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Mathematics & Statistics

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Undergraduate programs

Mathematics

Undergrad major Undergrad minor

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. 

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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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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

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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Catalogfor Statistics
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Graduate programs

Data Science

Graduate degree Graduate certificate
Quick glancefor Data Science

Applied Statistics

Graduate degree
Quick glancefor Applied Statistics

Featured courses

  • Calculus III: Multivariable Calculus
    MTH 310 | 4 credits
    A continuation of Calculus II with a rigorous introduction to vector and multivariable calculus. Topics include vectors, parametric curves, partial derivatives, directional derivatives, the chain rule, Lagrange multipliers, extrema, double and triple integrals, the Jacobian and change of coordinates, and vector calculus in 2-D and 3-D spaces culminating with Green's Theorem, Stokes' Theorem, and the Divergence Theorem. Prerequisite: grade of "C" or better in MTH 208. Offered Fall, Spring.
  • Foundations of Advanced Mathematics
    MTH 225 | 4 credits
    An introduction to mathematical reasoning. Mathematical logic, including quantification and the predicate calculus is introduced and used to discuss set theory, relations, functions, counting, graphs, and algorithms. Elementary proofs, including proofs by induction are stressed. Prerequisite: grade of "C" or better in MTH 175 or MTH 207. Course not open to those who have credit in CS 225. Offered Fall, Spring.
  • Linear Algebra
    MTH 309 | 4 credits
    This course is an introduction to the fundamental concepts of linear algebra. Topics include systems of linear equations, matrices, vector spaces, subspaces, basis and dimension, linear transformations and their matrix representations, similar matrices and diagonalization, projections and orthogonalization, and applications. In addition to computational proficiency, there is an emphasis on conceptual understanding of definitions and theorems, as well as the comprehension and construction of proofs. Prerequisite: grade of "C" or better in MTH 208; grade of "C" or better in MTH 225 or CS 225. Offered Fall, Spring.
  • Statistical Computing
    STAT 345 | 3 credits
    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 will be used. Prerequisite: STAT 245 and CS 120. Offered Spring.
  • Statistical Methods
    STAT 405 | 3 credits
    A survey of statistical methods from the point of view of how these methods are implemented with a standard statistics software package. Topics include descriptive statistics, graphical methods, tests of location, goodness of fit, simple and multiple regression, design of experiments, ANOVA, multiple comparisons, chi-square tests. Both parametric and nonparametric methods are treated. Computer use is an integral part of the course. This course is taught largely at an undergraduate level. Graduate students will have additional course requirements/expectations. Prerequisite: grade of "C" or better in STAT 145 or STAT 245; junior standing. Offered Fall.
  • Correlation and Regression Analysis
    STAT 445 | 3 credits
    An introduction to simple linear regression, multiple regression, polynomial regression. Inferences, appropriateness of model, model diagnostics/adequacy, difficulties in the application of models are discussed. A computer package will be used. Course participants will be involved with hands-on statistical applications and consulting. This course is taught largely at an undergraduate level. Graduate students will have additional course requirements/expectations. Prerequisite: grade of "C" or better in STAT 345 or STAT 405; junior standing. Offered Fall.
  • Applied Multivariate Statistics
    STAT 449 | 3 credits
    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 will be involved with hands-on statistical applications. This course is taught largely at an undergraduate level. Graduate students will have additional course requirements/expectations. Prerequisite: grade of "C" or better in STAT 345 or STAT 405; junior standing. Offered Fall - Odd Numbered Years.
Portrait of Haley Klundt

Our alumni

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.