graduate degree
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Data Science

Undergrad major

Turn raw data into actionable insights with a degree in Data Science.

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Communication, Writing, and Critical Reasoning

Undergrad minor
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Mathematics for Teachers

Undergrad minor
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Related programs from other departments

Communication Studies
Computational Science

Graduate programs

Data Science

Graduate degree Graduate certificate

UW-La Crosse's online Master of Science in Data Science and Graduate Certificate in Data Science are designed to help you advance in today’s data-driven world.

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Applied Statistics

Graduate degree

As our world becomes increasingly data-driven, the need for professionals who can analyze and interpret that data continues to grow.

Quick glancefor Applied Statistics

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, MTH 308 or MTH 309, and MTH 310. Offered Every Third Semester.
  • 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.
  • Foundations of Data Science
    DSC 210 | 3 credits
    This course is an introduction to the data science workflow covering project formulation, data pre-processing, algorithm application, and result interpretation. Students learn to select and apply appropriate data science techniques, differentiate between supervised and unsupervised problems, and process incomplete datasets for analysis. The course emphasizes comparing model performance and communicating methods and results effectively to both technical and non-technical audiences. Prerequisite: STAT 145 or STAT 245; MTH 160 or MTH 175 or MTH 207. Offered Annually.
  • Supervised Learning
    DSC 420 | 3 credits
    This course is an introduction to machine and statistical learning techniques for making predictions using large and complex data. Supervised learning methods are discussed such as linear and logistic regression, linear discriminant analysis, linear model selection and regularization, decision trees, support vector machines, and artificial neural networks. The uncertainty of the predictions are analyzed using cross-validation and bootstrapping. Prerequisite: grade of "C" or better in CS 120, MTH 308, and STAT 305. Offered Annually.
  • Unsupervised Learning
    DSC 430 | 3 credits
    This course provides an in-depth introduction to unsupervised learning techniques for analyzing and interpreting unlabeled data. Students explore key concepts such as clustering, dimensionality reduction, and anomaly detection, using both traditional and modern approaches. The curriculum emphasizes practical applications across various domains such as market basket analysis, customer segmentation, music genre classification, and fraud detection. Optional topics in graph-based learning and manifold learning allow further exploration of advanced methods used in social network analysis and high-dimensional data visualization. Prerequisite: CS 120; MTH 308; STAT 305. Offered Spring.
  • 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 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

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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.

Portrait of Kacie O'Hearn, '25, MS Applied Statistics

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Kacie O'Hearn, '25, MS Applied Statistics

"I’ve grown so much through this graduate program, and I’ve learned a lot. But what stands out most is the incredible people I’ve met along the way. Each interaction has taught me something valuable."

Portrait of Haley Klundt

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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.