Search Results
Your search for courses · during 25FA, 26WI, 26SP · tagged with STAT Elective · returned 8 results
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CS 314* Data Visualization (*=Junior Seminar) 6 credits
Though the wealth of data surrounding us can be overwhelming, we have evolved incredible tools for finding patterns in large amounts of information: our eyes! Data visualization is concerned with turning information into pictures to better communicate patterns or discover new insights, drawing from computer graphics, human-computer interaction, design, and perceptual psychology. In this junior seminar, we will learn different ways in which data can be expressed visually and which methods work best for which tasks, with a particular focus on technical communication. Using this knowledge, we will critique existing visualizations as well as design and build new ones.
- Spring 2026
- FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning
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Student has completed any of the following course(s): CS 200 or CS 201 with a grade of C- or better or received a Carleton Computer Science 201 or better Requisite Equivalency. Not open to students who have taken CS 314.
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CS 314*.01 Spring 2026
- Faculty:Eric Alexander 🏫 👤
- Size:16
- M, WAnderson Hall 223 12:30pm-1:40pm
- FAnderson Hall 223 1:10pm-2:10pm
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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 intelligence-related 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.
X seats held for CS Match until the day after X priority registration.
- Winter 2026
- FSR, Formal or Statistical Reasoning
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Student has completed any of the following course(s): CS 200 with a grade of C- or better or CS 201 with a grade of C- or better or received a Carleton Computer Science 200 Requisite Equivalency AND CS 202 with a grade of C- or better or received a Carleton Computer Science 202 Requisite Equivalency or MATH 236 with a grade of C- or better or received a Carleton Math 236 Requisite Equivalency. MATH 236 will be accepted in lieu of CS 202.
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CS 320.01 Winter 2026
- Faculty:Anna Meyer 🏫 👤
- Size:28
- M, WLanguage & Dining Center 244 9:50am-11:00am
- FLanguage & Dining Center 244 9:40am-10:40am
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28 seats held for CS Match until the day after Senior priority registration.
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CS 362 Computational Biology 6 credits
Recent advances in high-throughput experimental techniques have revolutionized how biologists measure DNA, RNA and protein. The size and complexity of the resulting datasets have led to a new era where computational methods are essential to answering important biological questions. This course focuses on the process of transforming biological problems into well formed computational questions and the algorithms to solve them. Topics include approaches to sequence comparison and alignment; molecular evolution and phylogenetics; DNA/RNA sequencing and assembly; and specific disease applications including cancer genomics.
- Spring 2026
- FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning
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Student has completed any of the following course(s): CS 200 with a grade of C- or better or CS 201 with a grade of C- or better or received a Carleton Computer Science 200 Requisite Equivalency AND CS 202 with a grade of C- or better or received a Carleton Computer Science 202 Requisite Equivalency or MATH 236 with a grade of C- or better or received a Carleton Math 236 Requisite Equivalency. MATH 236 will be accepted in lieu of CS 202.
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CS 362.01 Spring 2026
- Faculty:Layla Oesper 🏫 👤
- Size:34
- M, WLeighton 305 11:10am-12:20pm
- FLeighton 305 12:00pm-1:00pm
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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 2026
- FSR, Formal or Statistical Reasoning
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Student must have completed any of the following course(s): MATH 134 or MATH 232 AND MATH 120 or MATH 211 with a grade of C- or better or equivalents.
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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 2025, Winter 2026, Spring 2026
- FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning
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Student has completed any of the following course(s): STAT 120 or STAT 230, or STAT 250 with a grade of C- or better.
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STAT 260 Introduction to Sampling Techniques 6 credits
Covers sampling design issues beyond the basic simple random sample: stratification, clustering, domains, and complex designs like two-phase and multistage designs. Inference and estimation techniques for most of these designs will be covered and the idea of sampling weights for a survey will be introduced. We may also cover topics like graphing complex survey data and exploring relationships in complex survey data using regression and chi-square tests.
- Winter 2026
- FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning
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Student has completed any of the following course(s): STAT 120 or STAT 230, or STAT 250 with a grade of C- or better.
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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 zero-inflated Poisson models. Depending on time, additional topics could include survival analysis or generalized additive models.
- Spring 2026
- FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning
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Student has completed any of the following course(s): STAT 230 AND STAT 250 with a grade of C- or better AND has completed or is in the process of completing MATH 134 or MATH 232 with a grade of C- or better or received a Carleton Math 232 Requisite Equivalency.
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STAT 340 Bayesian Statistics 6 credits
The Bayesian approach to statistics provides a powerful framework for incorporating prior knowledge into statistical analyses, updating this knowledge with data, and quantifying uncertainty in results. This course serves as a comprehensive introduction to Bayesian statistical inference and modeling, an alternative to the frequentist approach to statistics covered in previous classes. Topics include: Bayes’ Theorem; prior and posterior distributions; Bayesian regression; hierarchical models; and model adequacy and posterior predictive checks. Computational techniques will also be covered, including Markov Chain Monte Carlo methods, and modern Bayesian modeling packages in R.
- Fall 2025
- FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning
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Student has completed any of the following course(s): STAT 230 and STAT 250 with a grade of C- or better.