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Your search for courses · during 24FA, 25WI, 25SP · tagged with STAT Elective · returned 7 results

CS 314 Data Visualization 6 credits
Understanding the wealth of data that surrounds us can be challenging. Luckily, we have evolved incredible tools for finding patterns in large amounts of information: our eyes! Data visualization is concerned with taking information and turning it into pictures to better communicate patterns or discover new insights. It combines aspects of computer graphics, humancomputer interaction, design, and perceptual psychology. In this course, we will learn the different ways in which data can be expressed visually and which methods work best for which tasks. Using this knowledge, we will critique existing visualizations as well as design and build new ones.
 Winter 2025
 FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning

Student has completed any of the following course(s): CS 200 – Data Structures with Problem Solving or CS 201 – Data Structures with a grade of C or better or equivalent.

CS 320 Machine Learning 6 credits
What does it mean for a machine to learn? Much of modern machine learning focuses on identifying patterns in large datasets and using these patterns to make predictions about the future. Machine learning has impacted a diverse array of applications and fields, from scientific discovery to healthcare to education. In this artificial intelligencerelated course, we’ll both explore a variety of machine learning algorithms in different application areas, taking both theoretical and practical perspectives, and discuss impacts and ethical implications of machine learning more broadly. Topics may vary, but typically focus on regression and classification algorithms, including neural networks.
 Spring 2025
 FSR, Formal or Statistical Reasoning

Student has completed any of the following course(s): CS 200 – Data Structures with Problem Solving or CS 201 – Data Structures AND CS 202 – Mathematics of Computer Science or MATH 236 – Mathematical Structures with a grade of C or better or equivalent. MATH 236 will be accepted in lieu of Computer Science 202.

CS 320.00 Spring 2025
 Faculty:Tom Finzell 🏫 👤
 Size:34
 M, WLanguage & Dining Center 104 11:10am12:20pm
 FLanguage & Dining Center 104 12:00pm1:00pm

MATH 271 Optimization 6 credits
Optimization is all about selecting the "best" thing. Finding the most likely strategy to win a game, the route that gets you there the fastest, or the curve that most closely fits given data are all examples of optimization problems. In this course we study linear optimization (also known as linear programming), the simplex method, and duality from both a theoretical and a computational perspective. Applications will be selected from statistics, economics, computer science, and more. Additional topics in nonlinear and convex optimization will be covered as time permits.
 Spring 2025
 FSR, Formal or Statistical Reasoning

Student must have completed any of the following course(s): MATH 134 – Linear Algebra with Applications or MATH 232 – Linear Algebra AND MATH 120 – Calculus 2 or MATH 211 – Multivariable Calculus with a grade of C or better or equivalents.

STAT 220 Introduction to Data Science 6 credits
This course will cover the computational side of data analysis, including data acquisition, management, and visualization tools. Topics may include: data scraping, data wrangling, data visualization using packages such as ggplots, interactive graphics using tools such as Shiny, an introduction to classification methods, and understanding and visualizing spatial data. We will use the statistics software R in this course.
 Fall 2024, Winter 2025, Spring 2025
 FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning

Student has completed any of the following course(s): STAT 120 – Introduction to Statistics or STAT 230 – Applied Regression Analysis, or STAT 250 – Introduction to Statistical Inference with a grade of C or better.

STAT 270 Statistical Learning 6 credits
Statistical learning (sometimes called statistical machine learning) centers on the discovery of structural patterns and making predictions using complex data sets. This course explores supervised and unsupervised statistical learning methods, and the ethical considerations of their use. Topics may include nonparametric regression, classification, cross validation, linear model selection techniques and regularization, and clustering. Students will implement these concepts using opensource computational tools, such as the R language.
Not open to students who have received credit for CS 320
 Fall 2024
 FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning

Student has completed any of the following course(s): STAT 230 Applied Regression Analysis with a grade of C or better and has NOT taken CS 320 – Machine Learning

STAT 320 Time Series Analysis 6 credits
Models and methods for characterizing dependence in data that are ordered in time. Emphasis on univariate, quantitative data observed over evenly spaced intervals. Topics include perspectives from both the time domain (e.g., autoregressive and moving average models, and their extensions) and the frequency domain (e.g., periodogram smoothing and parametric models for the spectral density). Exposure to matrix algebra may be helpful but is not required.
 Spring 2025
 FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning

Student has completed any of the following course(s): STAT 230 – Applied Regression Analysis and STAT 250 – Introduction to Statistical Inference with a grade of C or better.

STAT 330 Advanced Statistical Modeling 6 credits
Topics include linear mixed effects models for repeated measures, longitudinal or hierarchical data and generalized linear models (of which logistic and Poisson regression are special cases) including zeroinflated Poisson models. Depending on time, additional topics could include survival analysis or generalized additive models.
 Winter 2025
 FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning

Student has completed any of the following course(s): STAT 230 – Applied Regression Analysis and STAT 250 – Introduction to Statistical Inference with a grade of C or better and has completed or is in the process of completing MATH 134 – Linear Algebra with Practical Applications or MATH 232 – Linear Algebra with a grade of C or better or equivalents.