Mathematics, Statistics, and Computer Science

Ellen Haberoth, Regents Math 307
507-786-3113

wp.stolaf.edu/mscs

The Department of Mathematics, Statistics, and Computer Science offers programs in all three disciplines, including majors in mathematics and computer science and a concentration in statistics and data science. For more information on each program, see the separate listings under Computer Science, Mathematics, and Statistics and Data Science.

MSCS 124: A Four-Dimensional Excursion

This elementary introduction to the geometry of four-dimensional Euclidean space begins with an examination of lower-dimensional spaces and their relationships to one another. Generic objects are examined with the goal of understanding how a four-dimensional counterpart might be generated. Students explore what it means to "see" four-dimensional objects and in alternative ways. The term ends with group projects realized as a short research paper, a virtual reality show/object, and a class presentation. Offered periodically during Interim. No prerequisite.

MSCS 150: Statistical and Data Investigations

Students learn basic techniques to analyze, manage, visualize, and model data. Instruction focuses on the analysis of "real," salient datasets in a computer-equipped classroom. In small groups students discuss, analyze, and solve case study-based problems. Class sessions include the Inquiry-Based Learning technique, which engages students in frequent presentations of their solutions to the class. Students use the R statistical software to perform statistical computing and data visualizations. Offered annually.

MSCS 264: Introduction to Data Science

Data is the currency of the modern world, and data science is a field that sits at the intersection between statistics and computer science. At its heart, data science is about gleaning information and making decisions from data; this course provides a solid foundation to the most important data science tools. Students develop a common language for creating visualizations, wrangling with data, programming in a literate manner, producing reproducible research, and communicating results. Offered each semester. Counts toward statistics concentration.

MSCS 341: Algorithms for Decision Making

This course introduces students to the subject of machine learning. The primary focus is the development and application of powerful machine learning algorithms applied to complex, real-world data. Topics covered include linear regression, nearest neighbor models, k-means clustering, shrinkage methods, decision trees and forests, boosting, bagging, support vector machines, and hierarchical clustering. Applications are taken from a wide variety of disciplines, including biology, economics, public policy, public health, and sports. Familiarity with a programming language such as R or Python is highly recommended. Offered on a regular basis. Counts toward computer science and mathematics majors and statistics and data science concentration.
Prerequisite: MATH 220, CSCI 251, or STAT 272 or permission of the instructor.

MSCS 389: Math, Statistics, and Computer Science Research Methods (0.50)

Students focus on writing scientific papers, preparing scientific posters, and giving presentations in the context of a specific, year-long, interdisciplinary research project. In addition, this weekly seminar series builds collaborative research skills such as working in teams, performing reviews of math, statistics, and computer science literature, consulting effectively, and communicating proficiently. Exposure to post-graduate opportunities in math, statistics, and computer science disciplines is also provided. Open to students accepted into the Center for Interdisciplinary Research.

MSCS 390: Mathematics Practicum

Students work in groups on substantial problems posed by, and of current interest to, area businesses and government agencies. The student groups decide on promising approachesto their problem and carry out the necessary investigations with minimal faculty involvement. Each group reports the results of its investigations with a paper and an hour-long presentation to the sponsoring organization. Offered annually during Interim.
Prerequisite: Permission of instructor.

MSCS 396: Directed Undergraduate Research

This course provides a comprehensive research opportunity, including an introduction to relevant background material, technical instruction, identification of a meaningful project, and data collection. The topic is determined by the faculty member in charge of the course and may relate to his/her research interests. Offered based on department decision. May be offered as a 1.00 credit course or .50 credit course.
Prerequisite: determined by individual instructor.

MSCS 398:

Independent Research.

Chair, 2020-2021

Kristina C. Garrett

Professor of Mathematics, Statistics, and Computer Science

enumerative and algebraic combinatorics; partition theory; q-series

Adam H. Berliner

Associate Professor of Mathematics, Statistics, and Computer Science

combinatorial matrix theory; linear algebra; graph theory

Tyler Billingsley

Visiting Instructor in Mathematics, Statistics, and Computer Science

Laura Boehm

Assistant Professor of Mathematics, Statistics, and Computer Science

statistics; spatial data analysis

Richard A. Brown

Professor of Mathematics, Statistics, and Computer Science

computer science; parallel/distributed systems

Sara Clifton

Assistant Professor of Mathematics

Jaime I. Davila

Assistant Professor of Mathematics, Statistics, and Computer Science

Jill Dietz

Professor of Mathematics

algebraic topology; group theory

Olaf A. Hall-Holt

Associate Professor of Mathematics, Statistics, and Computer Science

computational geometry; computer vision/graphics

Bruce Hanson

Professor of Mathematics, Statistics, and Computer Science

real analysis; complex analysis

Paul Humke

Professor of Mathematics, Statistics, and Computer Science

real analysis; dynamical systems

Elizabeth A. Jensen

Assistant Professor of Computer Science

Sharon J. Lane-Getaz

Associate Professor of Mathematics, Statistics, and Computer Science

statistics; statistics education

Ryota Matsuura

Associate Professor of Mathematics, Statistics, and Computer Science

mathematics education; algebraic number theory

Steven McKelvey

Professor of Mathematics, Statistics, and Computer Science

operations research; wildlife modeling

Matthew P. Richey

Professor of Mathematics, Statistics, and Computer Science

applied and computational mathematics

Paul J. Roback

Professor of Mathematics, Statistics, and Computer Science

statistics

Joseph Roith

Visiting Assistant Professor of Mathematics, Statistics, and Computer Science

David P. Walmsley

Assistant Professor of Mathematics

Adam C. Wood

Visiting Instructor in Mathematics, Statistics, and Computer Science

Matthew Wright

Assistant Professor of Mathematics, Statistics, and Computer Science

applied and computational topology

Kathryn Ziegler-Graham (on leave)

Associate Professor of Mathematics, Statistics, and Computer Science

biostatistics