Mathematics, Statistics, and Computer Science

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 164: Data Science 1

Data is the currency of the modern world. At the intersection between statistics and computer science, data science is about gleaning information and making decisions from data. Using data from a variety of contexts and disciplines, students learn to summarize and extract insight from data, create compelling data visualizations, wrangle data, practice literate programming, and explore ethical issues in data science. No prior experience with programming is expected. This course cannot be taken after MSCS 264.

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 and data science 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. Offered on a regular basis. Counts toward computer science and mathematics majors and statistics and data science concentration.
Prerequisite: MSCS 164 or MSCS 264 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 approaches to 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 alternate years during January Term.
Prerequisite: Permission of instructor.

MSCS 394: Academic Internship

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 their 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